Diferencia entre revisiones de «Hyperspectral Remote Sensing Scenes»

De Grupo de Inteligencia Computacional (GIC)
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(No se muestran 17 ediciones intermedias de 2 usuarios)
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Collected by: M Graña, MA Veganzons, B Ayerdi
Here you can find information over some public available hyperspectral scenes. All of then are Earth Observation images taken from airbornes or satellites.
Here you can find information over some public available hyperspectral scenes. All of then are Earth Observation images taken from airbornes or satellites.


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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. After removing water absorption and low SNR bands, 176 bands were used for the analysis. Training data were selected using land cover maps derived from color infrared photography provided by the Kennedy Space Center and Landsat Thematic Mapper (TM) imagery. The vegetation classification scheme was developed by KSC personnel in an effort to define functional types that are discernable at the spatial resolution of Landsat and these AVIRIS data. Discrimination of land cover for this environment is difficult due to the similarity of spectral signatures for certain vegetation types. For classification purposes, 13 classes representing the various land cover types that occur in this environment were defined for the site.
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. After removing water absorption and low SNR bands, 176 bands were used for the analysis. Training data were selected using land cover maps derived from color infrared photography provided by the Kennedy Space Center and Landsat Thematic Mapper (TM) imagery. The vegetation classification scheme was developed by KSC personnel in an effort to define functional types that are discernable at the spatial resolution of Landsat and these AVIRIS data. Discrimination of land cover for this environment is difficult due to the similarity of spectral signatures for certain vegetation types. For classification purposes, 13 classes representing the various land cover types that occur in this environment were defined for the site.


* Take a fast look to the data! [http://www.ehu.es/ccwintco/uploads/2/28/KSC.gif]
* Take a fast look to the data! [http://www.ehu.es/ccwintco/uploads/2/28/KSC.gif]
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* [[Corrected version offered by Ilya Kavalerov]]


=== Botswana ===
=== Botswana ===


The NASA EO-1 satellite acquired a sequence of data over the Okavango Delta, Botswana in 2001-2004. The Hyperion sensor on EO-1 acquires data at 30 m pixel resolution over a 7.7 km strip in 242 bands covering the 400-2500 nm portion of the spectrum in 10 nm windows. Preprocessing of the data was performed by the UT Center for Space Research to mitigate the effects of bad detectors, inter-detector miscalibration, and intermittent anomalies. Uncalibrated and noisy bands that cover water absorption features were removed, and the remaining 145 bands were included as candidate features: [10-55, 82-97, 102-119, 134-164, 187-220]. The data analyzed in this study, acquired May 31, 2001, consist of observations from 14 identified classes representing the land cover types in seasonal swamps, occasional swamps, and drier woodlands located in the distal portion of the Delta.
The NASA EO-1 satellite acquired a sequence of data over the Okavango Delta, Botswana in 2001-2004. The Hyperion sensor on EO-1 acquires data at 30 m pixel resolution over a 7.7 km strip in 242 bands covering the 400-2500 nm portion of the spectrum in 10 nm windows. Preprocessing of the data was performed by the UT Center for Space Research to mitigate the effects of bad detectors, inter-detector miscalibration, and intermittent anomalies. Uncalibrated and noisy bands that cover water absorption features were removed, and the remaining 145 bands were included as candidate features: [10-55, 82-97, 102-119, 134-164, 187-220]. The data analyzed in this study, acquired May 31, 2001, consist of observations from 14 identified classes representing the land cover types in seasonal swamps, occasional swamps, and drier woodlands located in the distal portion of the Delta.


* Take a fast look to the data! [http://www.ehu.es/ccwintco/uploads/0/05/Botswana.gif]
* Take a fast look to the data! [http://www.ehu.es/ccwintco/uploads/0/05/Botswana.gif]
* Download MATLAB data file: [http://www.ehu.es/ccwintco/uploads/7/72/Botswana.mat Botswana (78.9 MB)]
* Download MATLAB data file: [http://www.ehu.es/ccwintco/uploads/7/72/Botswana.mat Botswana (78.9 MB)]
* Download MATLAB ground truth file: [http://www.ehu.es/ccwintco/uploads/5/58/Botswana_gt.mat Botswana gt (4.0 kB)]
* Download MATLAB ground truth file: [http://www.ehu.es/ccwintco/uploads/5/58/Botswana_gt.mat Botswana gt (4.0 kB)]
===anomaly detection  ===
[http://xudongkang.weebly.com/data-sets.html Xudong Kang dataset for anomaly detection]

Revisión actual - 19:27 12 jul 2021

Collected by: M Graña, MA Veganzons, B Ayerdi

Here you can find information over some public available hyperspectral scenes. All of then are Earth Observation images taken from airbornes or satellites.

You can find more information about hyperspectral sensors and remote sensing here.

Indian Pines

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. The Indian Pines scene contains two-thirds agriculture, and one-third forest or other natural perennial vegetation. There are two major dual lane highways, a rail line, as well as some low density housing, other built structures, and smaller roads. Since the scene is taken in June some of the crops present, corn, soybeans, are in early stages of growth with less than 5% coverage. The ground truth available is designated into sixteen classes and is not all mutually exclusive. We have also reduced the number of bands to 200 by removing bands covering the region of water absorption: [104-108], [150-163], 220. Indian Pines data are available through Pursue's univeristy MultiSpec site.

