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  • It is a unique optical sensor that delivers calibrated images of the upwelling spectral radiance in 224 contiguous spectral channels (bands) with wavelengths from 400 to 2500 nanometers. AVIRIS has been flown on four aircraft platforms: NASA's ER-2 jet, Twin Otter International's turboprop, Scaled Composites' Proteus, and NASA's WB-57.

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Linear spectral mixture analysis is designed to address this problem and it assumes that the pixel-to-pixel variability in a scene results from varying proportions of spectral endmembers. In this paper we present a different endmember-search algorithm called the Successive Projection Algorithm (SPA). Latent Dirichlet Allocation (LDA) Latent Dirichlet Allocation (LDA) is a type of probabilistic topic model commonly used in natural language processing to extract topics from large collections of documents in an unsupervised manner. LDA assumes that each document in a corpus (collection of documents) is associated with a mixture of ...

Data vendors usually provide gains/offsets in the metadata that accompanies the imagery. NASA/JPL documented the gain values for 2011 AVIRIS data in the file AVIRIS11_gain, which you will use later in this tutorial. By applying the gains from this file, the AVIRIS radiance image will be in units of µW/(cm 2 * sr * nm). Oct 23, 2016 · Python module for hyperspectral image processing. Contribute to spectralpython/spectral development by creating an account on GitHub. ... # aviris.py - This file is ...

AVIRIS-NG measures reflected radiance at 5-nanometer (nm) intervals in the visible to shortwave infrared spectral range between 380 and 2510 nm. Measurements are radiometrically and geometrically calibrated and provided at approximately 5-meter spatial resolution. Your df_aviris dataframe doesn't appear to have a column name 'x' nor 'y', so you have to use left_on and right_on for different columns in your dataframe. Or your rename the columns in your df_aviris to match what you expect. – Scott Boston Jul 5 '17 at 5:01

Jul 30, 2012 · I ran some Python to remove the space from my variable and this fixed the issue. I was inserting the values from a field into a variable, and naming the feature class from the variable. Unfortunately, some of the values had spaces in them.

AVIRIS-NG measures reflected radiance at 5-nanometer (nm) intervals in the visible to shortwave infrared spectral range between 380 and 2510 nm. Measurements are radiometrically and geometrically calibrated and provided at approximately 5-meter spatial resolution. Linear spectral mixture analysis is designed to address this problem and it assumes that the pixel-to-pixel variability in a scene results from varying proportions of spectral endmembers. In this paper we present a different endmember-search algorithm called the Successive Projection Algorithm (SPA). Ham et al. (2005) proposed a combination of RF with “adaptive random subspace feature selection” within a binary hierarchical classifier (BHC) to identify LC classes in seasonal swamps, occasional swamps, and drier woodland from Hyperion and AVIRIS data.

Support Vector Regression (SVR) using linear and non-linear kernels¶. Toy example of 1D regression using linear, polynomial and RBF kernels. Jun 20, 2019 · Opening and reading image files for hyperspectral image analysis. AVIRIS: https://aviris.jpl.nasa.gov/alt_locator/ THE BEST WEBSITE TO LEARN GIS WITH PYTHON:... principea of this algorithm using synthetic bands for the scene of HIS AVIRIS 92AV3C [1] , Figure.1.Then we approve its e ectiveness with applying it to real datat of HSI AVIRIS 92AV3C. So each pixel is shown as a vector of 220 components. Figure.2. shows the vector pixels notion [7 ]. So reducing di- This means you can author web maps in one ArcGIS app (including the Python API) and view and modify them in another. ... [aviris_layer]) ...

was an AVIRIS image for Jasper Ridge, California, which originally contained 224 bands (Figure 3b). The HYDICE image shows in urban area, while the AVRIS image covers a predominantly rural area. Other information on these two hyperspectral images is summarized in Table 1. Following the collection of image data, ground Jun 08, 2017 · A python suite for the identification and characterization of mining activity within AVIRIS data. Project details. Project links. COAL is a Python library for processing hyperspectral imagery from remote sensing devices such as the Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS-C & AVIRIS-NG). COAL was originally developed as a 2016 – 2017 Senior Capstone collaboration between scientists at the Jet Propulsion Laboratory (JPL) and computer science

