Classification of Land Cover from Remote Sensing Images using Morphological Linear Contact Distributions and Rough Sets.
A.V. Kavitha1, A. Srikrishna2, Ch. Satyanarayana3
1A.V.Kavitha*, Associate professor, Sri. A.B.R. Govt. degree college, Repallle, Guntur(Dt), Andhra Pradesh, India and Research scholar, Jawaharlal Nehru technological university – Kakinada, Kakinada, Andhra Pradesh, India.
2Dr. A. Srikrishna, Professor and HOD, Department of Information and Technology, RVR JC College of engineering, Chowdavaram, Guntur, Andhra Pradesh, India.
3Dr. Ch. Satyanarayana, Professor, Department of computer science, Jawaharlal Nehru technological university – Kakinada, Kakinada, Andhra Pradesh, India.department, Name of the affiliated College or University/Industry, City, Country.
Manuscript received on 15 August 2019. | Revised Manuscript received on 25 August 2019. | Manuscript published on 30 September 2019. | PP: 676-688 | Volume-8 Issue-3 September 2019 | Retrieval Number: B2822078219/19©BEIESP | DOI: 10.35940/ijrte.B2822.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Remote sensing image classification plays an essential role in computer vision and image processing to address the problems in the areas of agriculture, forest monitoring, urban development, environment protection, etc. A lot of literature is available on remote sensing image classification. But, it is still a research task even today because of the multitude of problems. RTBFCA (Rough Texture Based Features Classification Algorithm), a new classification algorithm has been proposed in this paper. This paper aims at classifying the remote sensing images into various cover types using mathematical morphology and rough sets. Morphological texture features (linear contact distributions) along with first order statistics are used to identify the pixels of various classes and the concepts of lower and upper approximations of rough sets are used for clustering the features of the pixels and then are finally classified to display the classified image. The proposed method was tested on Google Earth images and is able to classify even various crops patterns of a land cover image. The algorithm is compared with other algorithms like ”GLCM with rough sets”, ”intensity values with rough sets” and with ”linear contact distributions with rough sets”.
Keywords: Remote Sensing Images, Mathematical Morphology, Rough Sets, Linear Contact Distributions, Classification.
Scope of the Article: Classification