Sample band of Indian Pines dataset
Groundtruth of Indian Pines dataset
Groundtruth classes for the Indian Pines scene and their respective samples number
# Class Samples
1 Alfalfa 46
2 Corn-notill 1428
3 Corn-mintill 830
4 Corn 237
5 Grass-pasture 483
6 Grass-trees 730
7 Grass-pasture-mowed 28
8 Hay-windrowed 478
9 Oats 20
10 Soybean-notill 972
11 Soybean-mintill 2455
12 Soybean-clean 593
13 Wheat 205
14 Woods 1265
15 Buildings-Grass-Trees-Drives 386
16 Stone-Steel-Towers 93

Salinas

Salinas scene

This scene was collected by the 224-band AVIRIS sensor over Salinas Valley, California, and is characterized by high spatial resolution (3.7-meter pixels). The area covered comprises 512 lines by 217 samples. As with Indian Pines scene, we discarded the 20 water absorption bands, in this case bands: [108-112], [154-167], 224. This image was available only as at-sensor radiance data. It includes vegetables, bare soils, and vineyard fields. Salinas groundtruth contains 16 classes.

Sample band of Salinas dataset
Groundtruth of Salinas dataset
Groundtruth classes for the Salinas scene and their respective samples number
# Class Samples
1 Brocoli_green_weeds_1 2009
2 Brocoli_green_weeds_2 3726
3 Fallow 1976
4 Fallow_rough_plow 1394
5 Fallow_smooth 2678
6 Stubble 3959
7 Celery 3579
8 Grapes_untrained 11271
9 Soil_vinyard_develop 6203
10 Corn_senesced_green_weeds 3278
11 Lettuce_romaine_4wk 1068
12 Lettuce_romaine_5wk 1927
13 Lettuce_romaine_6wk 916
14 Lettuce_romaine_7wk 1070
15 Vinyard_untrained 7268
16 Vinyard_vertical_trellis 1807

Salinas-A scene

An small subscene of Salinas image, denoted Salinas-A, is usually used too. It comprises 86*83 pixels located within the same scene at [samples, lines] = [591-676, 158-240] and includes six classes.

Sample band of Salinas-A dataset
Groundtruth of Salinas-A dataset
Groundtruth classes for the Salinas-A scene and their respective samples number
# Class Samples
1 Brocoli_green_weeds_1 391
2 Corn_senesced_green_weeds 1343
3 Lettuce_romaine_4wk 616
4 Lettuce_romaine_5wk 1525
5 Lettuce_romaine_6wk 674
6 Lettuce_romaine_7wk 799

Pavia Centre and University

These are two scenes acquired by the ROSIS sensor during a flight campaign over Pavia, nothern Italy. The number of spectral bands is 102 for Pavia Centre and 103 for Pavia University. Pavia Centre is a 1096*1096 pixels image, and Pavia University is 610*610 pixels, but some of the samples in both images contain no information and have to be discarded before the analysis. The geometric resolution is 1.3 meters. Both image groundtruths differenciate 9 classes each. It can be seen the discarded samples in the figures as abroad black strips.

Pavia scenes were provided by Prof. Paolo Gamba from the Telecommunications and Remote Sensing Laboratory, Pavia university (Italy).

Pavia Centre scene

Sample band of Pavia Centre dataset
Groundtruth of Pavia Centre dataset
Groundtruth classes for the Pavia centre scene and their respective samples number
# Class Samples
1 Water 824
2 Trees 820
3 Asphalt 816
4 Self-Blocking Bricks 808
5 Bitumen 808
6 Tiles 1260
7 Shadows 476
8 Meadows 824
9 Bare Soil 820

Pavia University scene

Sample band of Pavia University dataset
Groundtruth of Pavia University dataset
Groundtruth classes for the Pavia University scene and their respective samples number
# Class Samples
1 Asphalt 6631
2 Meadows 18649
3 Gravel 2099
4 Trees 3064
5 Painted metal sheets 1345
6 Bare Soil 5029
7 Bitumen 1330
8 Self-Blocking Bricks 3682
9 Shadows 947

Cuprite

This data sets can be retrieved from AVIRIS NASA site. Among the many datasets available, the .mat archive posted here corresponds to the f970619t01p02_r02_sc03.a.rfl reflectance file.

False greyscale image of Cuprite sample.



Kennedy Space Center (KSC)

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. After removing water absorption and low SNR bands, 176 bands were used for the analysis. Training data were selected using land cover maps derived from color infrared photography provided by the Kennedy Space Center and Landsat Thematic Mapper (TM) imagery. The vegetation classification scheme was developed by KSC personnel in an effort to define functional types that are discernable at the spatial resolution of Landsat and these AVIRIS data. Discrimination of land cover for this environment is difficult due to the similarity of spectral signatures for certain vegetation types. For classification purposes, 13 classes representing the various land cover types that occur in this environment were defined for the site. 



* Corrected version offered by Ilya Kavalerov

Botswana

The NASA EO-1 satellite acquired a sequence of data over the Okavango Delta, Botswana in 2001-2004. The Hyperion sensor on EO-1 acquires data at 30 m pixel resolution over a 7.7 km strip in 242 bands covering the 400-2500 nm portion of the spectrum in 10 nm windows. Preprocessing of the data was performed by the UT Center for Space Research to mitigate the effects of bad detectors, inter-detector miscalibration, and intermittent anomalies. Uncalibrated and noisy bands that cover water absorption features were removed, and the remaining 145 bands were included as candidate features: [10-55, 82-97, 102-119, 134-164, 187-220]. The data analyzed in this study, acquired May 31, 2001, consist of observations from 14 identified classes representing the land cover types in seasonal swamps, occasional swamps, and drier woodlands located in the distal portion of the Delta.


anomaly detection

Xudong Kang dataset for anomaly detection

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