The fields notes & photos and AVIRIS data in this publication are for site 3. Residue was a focus for the research; therefore the notes and photos document the residue condition for the fields. This publication includes: Indian Pine Site 3 AVIRIS hyperspectral data image file (19920612_AVIRIS_IndianPine_Site3.tif) Documentation (reference data) Aug 19, 2015 · Surely the software gives the possibility to create custom made analysis tools by scripting in python, but this becomes very complicated for us, not feasible for a ...

principea of this algorithm using synthetic bands for the scene of HIS AVIRIS 92AV3C [1] , Figure.1.Then we approve its e ectiveness with applying it to real datat of HSI AVIRIS 92AV3C. So each pixel is shown as a vector of 220 components. Figure.2. shows the vector pixels notion [7 ]. So reducing di- COAL is a Python library for processing hyperspectral imagery from remote sensing devices such as the Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS). COAL is being developed as a 2016 – 2017 senior capstone collaboration between scientists at the Jet Propulsion Laboratory (JPL) and computer science students at Oregon State University (OSU). Jun 25, 2017 · Maybe multibandread() should work with .mat files, but, for whatever reason, it doesn't. You can send in a request for enhancement. My guess is that .mat files can be virtually anything under the sun while multibandread() is for the very specific use of reading in imagery that has multiple channels. Spectral Python or SPy is a Python module for hyperspectral image processing. It works with Python 2.6/3.3 or higher versions (Python 3.6.2 is available since yesterday! 😉). You can read, write, visualize and classify data with SPy. It is in a quite early development stage (version 0.19), but it is worth to give it a try! principea of this algorithm using synthetic bands for the scene of HIS AVIRIS 92AV3C [1] , Figure.1.Then we approve its e ectiveness with applying it to real datat of HSI AVIRIS 92AV3C. So each pixel is shown as a vector of 220 components. Figure.2. shows the vector pixels notion [7 ]. So reducing di-

Jun 08, 2017 · A python suite for the identification and characterization of mining activity within AVIRIS data. Project details. Project links.

Figure 2-1. Illustration of an AVIRIS-NG Level 0 Spectral Radiance Image Cube Onboard processing generates ancillary files that can be used to orthorectify the radiance image cube. These files follow the convention of ENVI and prior AVIRIS-NG and AVIRIS data products. Spectral Python or SPy is a Python module for hyperspectral image processing. It works with Python 2.6/3.3 or higher versions (Python 3.6.2 is available since yesterday! 😉). You can read, write, visualize and classify data with SPy. It is in a quite early development stage (version 0.19), but it is worth to give it a try! This means you can author web maps in one ArcGIS app (including the Python API) and view and modify them in another. ... [aviris_layer]) ...

COAL is a Python library for processing hyperspectral imagery from remote sensing devices such as the Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS). COAL is being developed as a 2016 – 2017 senior capstone collaboration between scientists at the Jet Propulsion Laboratory (JPL) and computer science students at Oregon State University (OSU). Latent Dirichlet Allocation (LDA) Latent Dirichlet Allocation (LDA) is a type of probabilistic topic model commonly used in natural language processing to extract topics from large collections of documents in an unsupervised manner. LDA assumes that each document in a corpus (collection of documents) is associated with a mixture of ... Support Vector Regression (SVR) using linear and non-linear kernels¶. Toy example of 1D regression using linear, polynomial and RBF kernels.

AVIRIS¶ SPy supports data files generated by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) . AVIRIS files are automatically recognized by the open_image function; however, spectral band calibration files are not automatically recognized; therefore you may want to open the image as an AVIRIS file explicitly and specify the cal file. COAL is a Python library for processing hyperspectral imagery from remote sensing devices such as the Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS-C & AVIRIS-NG). COAL was originally developed as a 2016 – 2017 Senior Capstone collaboration between scientists at the Jet Propulsion Laboratory (JPL) and computer science Linear spectral mixture analysis is designed to address this problem and it assumes that the pixel-to-pixel variability in a scene results from varying proportions of spectral endmembers. In this paper we present a different endmember-search algorithm called the Successive Projection Algorithm (SPA). At Avira, we believe that everyone has the right to enjoy life online safely, securely, and privately. And that’s something we’ve believed in for decades. In that time, we’ve built a base of over 100 million customers and pioneered the freemium software business model—offering high quality, market-leading security products for free to ... The NASA AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) instrument acquired data over the Kennedy Space Center (KSC), Florida, on March 23, 1996. AVIRIS acquires data in 224 bands of 10 nm width with center wavelengths from 400 - 2500 nm. The KSC data, acquired from an altitude of approximately 20 km, have a spatial resolution of 18 m.

