Abstract
Studying the urban environment with its highly dynamic nature is a challenging and difficult task. Use of remote sensing images has given potential scientific background to study such environments. Due to the non homogeneous land cover classes, rapid brightness variations of these classes, mixed pixel effects, shadows, and spectral confusions, identifying the urban land cover accurately through remote sensing has faced lot of difficulties. The Vegetation, Impervious surface and Soil (V-I-S) models has been accepted to parameterize the biophysical composition of the urban environment, further V-I-S models can serve as the foundation for characterizing the urban/near-urban environments universally, hens this model was used in the study to define the urban composition. Spectral Mixture Analysis (SMA) techniques with multispectral remote sensing images have been widely applied in the past to study the urban composition. Lots of technical difficulties were faced due to the spectral variation of each of the V-I-S components due to there brightness differences and the lack of the high spectral resolution of the multispectral imageries. The main objective of this study is to integrate the advantageous of Hyperspectral Images (Hyperion) with its high spectral resolution to account for these brightness variations over the multispectral imageries. The study uses EO1 Hyperion data over the Dehradun city of India. The linear unmixing results show the advantageous of atmospheric correction and de-stripping on Hyperion and also the disadvantageous of smiling effect inherent to the Hyperion image. A supervised endmember selection was used for the study to account for the linear unmixing and the result were validated with respect to Maximum Likelihood Classification (MLC) result with the use of IKONOS multispectral image. The high correlation in the range of 0.7 for Vegetation and Impervious classes, between the reference images and the Hyperion Linear Unmixing results shows the ability of Hyperspectral images to account for the urban composition.
| Original language | English |
|---|---|
| Pages | 610-618 |
| Number of pages | 9 |
| Publication status | Published - 1 Dec 2008 |
| Event | 29th Asian Conference on Remote Sensing 2008, ACRS 2008 - Colombo, Sri Lanka Duration: 10 Nov 2008 → 14 Nov 2008 Conference number: 29 |
Conference
| Conference | 29th Asian Conference on Remote Sensing 2008, ACRS 2008 |
|---|---|
| Abbreviated title | ACRS 2008 |
| Country | Sri Lanka |
| City | Colombo |
| Period | 10/11/08 → 14/11/08 |
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Keywords
- Hyperion image
- Linear unmixing
- Urban composition
- V-I-S model
Cite this
}
Integrating the advantages of Hyperion image with linear spectral unmixing to detect the urban composition. / Welikanna, D. R.; Tolpekin, V.; Kant, Yogesh.
2008. 610-618 Paper presented at 29th Asian Conference on Remote Sensing 2008, ACRS 2008, Colombo, Sri Lanka.Research output: Contribution to conference › Paper › Academic › peer-review
TY - CONF
T1 - Integrating the advantages of Hyperion image with linear spectral unmixing to detect the urban composition
AU - Welikanna, D. R.
AU - Tolpekin, V.
AU - Kant, Yogesh
PY - 2008/12/1
Y1 - 2008/12/1
N2 - Studying the urban environment with its highly dynamic nature is a challenging and difficult task. Use of remote sensing images has given potential scientific background to study such environments. Due to the non homogeneous land cover classes, rapid brightness variations of these classes, mixed pixel effects, shadows, and spectral confusions, identifying the urban land cover accurately through remote sensing has faced lot of difficulties. The Vegetation, Impervious surface and Soil (V-I-S) models has been accepted to parameterize the biophysical composition of the urban environment, further V-I-S models can serve as the foundation for characterizing the urban/near-urban environments universally, hens this model was used in the study to define the urban composition. Spectral Mixture Analysis (SMA) techniques with multispectral remote sensing images have been widely applied in the past to study the urban composition. Lots of technical difficulties were faced due to the spectral variation of each of the V-I-S components due to there brightness differences and the lack of the high spectral resolution of the multispectral imageries. The main objective of this study is to integrate the advantageous of Hyperspectral Images (Hyperion) with its high spectral resolution to account for these brightness variations over the multispectral imageries. The study uses EO1 Hyperion data over the Dehradun city of India. The linear unmixing results show the advantageous of atmospheric correction and de-stripping on Hyperion and also the disadvantageous of smiling effect inherent to the Hyperion image. A supervised endmember selection was used for the study to account for the linear unmixing and the result were validated with respect to Maximum Likelihood Classification (MLC) result with the use of IKONOS multispectral image. The high correlation in the range of 0.7 for Vegetation and Impervious classes, between the reference images and the Hyperion Linear Unmixing results shows the ability of Hyperspectral images to account for the urban composition.
AB - Studying the urban environment with its highly dynamic nature is a challenging and difficult task. Use of remote sensing images has given potential scientific background to study such environments. Due to the non homogeneous land cover classes, rapid brightness variations of these classes, mixed pixel effects, shadows, and spectral confusions, identifying the urban land cover accurately through remote sensing has faced lot of difficulties. The Vegetation, Impervious surface and Soil (V-I-S) models has been accepted to parameterize the biophysical composition of the urban environment, further V-I-S models can serve as the foundation for characterizing the urban/near-urban environments universally, hens this model was used in the study to define the urban composition. Spectral Mixture Analysis (SMA) techniques with multispectral remote sensing images have been widely applied in the past to study the urban composition. Lots of technical difficulties were faced due to the spectral variation of each of the V-I-S components due to there brightness differences and the lack of the high spectral resolution of the multispectral imageries. The main objective of this study is to integrate the advantageous of Hyperspectral Images (Hyperion) with its high spectral resolution to account for these brightness variations over the multispectral imageries. The study uses EO1 Hyperion data over the Dehradun city of India. The linear unmixing results show the advantageous of atmospheric correction and de-stripping on Hyperion and also the disadvantageous of smiling effect inherent to the Hyperion image. A supervised endmember selection was used for the study to account for the linear unmixing and the result were validated with respect to Maximum Likelihood Classification (MLC) result with the use of IKONOS multispectral image. The high correlation in the range of 0.7 for Vegetation and Impervious classes, between the reference images and the Hyperion Linear Unmixing results shows the ability of Hyperspectral images to account for the urban composition.
KW - Hyperion image
KW - Linear unmixing
KW - Urban composition
KW - V-I-S model
UR - https://ezproxy2.utwente.nl/login?url=https://library.itc.utwente.nl/login/2008/conf/tolpekin_int.pdf
M3 - Paper
SP - 610
EP - 618
ER -