ANALYSIS OF LAND SAT-5 TM IMAGERY FOR EXTRACTING AQUACULTURE FARMS INFORMATION IN THE KOREAN COASTAL WATERS
The objective of the present study is to compare certain conventional satellite image processing techniques with the recently evolved linear spectral unmixing method and to ascertain the best possible technique that can identify and position of aquaculture farms accurately in and around the Younggwa...
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| Autores principales: | , , |
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| Lenguaje: | English |
| Publicado: |
Facultad de Ciencias Naturales y Oceanográficas, Universidad de Concepción
2004
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| Acceso en línea: | http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0717-65382004000300038 |
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| Sumario: | The objective of the present study is to compare certain conventional satellite image processing techniques with the recently evolved linear spectral unmixing method and to ascertain the best possible technique that can identify and position of aquaculture farms accurately in and around the Younggwang coastal region of Korea. Various conventional techniques existed to extract such information are spectral enhancement and classification. However, these techniques performed on the Landsat-TM imagery do not seem to yield accurate information about the aquaculture farms, and instead lead to misinterpretation and misclassification. A large discrepancy between the actual and extracted information results from spectral confusion and inadequate spatial resolution of the sensor, which leads to occurrence of mixture pixels or "mixels", which are known to be the source of errors in the classified image. To over come this problem, more recently evolved methods such as step-by-step iterative partial spectral end-member extraction linear spectral unmixing methods are attempted. Large errors in extraction of aquaculture farms information through the conventional classification techniques are significantly minimized with the step-by-step iterative partial spectral end-member extraction approach and the accuracy of classification is further improved with linear spectral unmixing approach. The aquaculture fraction derived from unmxing of TM image data was validated using NDVI values in absence of field data. NDVI and aquaculture fraction are positively correlated (R² = 0.91), indicating the reliability of the sub-pixel classification |
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