This means you can author web maps in one ArcGIS app (including the Python API) and view and modify them in another. ... [aviris_layer]) ... Ham et al. (2005) proposed a combination of RF with “adaptive random subspace feature selection” within a binary hierarchical classifier (BHC) to identify LC classes in seasonal swamps, occasional swamps, and drier woodland from Hyperion and AVIRIS data.

This scene was gathered by AVIRIS sensor over the Indian Pines test site in North-western Indiana and consists of 145\times145 pixels and 224 spectral reflectance bands in the wavelength range 0.4–2.5 10^(-6) meters. This scene is a subset of a larger one. Figure 2-1. Illustration of an AVIRIS-NG Level 0 Spectral Radiance Image Cube Onboard processing generates ancillary files that can be used to orthorectify the radiance image cube. These files follow the convention of ENVI and prior AVIRIS-NG and AVIRIS data products. The NASA AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) instrument acquired data over the Kennedy Space Center (KSC), Florida, on March 23, 1996. AVIRIS acquires data in 224 bands of 10 nm width with center wavelengths from 400 - 2500 nm. The KSC data, acquired from an altitude of approximately 20 km, have a spatial resolution of 18 m.

arivis Storyboard. With the arivis Storyboard High Resolution Movies can be created easily to produce impressive sequences in 3D or 3D time series. Views, render modes and specific time points as well as the use of clipping planes or varying opacities of individual channels can be stored as key frames. arivis Vision4D automatically interpolates between these key frames to produce a seamless ... This scene was gathered by AVIRIS sensor over the Indian Pines test site in North-western Indiana and consists of 145\times145 pixels and 224 spectral reflectance bands in the wavelength range 0.4–2.5 10^(-6) meters. This scene is a subset of a larger one.

Spectral Python (SPy) is a pure Python module for processing hyperspectral image data. It has functions for reading, displaying, manipulating, and classifying hyperspectral imagery. It can be used interactively from the Python command prompt or via Python scripts. SPy is free, open source software distributed under the GNU General Public License. Jul 30, 2012 · I ran some Python to remove the space from my variable and this fixed the issue. I was inserting the values from a field into a variable, and naming the feature class from the variable. Unfortunately, some of the values had spaces in them. 220 Band Hyperspectral Image: June 12, 1992 AVIRIS image Indian Pine Test Site 3 (2 x 2 mile portion of Northwest Tippecanoe County, Indiana) This data set is available from the Purdue University Research Repository (PURR) along with ground reference information including the field notes and photos.

AVIRIS-NG measures reflected radiance at 5-nanometer (nm) intervals in the visible to shortwave infrared spectral range between 380 and 2510 nm. Measurements are radiometrically and geometrically calibrated and provided at approximately 5-meter spatial resolution. Apr 04, 2019 · Function for creating pickled AVIRIS flight data using spectral python library By defualt loads AVIRIS flight f111115t01p00r08 over sunshine canyon landfill Reference ===== N/A Inputs ===== Optional: aviris_flight - AVIRIS flight number aviris_flight_folder - AVIRIS flight folder to load aviris_bands_filename - filename to load in AVIRIS flight ... Pycoal: A python suite for the identification and characterization of mining activity within AVIRIS data.

was an AVIRIS image for Jasper Ridge, California, which originally contained 224 bands (Figure 3b). The HYDICE image shows in urban area, while the AVRIS image covers a predominantly rural area. Other information on these two hyperspectral images is summarized in Table 1. Following the collection of image data, ground Your df_aviris dataframe doesn't appear to have a column name 'x' nor 'y', so you have to use left_on and right_on for different columns in your dataframe. Or your rename the columns in your df_aviris to match what you expect. – Scott Boston Jul 5 '17 at 5:01

