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Clasificaciones y Análisis de Clasificaciones y Análisis de Mezclas EspectralesMezclas Espectrales
Clasificaciones y Análisis de Mezclas EspectralesClasificaciones y Análisis de Mezclas Espectrales
Ejercicios para hoyEjercicios para hoy
1.1. Correcciones atmosféricasCorrecciones atmosféricas
2.2. Análisis de Componentes Principales (PCA) y la Análisis de Componentes Principales (PCA) y la Transformación de la Fracción de Ruido Mínimo Transformación de la Fracción de Ruido Mínimo (MNF Transform)(MNF Transform)
3.3. Clasificación no supervisadaClasificación no supervisada3.3. Clasificación no supervisadaClasificación no supervisada
4.4. Conversión de raster a vectorConversión de raster a vector
5.5. Colección de muestrasColección de muestras
6.6. Clasificación supervisadaClasificación supervisada
7.7. Despliegue en N dimensionesDespliegue en N dimensiones
8.8. Análisis de mezclas espectrales (SMA)Análisis de mezclas espectrales (SMA)
9.9. Filtración de los resultadosFiltración de los resultados
Cobertura de la tierra Cobertura de la tierra -- 2008*2008*
¿Porque necesitamos hacer ¿Porque necesitamos hacer
clasificaciones?clasificaciones?
*Para validar
clasificaciones?clasificaciones?
El El problemaproblema
DespuésDespués de casi 40 de casi 40
años de percepción años de percepción
remota ambiental desde el remota ambiental desde el
lanzamiento de Landsatlanzamiento de Landsat--1 1
en el 23 de julio de 1972, en el 23 de julio de 1972, en el 23 de julio de 1972, en el 23 de julio de 1972,
todavía existe el problema todavía existe el problema
de identificar cobertura de de identificar cobertura de
la tierra (delineación e la tierra (delineación e
cuantificación) fácilmente cuantificación) fácilmente
y con exactitudy con exactitud
Belize City
False color LandSat TM mosaic courtesy of NASA / USGS
Source: TBDSource: TBD
Simulated true color LandSat TM image courtesy of NASA / USGS; image captured 28 March 2000
Simulated false color LandSat TM image courtesy of NASA / USGS; image captured 28 March 2000
Aerial photo of interface between savanna and forest near Belize City, April 5, 2009Aerial photo of interface between savanna and forest near Belize City, April 5, 2009
No.Date -
Ground Condition
Date - Publication
Original Source
Published as Imagery Used Resolution Geographic coverage, original
Number of Classes
1
c. 1990, c.
2000
2005 Earth
Satellite
Corporation
GeoCover LC
1990
Landsat TM,
Landsat ETM+30m global 11
2 1992-93 USGS GLCC AVHRR 1km global 96
3
1992-93 1998
PROARCA
/ CAPAS
"Central
American
Vegetation/L
and Cover
Classification
and
Conservation
Status"
AVHRR 1km regional 25
1991-99 2002 "Central
America
4 World Bank
America
Ecosystems
Mapping
Project"
Landsat TM 30m regional 196
52000 2002
JRC GLC 2000SPOT
Vegetation1km global 23
6
2000, 2001,
2002, 2003,
2004, 2005
2008UMD /
NASAMOD44B MODIS 500m global N / A
72000, 2003,
2004, 2005
2006 U. Ark. /
SERVIR
SERVIR
MesoClassMODIS 500m regional 6
8 2005 2005 USGS MODIS 500m regional 9
9
2004-06 2008 ESA /
MEDIAS
France
GlobCover
projectMERIS 300m global 24*
102000, 2005,
2007, 2008
2008 ESA /
GeoVille
DIVERSITY
projectMERIS* 300m regional 12
Mosaico temporal de imágenes de
2009, con menos que 2% de
nubosidad
Source: p. 2Source: p. 2--25, GOFC25, GOFC--GOLD REDD Sourcebook, Nov. 2009 editionGOLD REDD Sourcebook, Nov. 2009 edition
Source: Goodenough et al (2002): “Report on the Evaluation and Validation of EOSource: Goodenough et al (2002): “Report on the Evaluation and Validation of EO--11
for Sustainable Development (EVEOSD) Project”for Sustainable Development (EVEOSD) Project”
Radiometric calibrationRadiometric calibration
Mosaic, as necessaryMosaic, as necessary
Image Processing: CorrectionsImage Processing: Corrections
Preferably in ENVIPreferably in ENVI
Atmospheric correctionAtmospheric correction Dark object subtractionDark object subtraction
IntraIntra--scene calibrationscene calibration Histogram matchingHistogram matching
In ERDASIn ERDAS
In ERDASIn ERDAS
In ERDASIn ERDAS
El sistema ideal de percepción remota
(1) Fuente
uniforme de
energía
(4) Súper
sensor
(2) Atmosfera
sin interferencia
(3) Interacciones
únicas con el
superficie de la tierra
Fuente: M. Vasquez (2006)
Interacciones de Energía con la Atmosfera
Scattering
Refracción
Absorción
Fuente: B. Howell (2006)
Energía en el Objeto
Radiacion Incidente puede ser…• Reflejada• Transmitida• Absuelto (y reEmitida)
I = R + T + A
Fuente: B. Howell (2006)
Usos de Percepción Remota
• Actualizar o reemplazar mapas existentes
• Determinar áreas de categorías conocidas
• Hacer inventarios de tipos de cobertura
• Documentar cambios entre periodos
• Medir condiciones en una área• Medir condiciones en una área
• Medidas cuantitativa de propiedades
Fuente: B. Howell (2006)
Centroamérica:Centroamérica:50 imágenes cubren50 imágenes cubren
50 millón de ha,50 millón de ha,
=25 GB de datos para procesar=25 GB de datos para procesar
Entonces, desde L4 en 1984 Entonces, desde L4 en 1984
hasta al presente, hay ~769 hasta al presente, hay ~769
escenas disponible para escenas disponible para
