Land Cover Classification of Multispectral Remotely Sensed Data Based On Channel Relative Spatial Patter
M. Christy Rama1, D. S. Mahendran2, T. C. Raja Kumar3
1M. Christy Rama, Research Scholar, Department of Computer Science, Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, Tamil Nadu, India.
2D. S. Mahendran, Principal, Aditanar College of Arts and Science, Tiruchendur, Tamil Nadu, India.
3T. C. Raja Kumar, Associate Professor, Department of Computer Science, St. Xavier’s College, Tirunelveli, Tamil Nadu, India.
Manuscript received on December 14, 2019. | Revised Manuscript received on December 22, 2019. | Manuscript published on January 10, 2020. | PP: 1415-1421 | Volume-9 Issue-3, January 2020. | Retrieval Number: B7230129219/2020©BEIESP | DOI: 10.35940/ijitee.B7230.019320
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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: Land spread grouping of remotely detected pictures includes characterizing the satellite pictures into various land use/land spread classes, for example, water, urban region, crop land, backwoods and so on. To screen the ecological effects. Highlights like shading and surface assume a prevalent job in land spread grouping. Picking an appropriate shading space is a significant issue for shading picture order. The quality of various shading spaces, for example, RGB, HSV, LUV have been coordinated successfully to make sense of the element vector. In this paper, another Channel Relative Spatial Pattern (CRSP) is proposed for separating the surface highlights. The extricated highlights are prepared and tried with Random Forest (RF) classifier. Examinations were directed on IRS LISS IV datasets and the outcomes were assessed dependent on the disarray grid, characterization exactness and Kappa insights. The proposed surface example is additionally contrasted and the (LBP), (LDP) and (LTrP) surface techniques and the precision appraisal results have demonstrated exceptionally encouraging outcomes for the CRSP surface example.
Keywords: Confusion Matrix, Chromaticity, Color Percentile, Entropy, Integrative Co-occurrence Matrix, Random forest.
Scope of the Article: Classification