And in Python, a database isn’t the simplest solution for storing a bunch of structured data. This is what dataset is going to change! dataset provides a simple abstraction layer removes most direct SQL statements without the necessity for a full ORM model - essentially, databases can be used like a JSON file or NoSQL store. Oct 19, 2016 · In your example, AVIRIS has 432 elements (AVIRIS.shape[0]).What's the shape of vals1?I'm guessing less than 432. And what version of numpy are you running? – hpaulj Feb 17 '16 at 19:40

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Jun 08, 2017 · A python suite for the identification and characterization of mining activity within AVIRIS data. Project details. Project links. Jun 25, 2017 · Maybe multibandread() should work with .mat files, but, for whatever reason, it doesn't. You can send in a request for enhancement. My guess is that .mat files can be virtually anything under the sun while multibandread() is for the very specific use of reading in imagery that has multiple channels. The fields notes & photos and AVIRIS data in this publication are for site 3. Residue was a focus for the research; therefore the notes and photos document the residue condition for the fields. This publication includes: Indian Pine Site 3 AVIRIS hyperspectral data image file (19920612_AVIRIS_IndianPine_Site3.tif) Documentation (reference data) Your df_aviris dataframe doesn't appear to have a column name 'x' nor 'y', so you have to use left_on and right_on for different columns in your dataframe. Or your rename the columns in your df_aviris to match what you expect. – Scott Boston Jul 5 '17 at 5:01

So SPy uses pure Python drivers to load the following file types into NumPy: AVIRIS, ERDAS .LAN, ENVI headers and data, pure BSQ, BIL, and BIP files. SPy performs a variety of remote sensing operations including supervised and unsupervised classifications, NDVIs, hypercube renderings, and more. COAL is a Python library for processing hyperspectral imagery from remote sensing devices such as the Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS). COAL is being developed as a 2016 – 2017 senior capstone collaboration between scientists at the Jet Propulsion Laboratory (JPL) and computer science students at Oregon State University (OSU).

Latent Dirichlet Allocation (LDA) Latent Dirichlet Allocation (LDA) is a type of probabilistic topic model commonly used in natural language processing to extract topics from large collections of documents in an unsupervised manner. LDA assumes that each document in a corpus (collection of documents) is associated with a mixture of ... COAL is a Python library for processing hyperspectral imagery from remote sensing devices such as the Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS). COAL is being developed as a 2016 – 2017 senior capstone collaboration between scientists at the Jet Propulsion Laboratory (JPL) and computer science students at Oregon State University (OSU). Oct 23, 2016 · Python module for hyperspectral image processing. Contribute to spectralpython/spectral development by creating an account on GitHub. ... # aviris.py - This file is ...

The fields notes & photos and AVIRIS data in this publication are for site 3. Residue was a focus for the research; therefore the notes and photos document the residue condition for the fields. This publication includes: Indian Pine Site 3 AVIRIS hyperspectral data image file (19920612_AVIRIS_IndianPine_Site3.tif) Documentation (reference data) The fields notes & photos and AVIRIS data in this publication are for site 3. Residue was a focus for the research; therefore the notes and photos document the residue condition for the fields. This publication includes: Indian Pine Site 3 AVIRIS hyperspectral data image file (19920612_AVIRIS_IndianPine_Site3.tif) Documentation (reference data)

Pycoal: A python suite for the identification and characterization of mining activity within AVIRIS data. And in Python, a database isn’t the simplest solution for storing a bunch of structured data. This is what dataset is going to change! dataset provides a simple abstraction layer removes most direct SQL statements without the necessity for a full ORM model - essentially, databases can be used like a JSON file or NoSQL store. Read aviris data with python. Ask Question Asked 10 months ago. Active 9 months ago. Viewed 182 times 2. I try for too ling to read the envi file in python. ...