cualquier área (23 imágenes cualquier área (23 imágenes
por año por L5 y L7)por año por L5 y L7)
Senderos de LandSat para CentroaméricaSenderos de LandSat para Centroamérica
Los senderosLos senderos
fijos en elfijos en el
sistemas de sistemas de
referencia de referencia de
LandsatLandsat
Resultados de una búsqueda en Glovis Resultados de una búsqueda en Glovis para El Salvador para El Salvador –– 262 imágenes con 262 imágenes con menos de 30% de nubosidad, entre enero menos de 30% de nubosidad, entre enero de 2007 y febrero de 2009de 2007 y febrero de 2009
Landsat TMLandsat TMFalse color mosaicFalse color mosaic2828--0303--20002000
Land cover mapLand cover map20042004
Landsat TMLandsat TMFalse color mosaicFalse color mosaic2828--0303--20002000
Land cover mapLand cover map20042004(Source: BTFS)(Source: BTFS)
Landsat TMLandsat TMFalse color mosaicFalse color mosaic2828--0303--20002000
Land cover mapLand cover map20042004(Source: BTFS)(Source: BTFS)
1.1. Green Green isis forestforest
2.2. To be able to extract To be able to extract
the green is to be the green is to be
Identifying Forest Cover w/ Satellite ImageryIdentifying Forest Cover w/ Satellite Imagery
able to extract the able to extract the
forest coverforest cover
Landsat TMLandsat TMFalse color mosaicFalse color mosaic2828--0303--20002000
Forest cover mapForest cover map20042004(Source: BTFS)(Source: BTFS)
••El Sistema de El Sistema de
Sistemas de la Sistemas de la
Observación de Observación de
la Tierra la Tierra
(GEOSS)(GEOSS)
El Sistema de Visualización y Monitoreo(SERVIR)
TerraTerra
Aqua
LandSat MODISSRTM AMSR-EIKONOS ASTER
Mesoamerica’s Earth Observation& Forecasting Platform
Fires
Red Tides
Land Cover / Use Change
Data ingest fromEOS and Central
EDOSEDOS
Central American
Users
••La Carta La Carta
Internacional Internacional
sobre el Espacio sobre el Espacio
y Grandes y Grandes
CatástrofesCatástrofes
••El Sistema de El Sistema de
Información Información
Ambiental Ambiental
Mesoamericano Mesoamericano
(SIAM)(SIAM)
Test-bed atNASA MSFC
EnvironmentalMonitoring & Decision
Support Products
Web Interfacewww.servir.net
Operational Nodeat CATHALAC Panama
AgricultureBiodiversityClimateEcosystemsEnergyDisastersHealthWaterWeather
ThematicAreas
Central AmericanGovernment agenciesNGOs, researchersEducators, etc.
Impacts
Emergency ResponsePolicy ChangesCorridor PreservationSpecies PreservationSustained DevelopmentImproved livelihoods
SERVIR tiene herramientas muy relevantes al tema de monitoreo de REDDSERVIR tiene herramientas muy relevantes al tema de monitoreo de REDD
SERVIR Disaster Response (2004SERVIR Disaster Response (2004--))1. Red tide event - El Salvador (June 2004)
2. Flooding – Panama City, Panama (Sept. 2004)
3. Flooding - Rio Sixaola, Costa Rica / Panama (Jan 2005)
4. Hurricane Stan – Guatemala, Mexico, El Salvador (Oct .2005)
5. Flooding – Colon Province, Panama (Nov. 2006)
6. Fire - Mountain Pine Ridge Forest Reserve, Belize (May 2007)
7. Hurricane Dean – Mexico / Belize (Aug. 2007)
8. Hurricane Felix – Nicaragua / Honduras (Sept. 2007)
9. Tropical Storm Noel – Dominican Republic (Oct. 2007)
10. Tropical Storm Olga – Dominican Republic (Dec. 2007)
11. Turrialba Volcano – Costa Rica (April 2008)11. Turrialba Volcano – Costa Rica (April 2008)
12. Tropical Storm Arthur – Belize (June 2008)
13. Hurricane Gustav – Haiti / Dominican Rep. (August 2008)
14. Hurricane Hanna – Haiti (Sept. 2008)
15. Hurricane Ike – Haiti (Sept. 2008)
16. Landslide – Huahua Michoacán, Mexico (Oct. 2008)
17. Tropical Depression 16 – Belize / Guatemala / Honduras (Oct. 2008)
18. Flooding – Costa Rica / Panama (Nov. 2008)
19. Landslide – Alta Verapaz, Guatemala (Jan. 2009)
20. Earthquake – San Jose metropolitan area, Costa Rica (Jan. 2009)
21. Fire – Volcan Santo Tomas, Quetzaltenango, Guatemala (Feb. 2009)
22. Flooding – Lago Enriquillo, Dominican Republic (Feb. 2009)
Extracción de Objetos
• Clasificación
• Índices / ratios
• Umbrales de bandas (“band thresholding”)
• Supervisada
• No supervisada
• NDVI
• NBR / NDBR
(“band thresholding”)
• Análisis de mezclas espectrales
• Varios – Sombra, Suelo, Vegetación; Sombra, Suelo, Vegetación verde, vegetación no fotosintética
Pasos básicos en clasificación de imágenes:
1) Estudio y organización de los datos
2) Aplicación de un algoritmo de clasificación
3) Validación3) Validación
Estudio y organización de los datos
Estudio
¿Que esta en la escena / imagen?
¿ Cuales bandas están disponible?
¿ Cuales preguntas necesitan respuestas?
¿ Se puede responder a las preguntas con la imagen?
¿ Hay suficiente información para distinguir que esta en la escena?¿ Hay suficiente información para distinguir que esta en la escena?
Organización de los datos
¿ Cuantas clusters se puede re-organizar en espacio n?
¿ Como son los limites de los clusters?
¿ Los clusters corresponde a los clases deseados?
Clasificación
Clasificación es el proceso de asignar los pixeles a
clases homogéneos basado en análisis de las
estadísticas de valores de reflectancia en uno o mas
bandas.
Clasificación es el proceso de derivar information clases
informativos / utiles de clases espectrales.
