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Remote sensing images, mathematical morphology, texture features, linear contact distributions, first order statistics, image classification
Remote sensing image classification is very much essential for many socio, economic and environmental applications in the society. They aid in agriculture monitoring, urban planning, forest monitoring, etc. Classification of a remote sensing image is still a challenging problem because of its multifold problems. A new algorithm LCDFOSCA (Linear Contact Distribution First Order Statistics Classification Algorithm) is proposed in this paper to extract the texture features from a Color remote sensing image. This algorithm uses linear contact distributions, mathematical morphology, and first-order statistics to extract the texture features. Later k-means is used to cluster these feature vectors and then classify the image. This algorithm is implemented on NRSC ‘Tirupathi’ area 2.5m, 1m color images and on Google Earth images. The algorithm is evaluated with various measures like the dice coefficient, segmentation accuracy, etc and obtained promising results.
A.V. Kavitha, A. Srikrishna, Ch. Satyanarayana, " An Efficient Texture Feature Extraction Algorithm for High Resolution Land Cover Remote Sensing Image Classification", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.10, No.12, pp. 21-28, 2018. DOI: 10.5815/ijigsp.2018.12.03
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