A. Srikrishna

Work place: Department of Information and Technology, RVR JC College of engineering, Chowdavaram, Guntur, Andhra Pradesh, India

E-mail: atlurisrikrishna@yahoo.com


Research Interests: Information-Theoretic Security, Image Processing, Information Security, Computer Vision, Computer systems and computational processes


Dr. A. Srikrishna is professor and head of the Department of Information and Technology at RVR & JC College of engineering, Chowdavaram, Guntur. She has 26 years of teaching experience. She has published more than 35 papers in international journals and conferences. She has received major and minor research project grants from UGC and AICTE. She has authored a book entitled “Parametric Based Morphological Transformation for Contrast Enhancement. Her research interests are in Image processing, Computer vision, Information security, and algorithms

Author Articles
An Efficient Texture Feature Extraction Algorithm for High Resolution Land Cover Remote Sensing Image Classification

By A.V. Kavitha A. Srikrishna Ch. Satyanarayana

DOI: https://doi.org/10.5815/ijigsp.2018.12.03, Pub. Date: 8 Dec. 2018

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.

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