Se nombran los procesos de clasificación como Se nombran los procesos de clasificación como
supervisada o no supervisada basado en la metodología
para “entrenar” el classifier (clasificador?).
Con datos de ‘entrenamiento,’ los dos sistemas utilicen
la misma forma de operación (división basada en las
estadísticas).
Fuente: B. Howell (2006)
Clasificación
Pros:
• Puede apoyar en la descripción de usos y cobertura de
la tierra
• Puede simplificar el proceso de detección de cambios
• Con procesamiento posterior, puede generar polígonos
con atributos para uso en un SIG
The Attainment of the Sanc GraelDante Gabriel Rosetti
Contra:
• Clasificación con ella misma, no va extraer información
útil de la data
• No hay clasificadores universales
• NO es perfecto
• Necesita procesamiento posterior para llegar a una foto
bonita
Fuente: B. Howell (2006)
Histogramas deHistogramas decada bandacada banda
Fuente: B. Howell (2006)
Respuestas espectrales
Fuente: B. Howell (2006)
Histogramas deHistogramas decada bandacada banda
ASTER: ASTER: AAdvanced dvanced SSpaceborne paceborne TThermal hermal EEmission & mission & RReflection Radiometereflection Radiometer
Terra
�� Uno de los 5 sensores en el satélite Terra (lanzando en Uno de los 5 sensores en el satélite Terra (lanzando en Dic. de 1999); debe estar encendidoDic. de 1999); debe estar encendido
�� Similar que el sensor ‘Thematic Mapper’ de LandSatSimilar que el sensor ‘Thematic Mapper’ de LandSat
�� Tamaño de una escena es 1/9 de una escena de LandSatTamaño de una escena es 1/9 de una escena de LandSat
�� 14 bandas (3 de 15m / 6 de 30m / 5 de 90m) midiendo 14 bandas (3 de 15m / 6 de 30m / 5 de 90m) midiendo luz en longitudes de onda visible a infrarrojo termalluz en longitudes de onda visible a infrarrojo termal
�� One of 5 sensors on the satellite Terra One of 5 sensors on the satellite Terra (launched in Dec. 1999); ‘on(launched in Dec. 1999); ‘on--call’call’
�� Similar to the Thematic Mapper sensor Similar to the Thematic Mapper sensor on the LandSat satelliteson the LandSat satellites
�� Swath about 1/9 the size of LandSat Swath about 1/9 the size of LandSat swathswath
�� 14 bands (3 15m / 6 30m / 5 90m) 14 bands (3 15m / 6 30m / 5 90m) measuring light from the visible to measuring light from the visible to infrared thermal wavelengthsinfrared thermal wavelengths
Respuestas espectrales
Fuente: B. Howell (2006)
Histogramas deHistogramas decada bandacada banda
Firmas espectrales de materiales comunes
Absorción de
clorofilaDispersión en
las celdas
Absorción
de agua
Firmas espectrales
Fuente: B. Howell (2006)
Clasificación No Clasificación No SupervisadaSupervisadaSupervisadaSupervisada
Belize City
False color LandSat TM mosaic courtesy of NASA / USGS
¯14-Nov-1980
Belize City pop’n ~39,771
Ladyville area
Belize’s
International
Airport
0 3 61.5 km
False color LandSat MSS image courtesy of NASA / USGSPopulation data from Belize CSO
¯27-Dec-1989
Savanna clearing for
shrimp farm development
Diminishing mangrove forests
Coastal development
(Buttonwood Bay &
Bella Vista)
0 3 61.5 km
False color LandSat TM image courtesy of NASA / USGSPopulation data from Belize CSO
¯28-Mar-2000
Expansive shrimp ponds
Settlement on former mangrove
forest (Vista del Mar)
New beachfront properties
Cleared mangrove
Expansion of
north-side Belize
City (Belama)
0 3 61.5 km
Coastal development
Expansion of
south-side
Belize City
City (Belama)
False color LandSat TM image courtesy of NASA / USGSPopulation data from Belize CSO
¯12-Feb-2004
Nova Shrimp Farm
at size of Belize City
Clearing of 100s of acres of
mangrove for Port developmentLand reclamation at
the Marine Parade
0 3 61.5 km
False color LandSat ETM image courtesy of NASA / USGSPopulation data from Belize CSO
¯31-March-2007Nova Shrimp Farm
ceases operations
Further wetland clearing at
Belama Phase IV
0 3 61.5 km
False color ASTER image courtesy of NASA / JAXAPopulation data produced by extrapolating Belize CSO data
19801980 19891989 19981998
La Ciudad de Belice: 1980 hasta 2006La Ciudad de Belice: 1980 hasta 2006
20002000 20022002 20062006
La Ciudad de Belice: 1980 hasta 2006La Ciudad de Belice: 1980 hasta 2006
27-Dec-1989
False color LandSat TM image courtesy of NASA / USGSPopulation data interpolated from Belize CSO data
Belize City pop’n
~42,518
27-Dec-1989
False color LandSat TM image courtesy of NASA / USGSPopulation data interpolated from Belize CSO data
Belize City pop’n
~42,518
Approx. area:
2,089 acres (845 ha.)
2-Feb-2006
False color ASTER image courtesy of NASA / JAXAPopulation data extrapolated from Belize CSO data
Belize City pop’n
~64,700
2-Feb-2006
False color ASTER image courtesy of NASA / JAXAPopulation data extrapolated from Belize CSO data
Belize City pop’n
~64,700
Approx. area:
3,382 acres (1,369 ha.)
Antes: Belize City en 1980
• ~39,771 habitantes
• 1,706 acres (6.9 km2)
• Densidad de 5,756 personas / km2
• Densidad nacional de 6 personas / km2
Reciente: Belize City en 2007
• ~66,422 habitantes
• 3,449 acres (14 km2)
• Densidad de 4,758 personas / km2
• Densidad nacional de 13 personas / km2• Densidad nacional de 13 personas / km2
• El área ha doblado entre 1980 y 2007
• El crecimiento anual era ~106 acres / 43 ha.