Jun 25, 2017 · Maybe multibandread() should work with .mat files, but, for whatever reason, it doesn't. You can send in a request for enhancement. My guess is that .mat files can be virtually anything under the sun while multibandread() is for the very specific use of reading in imagery that has multiple channels. AVIRIS. Imaging spectrometer data from the Jet Propulsion Laboratory can be downloaded or requested via the AVIRIS and AVIRIS-NG websites. The National Map. The National Map from the United States Geological Survey (USGS) provides detailed hydrography, transportation, and elevation datasets. Usage. This section demonstrates basic usage of COAL. Jun 08, 2017 · A python suite for the identification and characterization of mining activity within AVIRIS data. Project details. Project links.

220 Band Hyperspectral Image: June 12, 1992 AVIRIS image Indian Pine Test Site 3 (2 x 2 mile portion of Northwest Tippecanoe County, Indiana) This data set is available from the Purdue University Research Repository (PURR) along with ground reference information including the field notes and photos. was an AVIRIS image for Jasper Ridge, California, which originally contained 224 bands (Figure 3b). The HYDICE image shows in urban area, while the AVRIS image covers a predominantly rural area. Other information on these two hyperspectral images is summarized in Table 1. Following the collection of image data, ground

AVIRIS. Imaging spectrometer data from the Jet Propulsion Laboratory can be downloaded or requested via the AVIRIS and AVIRIS-NG websites. The National Map. The National Map from the United States Geological Survey (USGS) provides detailed hydrography, transportation, and elevation datasets. Usage. This section demonstrates basic usage of COAL.

SPATIAL/SPECTRAL IDENTIFICATION OF ENDMEMBERS FROM AVIRIS DATA USING MATHEMATICAL MORPHOLOGY Antonio Plaza,1 Pablo Martínez,1 J. Anthony Gualtieri,2 and Rosa M. Pérez1 1. INTRODUCTION During the last several years, a number of airborne and satellite hyperspectral sensors have been developed or A tutorial on support vector regression∗ ALEX J. SMOLA and BERNHARD SCHOLKOPF¨ RSISE, Australian National University, Canberra 0200, Australia [email protected] Max-Planck-Institut f¨ur biologische Kybernetik, 72076 T¨ubingen, Germany [email protected] Received July 2002 and accepted November 2003 principea of this algorithm using synthetic bands for the scene of HIS AVIRIS 92AV3C [1] , Figure.1.Then we approve its e ectiveness with applying it to real datat of HSI AVIRIS 92AV3C. So each pixel is shown as a vector of 220 components. Figure.2. shows the vector pixels notion [7 ]. So reducing di- Hyperspectral Image dimensionality reduction? I have an aviris dataset with 776 samples, 21576 lines and 224 bands. It is difficult reading this into matlab as i run into memory issues. was an AVIRIS image for Jasper Ridge, California, which originally contained 224 bands (Figure 3b). The HYDICE image shows in urban area, while the AVRIS image covers a predominantly rural area. Other information on these two hyperspectral images is summarized in Table 1. Following the collection of image data, ground AVIRIS. Imaging spectrometer data from the Jet Propulsion Laboratory can be downloaded or requested via the AVIRIS and AVIRIS-NG websites. The National Map. The National Map from the United States Geological Survey (USGS) provides detailed hydrography, transportation, and elevation datasets. Usage. This section demonstrates basic usage of COAL. Support Vector Regression in R The aim of this script is to create in R the following bivariate SVR model (the observations are represented with blue dots and the predictions with the orange 3D surface) :

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AVIRIS-NG data file sizes can be prohibitively large owing to the large number of raster bands. We created a new tutorial to show users how to work with this data in Python. The tutorial demonstrates how to extract AVIRIS-NG imagery at specific locations, plot individual bands, calculate NDVI and other vegetation indices, and save outputs. Apr 04, 2019 · Function for creating pickled AVIRIS flight data using spectral python library By defualt loads AVIRIS flight f111115t01p00r08 over sunshine canyon landfill Reference ===== N/A Inputs ===== Optional: aviris_flight - AVIRIS flight number aviris_flight_folder - AVIRIS flight folder to load aviris_bands_filename - filename to load in AVIRIS flight ... And in Python, a database isn’t the simplest solution for storing a bunch of structured data. This is what dataset is going to change! dataset provides a simple abstraction layer removes most direct SQL statements without the necessity for a full ORM model - essentially, databases can be used like a JSON file or NoSQL store. This means you can author web maps in one ArcGIS app (including the Python API) and view and modify them in another. ... [aviris_layer]) ...

