Land use land cover change detection by different supervised classifiers on LISS-III temporal datasets

2017 1st International Conference on Intelligent Systems and Information Management (ICISIM) Pub Date : 2017-10-01 DOI:10.1109/ICISIM.2017.8122150

Ajay D. Nagne, Rajesh K. Dhumal, Amol D. Vibhute, S. Gaikwad, K. Kale, S. Mehrotra

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引用次数: 3

Abstract

The objective of this paper is to report the study carried out to assess and evaluate changes in Land-Use Land-Cover(LULC) at the region of Aurangabad Municipal Corporation (AMC) for the year 2009 and 2015 using multispectral images acquired from remotely sensed Linear-Imaging-Self-Scanning-Sensor-HI(LISS-HI). The area was categorized into six types, viz. Residential(R), Vegetation(V), Water_Body(W), Rock(Ro), Barren Land(B) and Fallow_Land(F). Four different types of supervised classifiers have been used and it was found the Maximum Likelihood classifier has provided satisfactory and reliable results. The overall accuracy with the classifier was found to be 83% and 93% with Kappa Coefficient 0.78 and 0.90 for the year 2009 and 2015, respectively. The residential area was found to be increased by 1.35% whereas area related to Water Body, Vegetation and Fallow Land have decreased by 0.83%, 2.59% and 18.43% respectively. The areas for Rock remain same, as it was reserved. The area covered by Barren Land increased by 20.44%. The results are of significant for planning and management of AMC.

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基于LISS-III时间数据集的不同监督分类器土地利用土地覆盖变化检测

本文的目的是报告利用遥感线性成像-自扫描-传感器- hi (lss - hi)获取的多光谱图像对奥兰加巴德市政公司(AMC)地区2009年和2015年土地利用-土地覆盖(LULC)变化进行评估和评价的研究。该地区分为6类，即住宅(R)、植被(V)、水体(W)、岩石(Ro)、荒地(B)和休耕地(F)。使用了四种不同类型的监督分类器，发现最大似然分类器提供了令人满意和可靠的结果。2009年和2015年，该分类器的总体准确率分别为83%和93%，Kappa系数分别为0.78和0.90。住区面积增加了1.35%，而水体、植被和休耕地面积分别减少了0.83%、2.59%和18.43%。岩石的区域保持不变，因为它是保留的。荒地面积增加20.44%。研究结果对AMC的规划和管理具有重要意义。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2017 1st International Conference on Intelligent Systems and Information Management (ICISIM)

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