• La mayoría de la expansión de 705 ha fue
deforestación de manglares y destrucción de
humedales
False color ASTER image courtesy of NASA / JAXA
2,500
3,000
3,500
4,000
Are
a (a
cres
)
40,000
50,000
60,000
70,000
Pop
ulat
ion
Tendencias del crecimiento de la población y Tendencias del crecimiento de la población y expansión urbana en la Ciudad de Beliceexpansión urbana en la Ciudad de Belice
0
500
1,000
1,500
2,000
31-Mar-072-Feb-0619-Sep-0228-Mar-0015-Sep-9827-Dec-8914-Nov-80
Date
Are
a (a
cres
)
0
10,000
20,000
30,000Pop
ulat
ion
Area Population
80
100
120
140
160
180
Tendencias de expansión urbana (cambios anuales)Tendencias de expansión urbana (cambios anuales)
143 acres cortadas
448 acres cortadas
Annual
expansi
on r
ate
(acr
es /
yr)
0
20
40
60
1981 1990 1999 2000 2003 2006
383 acres cortadas
654 acres cortadas
48 acres cortadas / reclamadas
67 acres cortadas / reclamadas
Annual
expansi
on r
ate
(acr
es /
yr)
Expansión de 705 ha entre nov Expansión de 705 ha entre nov
de 1980 y marzo de 2007de 1980 y marzo de 2007
Rates of Urban Expansion & Population Density
Date Area Pop’n Pop’nDensity(people/ km
2)
ChangeFrom
previousperiod(acres)
Avg. changeper year from
Previousperiod(acres)
Periodof
Change
Major Drivers ofLand Cover Conversionin PeriodAcres Ha. Km
2
Mar-07 3,449 1,396 13.96 66,422 4,758 67 57.3
2006-
2007
Belama Phase
IV
Feb-06 3,382 1,369 13.69 64,128 4,684 48 13.7
2002-
2006
Land
reclamation
Sep-02 3,334 1,349 13.49 56,700 4,203 448 179.2
2000-
2002
Development of
Port
Mar-00 2,886 1,168 11.68 49,050 4,199 143 95.3
1998-
2000
General
expansion
Sep-98 2,743 1,110 11.10 47,947 4,320 654 72.7
1989-
1998
Belama Phases
I-III
Dec-89 2,089 845 8.45 42,518 5,032 383 42.6
1980-
1989
Buttonwood Bay,
general expansion
Nov-80 1,706 691 6.91 39,771 5,756 N / A N / A N / A N / A
Aerial photo by Emil A. Cherrington
Unsupervised Classification
Basic Iterative Clustering Algorithm (K-Means)
• Place K points into the feature space containing the samples to be clustered. These points represent initial group centroids.
• Assign each sample to the group that has the closest centroid.
• When all samples have been assigned, recalculate the positions of the K centroids.
• Repeat Steps 2 and 3 until the centroids no longer move.
Fuente: B. Howell (2006)
Unsupervised Classification
Basic Iterative Clustering Algorithm (K-Means)
• Place K points into the feature space containing the samples to be clustered. These points represent initial group centroids.
• Assign each sample to the group that has the closest centroid.
• When all samples have been assigned, recalculate the positions of the K centroids.
• Repeat Steps 2 and 3 until the centroids no longer move.
Fuente: B. Howell (2006)
Unsupervised Classification
Basic Iterative Clustering Algorithm (K-Means)
• Place K points into the feature space containing the samples to be clustered. These points represent initial group centroids.
• Assign each sample to the group that has the closest centroid.
• When all samples have been assigned, recalculate the positions of the K centroids.
• Repeat Steps 2 and 3 until the centroids no longer move.
Fuente: B. Howell (2006)
Unsupervised Classification
Basic Iterative Clustering Algorithm (K-Means)
• Place K points into the feature space containing the samples to be clustered. These points represent initial group centroids.
• Assign each sample to the group that has the closest centroid.
• When all samples have been assigned, recalculate the positions of the K centroids.
• Repeat Steps 2 and 3 until the centroids no longer move.
Fuente: B. Howell (2006)
Unsupervised Classification
Improving clustering
• “Real world” sample distributions are much more likely to be irregularly shaped with cluster axes rotated to each other.
• These distributions are better characterized by parametric characterized by parametric statistics.
• Simple n-dimensional space segregation is more likely to assign pixels to incorrect clusters.
Fuente: B. Howell (2006)
Unsupervised Classification
Improving clustering
• “Real world” sample distributions are much more likely to be irregularly shaped with cluster axes rotated to each other.
• These distributions are better characterized by parametric characterized by parametric statistics.
• Simple n-dimensional space segregation is more likely to assign pixels to incorrect clusters.
Fuente: B. Howell (2006)
Unsupervised Classification
Improving clustering
• “Real world” sample distributions are much more likely to be irregularly shaped with cluster axes rotated to each other.
• These distributions are better characterized by parametric characterized by parametric statistics.
• Simple n-dimensional space segregation is more likely to assign pixels to incorrect clusters.