At Avira, we believe that everyone has the right to enjoy life online safely, securely, and privately. And that’s something we’ve believed in for decades. In that time, we’ve built a base of over 100 million customers and pioneered the freemium software business model—offering high quality, market-leading security products for free to ...

SPATIAL/SPECTRAL IDENTIFICATION OF ENDMEMBERS FROM AVIRIS DATA USING MATHEMATICAL MORPHOLOGY Antonio Plaza,1 Pablo Martínez,1 J. Anthony Gualtieri,2 and Rosa M. Pérez1 1. INTRODUCTION During the last several years, a number of airborne and satellite hyperspectral sensors have been developed or Support Vector Regression (SVR) using linear and non-linear kernels¶. Toy example of 1D regression using linear, polynomial and RBF kernels.

Oct 19, 2016 · In your example, AVIRIS has 432 elements (AVIRIS.shape[0]).What's the shape of vals1?I'm guessing less than 432. And what version of numpy are you running? – hpaulj Feb 17 '16 at 19:40

Support Vector Regression (SVR) using linear and non-linear kernels¶. Toy example of 1D regression using linear, polynomial and RBF kernels.

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In this tutorial, you learned how to build a machine learning classifier in Python. Now you can load data, organize data, train, predict, and evaluate machine learning classifiers in Python using Scikit-learn. The steps in this tutorial should help you facilitate the process of working with your own data in Python.

Jun 25, 2017 · Maybe multibandread() should work with .mat files, but, for whatever reason, it doesn't. You can send in a request for enhancement. My guess is that .mat files can be virtually anything under the sun while multibandread() is for the very specific use of reading in imagery that has multiple channels. was an AVIRIS image for Jasper Ridge, California, which originally contained 224 bands (Figure 3b). The HYDICE image shows in urban area, while the AVRIS image covers a predominantly rural area. Other information on these two hyperspectral images is summarized in Table 1. Following the collection of image data, ground

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At Avira, we believe that everyone has the right to enjoy life online safely, securely, and privately. And that’s something we’ve believed in for decades. In that time, we’ve built a base of over 100 million customers and pioneered the freemium software business model—offering high quality, market-leading security products for free to ... ®Nokia 3330 original®Biodata questionnaire sampleMilitary funeral honors handbookSteam install button greyed out
Python for Hyperspectral Analysis Python 2.x vs. 3.x • “ShouldI use Python 2 or Python 3 for my development activity?” • One sentence difference : “Python 2.x is legacy and Python 3.x is the present and future of the language. • Python 2.x and Python 3.x share many similar capabilities but they should not be thought of as
Support Vector Regression (SVR) using linear and non-linear kernels¶. Toy example of 1D regression using linear, polynomial and RBF kernels.
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Spectral Python (SPy) is a pure Python module for processing hyperspectral image data. It has functions for reading, displaying, manipulating, and classifying hyperspectral imagery. It can be used interactively from the Python command prompt or via Python scripts. SPy is free, open source software distributed under the GNU General Public License. Jun 25, 2017 · Maybe multibandread() should work with .mat files, but, for whatever reason, it doesn't. You can send in a request for enhancement. My guess is that .mat files can be virtually anything under the sun while multibandread() is for the very specific use of reading in imagery that has multiple channels.
220 Band Hyperspectral Image: June 12, 1992 AVIRIS image Indian Pine Test Site 3 (2 x 2 mile portion of Northwest Tippecanoe County, Indiana) This data set is available from the Purdue University Research Repository (PURR) along with ground reference information including the field notes and photos.
SPATIAL/SPECTRAL IDENTIFICATION OF ENDMEMBERS FROM AVIRIS DATA USING MATHEMATICAL MORPHOLOGY Antonio Plaza,1 Pablo Martínez,1 J. Anthony Gualtieri,2 and Rosa M. Pérez1 1. INTRODUCTION During the last several years, a number of airborne and satellite hyperspectral sensors have been developed or