Development of New Index Based Supervised Algorithm for Separation of Built-Up and River Sand Pixels from Landsat7 Imagery: Comparison of Performance with SVM

While extracting "built-up" pixels from satellite imagery, supervised classification algorithms often misclassify "river sand" pixels as "built-up" ones due to the similarity in their spectral profiles. With the help of the spectral reflectance information in BLUE &...

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Bibliographic Details
Main Authors: Mukherjee, Amritendu, Ramachandran, Parthasarathy
Format: Conference Proceeding
Language:English
Subjects:
Artificial satellites
Built-Up & River Sand Separation
Classification algorithms
Earth
Index based Methodology
Landsat7
Machine Learning
Reflectivity
Rivers
Satellites
Support vector machines
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Summary:While extracting "built-up" pixels from satellite imagery, supervised classification algorithms often misclassify "river sand" pixels as "built-up" ones due to the similarity in their spectral profiles. With the help of the spectral reflectance information in BLUE & GREEN bands of Landsat satellite imagery, this study has introduced a new index BRSSI (Built-Up & River Sand Separation Index) that efficiently reduce the misclassification between these two classes. The results shows that average overall accuracy, F1 score and kappa ( \kappa ) coefficient for the developed index corresponding to selected 3 study regions across India are 0.9763, 0.9767 & 0.9527 respectively.
ISSN:2153-7003
DOI:10.1109/IGARSS46834.2022.9884652