Fuente: B. Howell (2006)
Unsupervised Classification
ISODATA (Iterative Self-Organizing Data Analysis Technique)
• Operates in the same iterative fashion as K-Means with three significant differences…
• Uses parametric statistics to describe clusters and determine nearest centroids
• New clusters can formed by splitting “elongated” clusters into multiples
• Clusters with centroids that are “too close” can be lumped together
• Because of these differences, K becomes a “desired” number of final classes, not an absolute
Fuente: B. Howell (2006)
Unsupervised Classification
ISODATA (Iterative Self-Organizing Data Analysis Technique)
• Operates in the same iterative fashion as K-Means with three significant differences…
• Uses parametric statistics to describe clusters and determine nearest centroids
• New clusters can formed by splitting “elongated” clusters into multiples
• Clusters with centroids that are “too close” can be lumped together
• Because of these differences, K becomes a “desired” number of final classes, not an absolute
Fuente: B. Howell (2006)
Unsupervised Classification
ISODATA (Iterative Self-Organizing Data Analysis Technique)
• Operates in the same iterative fashion as K-Means with three significant differences…
• Uses parametric statistics to describe clusters and determine nearest centroids
• New clusters can be formed by splitting “elongated” clusters into multiples
• Clusters with centroids that are “too close” can be lumped together
• Because of these differences, K becomes a “desired” number of final classes, not an absolute
Fuente: B. Howell (2006)
Unsupervised Classification
ISODATA (Iterative Self-Organizing Data Analysis Technique)
• Operates in the same iterative fashion as K-Means with three significant differences…
• Uses parametric statistics to describe clusters and determine nearest centroids
• New clusters can formed by splitting “elongated” clusters into multiples
• Clusters with centroids that are “too close” can be lumped together
• Because of these differences, K becomes a “desired” number of final classes, not an absolute
Fuente: B. Howell (2006)
Unsupervised Classification
ISODATA (Iterative Self-Organizing Data Analysis Technique)
• Operates in the same iterative fashion as K-Means with three significant differences…
• Uses parametric statistics to describe clusters and determine nearest centroids
• New clusters can formed by splitting “elongated” clusters into multiples
• Clusters with centroids that are “too close” can be lumped together
• Because of these differences, K becomes a “desired” number of final classes, not an absolute
Fuente: B. Howell (2006)
a)a) ISODATAISODATA initial distribution of five initial distribution of five
hypothetical mean vectors using hypothetical mean vectors using ±±11σσ
standard deviations in both bands as standard deviations in both bands as
beginning and ending points. b) In the first beginning and ending points. b) In the first
iteration, each candidate pixel is compared iteration, each candidate pixel is compared
to each cluster mean and assigned to the to each cluster mean and assigned to the
cluster whose mean is closest in Euclidean cluster whose mean is closest in Euclidean
distance. c) During the second iteration, a distance. c) During the second iteration, a
new mean is calculated for each cluster new mean is calculated for each cluster
based on the actual spectral locations of the based on the actual spectral locations of the
pixels assigned to each cluster, instead of pixels assigned to each cluster, instead of
the initial arbitrary calculation. This the initial arbitrary calculation. This
involves analysis of several parameters to involves analysis of several parameters to involves analysis of several parameters to involves analysis of several parameters to
merge or split clusters. After the new cluster merge or split clusters. After the new cluster
mean vectors are selected, every pixel in the mean vectors are selected, every pixel in the
scene is assigned to one of the new clusters. scene is assigned to one of the new clusters.
d) This splitd) This split––mergemerge––assign process assign process
continues until there is little change in class continues until there is little change in class
assignment between iterations (the assignment between iterations (the TT
threshold is reached) or the maximum threshold is reached) or the maximum
number of iterations is reached (number of iterations is reached (MM). ).
Jensen, 2005
a) Distribution of 20 a) Distribution of 20 ISODATA ISODATA
mean vectors after just one mean vectors after just one
iteration using Landsat TM iteration using Landsat TM
band 3 and 4 data of band 3 and 4 data of
Charleston, SC. Notice that the Charleston, SC. Notice that the
initial mean vectors are initial mean vectors are
distributed along a diagonal in distributed along a diagonal in
twotwo--dimensional feature space dimensional feature space
according to the according to the ±±22σσ standard standard
deviation logic discussed. b) deviation logic discussed. b)
Distribution of 20 ISODATA Distribution of 20 ISODATA
mean vectors after 20 mean vectors after 20 mean vectors after 20 mean vectors after 20
iterations. The bulk of the iterations. The bulk of the
important feature space (the important feature space (the
gray background) is partitioned gray background) is partitioned
rather well after just 20 rather well after just 20
iterations. iterations.
Jensen, 2005
Jensen, 2005
Plot of the Charleston, SC,
Landsat TM training statistics
for five classes measured in
bands 4 and 5 displayed as
cospectral parallelepipeds.
The upper and lower limit of
each parallelepiped is ±1σ.
The parallelepipeds are
superimposed on a feature
space plot of bands 4 and 5.
Jensen, 2005Jensen, 2005
ISODATA ISODATA
ClusteringClustering
LogicLogic
Jensen, 2005
Classification Classification
Based on Based on
ISODATA ISODATA
ClusteringClustering
Jensen, 2005
Clasificación SupervisadaClasificación Supervisada
Clasificación supervisadaClasificación supervisada
1) estimación de similitud espectral
2) asociación de tipos espectrales con clases útiles
SUPOSICION BASICO
Objetos de interés tienen “firmas espectrales” clarosObjetos de interés tienen “firmas espectrales” claros
*Esto no siempre es el caso
A veces hay que modificar la data para que sea real
- combinaciones de bandas
- imágenes multi-temporales
Clases, áreas de muestras, y mímicosClases, áreas de muestras, y mímicos
Clase: Unido deseado, en la forma de un cluster espectral
2 atributos: identidad y firma espectral
Área de muestra: una región de una imagen que es un buen
ejemplo de un clase; se utilice para definirejemplo de un clase; se utilice para definir
clústeres espectrales de una clase especifica; se
define la identidad con foto-interpretación o
información del campo
Mímicos: otros unidos de mapeo con clústeres similares
Clasificación supervisada
Supervised classification involves imposing a priori
information classes on a landscape.
Implicit in the supervised classification process is the
notion that the spectral data of members of a class will
have similar statistical characteristics and that those
characteristics can be visually discerned and manually
segregated by a human.
The process of creating a class structure and The process of creating a class structure and
determining the statistical characteristics of each class
is called training.
Training is the method by which a classifier “learns” the
appearance of individual classes.
For the classifier to be successful in properly assigning
pixels to a class, the training samples must be “pure”
(consisting of class members only).
Fuente: B. Howell (2006)
Clasificación supervisada
• Filosofía y estrategia de entrenamiento manual
• Seleccionar las bandas insumos con mayor
información
• Mostrar combinaciones de bandas con mayor
contraste entre clases
• No seleccionar muestras que no son miembros
del clase de interés
• Examinar histogramas para determinar si las
muestras son buenas (separables)
• Seleccionar un rango de muestras de un clase, y
combinarlos antes de la clasificación
Fuente: B. Howell (2006)
ParallelepipedParallelepiped
HybridHybrid
Minimum DistanceMinimum Distance
Maximum LikelihoodMaximum Likelihood HybridHybridMaximum LikelihoodMaximum Likelihood
Classifiers
Parallelepiped
• Determines class membership using parallelepipeds
(n-dimensional “boxes” in feature space)
• Advantages:
• Very fast
• Can classify 100% of candidates
• Makes good-looking output• Makes good-looking output
• Disadvantages:
• Candidates that fall outside any parallelepiped
remain unclassified
• Candidates that fall in overlaps are assigned to
the “first” parallelepiped
• Poorly matched to normal data distributions
Fuente: B. Howell (2006)
Classifiers
Minimum Distance
• Determines class membership by measuring distance
from class centroid
• Advantages:
• Fast
• More accurate* classification than Parallelepiped
• Better than Parallelepiped for handling “real • Better than Parallelepiped for handling “real
world” data distributions
• Disadvantages:
• Candidates that fall outside distance limits remain
unclassified
• Candidates that fall in overlaps are assigned by
an operator defined rule
• Imperfectly matched to normal data distributions
*assuming you know how to make it so
Fuente: B. Howell (2006)
Classifiers
Maximum Likelihood
• Determines class membership using parametric
statistics
• Advantages:
• Very well matched to normal data distributions
• More accurate classification* than Parallelepiped
or Minimum Distanceor Minimum Distance
• Candidates that fall into overlaps are assigned
based on likelihood of membership
• Disadvantages:
• Candidates that fall outside any parallelepiped
remain unclassified
• Accuracy heavily dependent on normal data
distributions
*assuming you know how to make it so
Fuente: B. Howell (2006)
Classifiers
Hybrid
• Determines class membership using parametric and
nonparametric techniques
• Advantages:
• Fast and very accurate*
• Perform first-order classifications using
ParallelepipedsParallelepipeds
• Perform second-order classification on outliers
and overlaps using distance or likelihood rule
• Disadvantages:
• Virtually every mistake that can be made using
Parallelepiped, Minimum Distance, and Maximum
Likelihood classifiers can be achieved in a single
operation
*assuming you know how to make it so
Fuente: B. Howell (2006)
Análisis de Mezclas EspectralesAnálisis de Mezclas Espectrales
•Imágenes multiespectrales miden
spectra integrada en cada píxel
•Cada píxel contiene materiales
diferentes, cada con su firma diferentes, cada con su firma
espectral diferente
•Varios tipos de spectra usualmente
están mezclados. Esos son mezclas.
•Otros tipos no mezcla mucho.
Fuente: A. Gillespie, la Universidad de WashingtonFuente: A. Gillespie, la Universidad de Washington
Usualmente, el numero de clases (‘endmembers’)
útiles para datos de Landsat es 4-5
Puede ser 8-10 para datos híper-espectrales
Hay muchos componentes espectrales en varias Hay muchos componentes espectrales en varias
escenas, pero usualmente no mezclan, entonces no son
útiles.
Análisis de mezclas espectrales es útil
porque –
1) Genera imágenes de fracciones que
se puede entender fácilmente
2) Reducción en la dimensionalidad de
los datos sin botar mucha los datos sin botar mucha
información útil
3) Identificación de efectos topográficos
para mas estable información para
análisis en SIG
Soil Soil
100
90
80
70
60
50
10
20
30
40
50
60
Sombra
Soil Soil40
30
20
10
100908070605040302010
100
60
70
80
90
% sueloSueloVegetación
verde
Fuente: UW ESS 421 (2004)Fuente: UW ESS 421 (2004)
Fuente: Lu et al (2002)Fuente: Lu et al (2002)
Imagen de Landsat5
de un parte de la
Reserva Forestal
Nacional Gifford
Pinchot de los
EE.UU.
Fuente: UW ESS 421 (2004)Fuente: UW ESS 421 (2004)
BurnedMature
regrowth
Old growth
Immature
regrowth
BroadleafBroadleaf
Deciduous
Clearcut
Grasses
Fuente: UW ESS 421 (2004)Fuente: UW ESS 421 (2004)
Vegetacion
verdeVegetacion no
fotosintetica
(NPV)
Claro = abundancia del objeto
Sombra
Rojo = NPV
Verde = veg. verde
Azul = sombra
Fuente: UW ESS 421 (2004)Fuente: UW ESS 421 (2004)
SMA can easily SMA can easily extract areas of extract areas of
bare soilbare soil
Interpreting spectral Interpreting spectral unmixing resultsunmixing results
Color Code
Color in RG comp % Soil
% Photosynthetic Vegetation Description
1 Yellow
Very high
(90-100%)
Very high
(90-100%) Cropland
2
Light
green
Low-Very Low
(0-30%)
High
(80-100%) Open forest
3
Light
green
Medium
(60-70%)
Medium
(40-50%) Shrubland
CATHALAC (unpublished, Nov CATHALAC (unpublished, Nov
2009)2009)
3 green (60-70%) (40-50%) Shrubland
4
Dark
green
Very low
(0-10%)
Medium
(40-80%) Closed forest
5 Red
Very high
(90-100%)
Low
(10-20%) Bare land / urban
6
Coffee
brown
Low
(10-30%)
Low-Medium
(30-50%) Mangrove scrub
7 Black
Very low
(0-10%)
Low-Very Low
(0-20%) Water
Black
Very low
(0-10%)
Low-Medium
(20-40%) Wetland
Interpreting spectral unmixing resultsInterpreting spectral unmixing results
Color Code
Color in RG comp % Soil
% Photosynthetic Vegetation Description
1 Yellow
Very high
(90-100%)
Very high
(90-100%) Cropland
2
Light
green
Low-Very Low
(0-30%)
High
(80-100%) Open forest
3
Light
green
Medium
(60-70%)
Medium
(40-50%) Shrubland
CATHALAC (unpublished, Nov CATHALAC (unpublished, Nov
2009)2009)
3 green (60-70%) (40-50%) Shrubland
4
Dark
green
Very low
(0-10%)
Medium
(40-80%) Closed forest
5 Red
Very high
(90-100%)
Low
(10-20%) Bare land / urban
6
Coffee
brown
Low
(10-30%)
Low-Medium
(30-50%) Mangrove scrub
7 Black
Very low
(0-10%)
Low-Very Low
(0-20%) Water
Black
Very low
(0-10%)
Low-Medium
(20-40%) Wetland
4. Closed tree canopy4. Closed tree canopy
(e.g. ‘mature’ forest):(e.g. ‘mature’ forest):
Should be defined Should be defined
principally by low soil principally by low soil
exposure and moderate to exposure and moderate to
high chlorophyll contenthigh chlorophyll content
3. Open tree canopy3. Open tree canopy
(e.g. ‘open’ forest):(e.g. ‘open’ forest):
Should be defined Should be defined
principally by some soil principally by some soil
exposure and high exposure and high
1.1. 2.2. 3.3. 4.4.
Source: ITTO / JOFCA
2. Low vegetation 2. Low vegetation
(e.g. cropland, (e.g. cropland,
shrubland):shrubland):
Should be defined Should be defined
by high chlorophyll by high chlorophyll
content and some content and some
soil exposure b/c of soil exposure b/c of
usual low plant usual low plant
density (i.e. no density (i.e. no
canopy)canopy)
1. Bare land1. Bare land
(e.g. urban areas):(e.g. urban areas):
Should be defined by Should be defined by
high soil exposure high soil exposure
and no chlorophyll and no chlorophyll
contentcontent
exposure and high exposure and high
chlorophyll contentchlorophyll content
Spectral Mixture Analysis works with spectra that mix together
to estimate mixing fractions for each pixel in a scene.
Spectral Mixtures, green leaves and soil
100
0
20
40
60
80
0 1 2 3
Wavelength, micrometers
Ref
lect
ivity
, %
0% leaves
25% leaves
50% leaves
75% leaves
100% leaves
The extreme
spectra that mix and
that correspond to
scene components
are called spectral
endmembers.
Please note – wavelength scale is messed upSource: TBDSource: TBD
Forest Spectral Endmembers
0
20
40
60
80
100
0 1 2 3
Ref
lect
ivit
y, %
dry grass
leaves
soil
Endmembers from one
type of scene – forest,
lake, desert – form a
In a forest, important endmembers may be leaves, wood,
shade, and soil.
In a desert, leaves may be less important, but there may
several rock types.
0 1 2 3
Wavelength, micrometers lake, desert – form a
cohort.
Source: TBDSource: TBD
Soil Soil
100
90
80
70
60
50
10
20
30
40
50
60
We can use a
ternary diagram
used to show
mixtures of forest
endmembers.
We will see a
Shade
Soil Soil 40
30
20
10
100908070605040302010
100
60
70
80
90
percent soil
We will see a
detailed example
of this in a later
lecture
SoilGV
Source: TBDSource: TBD
BurnedMature
regrowth
Old growth
Immature
regrowth
BroadleafBroadleaf
Deciduous
Clearcut
Grasses
Source: TBDSource: TBD
Round 1: 1Round 1: 1stst set of samples, selected from PPI of all MNF componentsset of samples, selected from PPI of all MNF components
1: Unconstrained
All MNF components
2: Constrained – Weight 1
All MNF components
3: Constrained – Weight
All MNF components
Round 1: 1Round 1: 1stst set of samples, selected from PPI of all MNF componentsset of samples, selected from PPI of all MNF components
4: Constrained – Weight 50
All MNF components
5: Unconstrained
ALI bands 3,4,5,6,8,9
6: Constrained – Weight
ALI bands 3,4,5,6,8,9
Round 2: 2Round 2: 2ndnd set of samples, selected from PPI of first 3 componentsset of samples, selected from PPI of first 3 components
7: Constrained – Weight 1
ALI bands 4,5,6,8
8: Constrained – Weight 1,000
ALI bands 4,5,6,8
9: Constrained – Weight 1
MNF components 1,2,3
Round 2: 2Round 2: 2ndnd set of samples, selected from PPI of first 3 componentsset of samples, selected from PPI of first 3 components
10: Constrained – Weight 1
MNF components 1,3
11: Constrained – Weight 100
MNF components 1,3
12: Constrained – Weight
MNF components 1,3
Round 2: 2Round 2: 2ndnd set of samples, selected from PPI of first 3 componentsset of samples, selected from PPI of first 3 components
13: Unconstrained
MNF components 1,3
14: Constrained – Weight 1
All MNF components
15: Constrained – Weight
Color 2000 2005 2010Black Low Low Low
Red High Low Low
Green Low High Low
Blue Low Low High
Yellow High High Low
Magenta High Low High
Cyan Low High High
White High High High
RGB NDVI composite: 2000RGB NDVI composite: 2000--20052005--20102010
Cambio de cobertura: 2000Cambio de cobertura: 2000--20102010
Cambio de Cambio de cobertura:cobertura:20002000--20102010
Color KeyBlack No change: forest
Red Regenerated 2000-05; no change 2005-10
Green Cut 2000-05, regenerated 2005-10
Blue No change 2000-05; cut 2005-10
Yellow No change 2000-05; regeneration 2005-10
Magenta Regenerated 2000-05, re-cut 2005-10
Cyan Cut 2000-05; no change 2005-10
White No change: non-forest
Color 2000 2005 2010Black High High High No change: forest
Red Low High High Regenerated 2000-05; no change 2005-10
Green High Low High Cut 2000-05, regenerated 2005-10
Blue High High Low No change 2000-05; deforested 2005-10
Yellow Low Low High No change 2000-05; regeneration 2005-10
Magenta Low High Low Regenerated 2000-05, re-cut 2005-10
Cyan High Low Low Cut 2000-05; no change 2005-10
White Low Low Low No change: non-forest
White No change: non-forest
ÍndicesÍndices
A
NDx
ND
y
A
B
° x
NDx
ND
y
Sombra
° x
A con sol
B con sol
A
Línea de ratio constante
y
x
x/y
y/z
A
B
NDx
ND
y
A
B
° x
NDx
ND
y
Sombra
° x
A con sol
B con sol
°Después: LandSat7 Después: LandSat7 -- 11 de mayo de 200711 de mayo de 2007
Plumas de humoPlumas de humo
0 1 2 3 40.5Miles
CicatricesCicatrices
°Antes: LandSat7 Antes: LandSat7 -- 21 de marzo de 200621 de marzo de 2006
0 1 2 3 40.5Miles
°20062006
°20072007
Procesamiento Procesamiento DigitalDigital
Principio: Principio: Se puede aplicar Se puede aplicar
metodologías para extraer info útil de metodologías para extraer info útil de
imágenes satelitalesimágenes satelitales
0 1 2 3 40.5Miles °
0 1 2 3 40.5Miles
20072007
Normalized Difference Vegetation Normalized Difference Vegetation Index (NDVI): Index (NDVI): ratio entre la luz ratio entre la luz
infrarrojo cercano (NIR) y rojo (R), infrarrojo cercano (NIR) y rojo (R),
indicando vegetación en estrésindicando vegetación en estrés
Normalized Burn Ratio (NBR): Normalized Burn Ratio (NBR): ratio ratio
entre infrarrojo medio (SWIR) y entre infrarrojo medio (SWIR) y
infrarrojo cercano (NIR), para delinear infrarrojo cercano (NIR), para delinear
cicatrices (muy similar a NDVI)cicatrices (muy similar a NDVI)
°LandSat: 21 March 2006LandSat: 21 March 2006
NDVI: March 2006High : 0.964912
Low : -0.957447
0 0.9 1.8 2.7 3.60.45Miles
°LandSat: 11 May 2007LandSat: 11 May 2007
Contamination por Contamination por
el humoel humo
NDVI: May 2007High : 0.964912
Low : -0.957447
0 0.9 1.8 2.7 3.60.45Miles
°Differencing 2007 NDVI against 2006 NDVIDifferencing 2007 NDVI against 2006 NDVI
NDVI differenceHigh : 0.919945
Low : -1.04501
0 0.9 1.8 2.7 3.60.45Miles
°LandSat: 21 March 2006LandSat: 21 March 2006
0 0.9 1.8 2.7 3.60.45Miles
Normalized Burn RatioHigh : 0.978947
Low : -0.931034
°LandSat: 11 May 2007LandSat: 11 May 2007
0 0.9 1.8 2.7 3.60.45Miles
Normalized Burn RatioHigh : 0.978947
Low : -0.931034
°Differencing 2007 NBR against 2006 NBRDifferencing 2007 NBR against 2006 NBR
0 0.9 1.8 2.7 3.60.45Miles
Normalized Difference
Burn Ratio
-1.22 - -0.42
-0.42 - -0.31
-0.31 - -0.18
-0.18 - 0
0 - 1
°
0 0.9 1.8 2.7 3.60.45Miles
Normalized Difference
Burn Ratio
-1.22 - -0.42
-0.42 - -0.31
-0.31 - -0.18
-0.18 - 0
0 - 1
NDVI differenceHigh : 0.919945
Low : -1.04501
°
0 0.9 1.8 2.7 3.60.45Miles
°°
NDBRNDBR NDVI Diff.NDVI Diff.
°
0 1 2 3 40.5Miles
°
0 1 2 3 40.5Miles
20062006 20072007
°LandSat7: 11 de mayo de 2007LandSat7: 11 de mayo de 2007
Estimación: ~24,000 acres quemadasEstimación: ~24,000 acres quemadas
0 0.9 1.8 2.7 3.60.45Miles
ValidaciónValidación
c. 2000c. 2000
c. 2009c. 2009
Matrices de confusion / Matrices de error
Datos de validación
A B C D E F
A
B
clas
ific
ació
n Sumas de filas
480 0 5 0 0 0 485
0 52 0 20 0 0 72B
C
D
E
FDat
os
de
la c
clas
ific
ació
n
Sumas de columnas
0
0
0
0
0
0
0
0
480
52
16
68
0 20 0 0 72
Classmangroves
no mangroves water Total
User accuracy
Producer accuracy
Total class accuracy
Number %
mangroves 1 59 10 70 21% 1% 50% 26%
no mangroves 1 75 5 81 24% 93% 55% 74%
water 0 2 186 188 55% 99% 93% 96%
Total 2 136 201 339 100%
Producer
Accuracy 50% 55% 93% 77.3%
Classmangroves
no mangroves water Total
User accuracy
Producer accuracy
Total class accuracy
Number %
mangroves 2 77 6 85 26% 2% 100% 51%
no mangroves 0 54 5 59 18% 92% 39% 65%
water 0 7 182 189 57% 96% 94% 95%
Total 2 138 193 333100
%
Producer
Accuracy 100% 39% 94% 71.5%
Fuentes de Mayor InformaciónFuentes de Mayor InformaciónLibros (vea www.amazon.com) -
• Teledetección Ambiental (2002) – Emilio Chuvieco
• Remote Sensing and Image Interpretation (2007) – Thomas Lillesand, Ralph Kiefer, Jonathan Chipman
• Remote Sensing of the Environment (2006) – John R. JensenJensen
• Remote Sensing for GIS Managers (2005) – Stan Aronoff
En línea -
• TELEDET: http://www.teledet.com.uy/tutorial-imagenes-satelitales/imagenes-satelitales-tutorial.htm
• NASA: http://rst.gsfc.nasa.gov/
• CATHALAC: servir@cathalac.org
• GOOGLE / Wikipedia
Referencias / ReconocimientosReferencias / Reconocimientos
Mucha de la información en este presentación
fue adoptada de los siguientes fuentes:
• Burgess Howell, NASA GSFC (2006)• Burgess Howell, NASA GSFC (2006)
• Jason Tullis, University of Arkansas (2005)
• Lecturas / materiales del curso ESS 421 y ESS
422 de la U. de Washington (2004)
¿¿Preguntas?Preguntas?
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