专题制图器
端元
多光谱图像
遥感
随机森林
变更检测
像素
图像融合
专题地图
多光谱模式识别
计算机科学
环境科学
科恩卡帕
人工智能
模式识别(心理学)
卫星图像
地图学
地理
图像(数学)
机器学习
作者
K. Kalaivani,Y. Asnath Victy Phamila,Sakkaravarthi Ramanathan
标识
DOI:10.1016/j.eswa.2023.120072
摘要
The gradual depletion of surface water in major lakes and their impact in the sustainable development of local water resources has been a great challenge. Monitoring surface water and detecting changes in the lake are the main objectives of this study. Landsat Thematic Mapper (TM), Landsat Enhanced Thematic Mapper (ETM+) and Landsat Operational Land Imager (OLI) of 2010, 2000 and 2018 Lake Urmia images acquired from US Geological Survey were used for detecting the changes. Surface water changes are usually identified by extracting the water features from individual time series multispectral images. In this study, a novel change detection framework has been proposed involving pixel level fusion and classification. The spatial frequency based undecimated wavelet transform fusion (UDWT – SF) effectively extracted the spectral information from MS image and spatial information from PAN image of the same scene captured at different time periods. The endmembers of the fused images were selected using pixel purity index endmember extraction algorithm and the abundance estimation by the application of fully constrained least square spectral unmixing algorithm. An efficient sub-pixel classification process is designed by employing the spectral signatures, Normalized Difference Water Index (NDWI) and the abundance estimation generated from the pansharpened image in a random forest classifier. Experimental results indicate that the proposed classifier attained 99.9945% user’s accuracy for water area and 99.9675% producer’s accuracy for changed area. Similarly, the producer’s accuracy of water and changed area are 99.9866% and 99.9868% respectively. The kappa coefficient and the overall accuracy of the proposed sub-pixel random forest classification on multitemporal multispectral image is 0.97 and 99.89% respectively. The lake surface area is computed and it is found that an area of 1369 Sq km has been decreased from the year 2000 to 2010 and 310 Sq km decreased from 2010 to 2018 as per the assessment based on the proposed random forest classifier. Spectral unmixing based random forest classifier (RF-SP) on fused image yields better results in terms of accuracy compared to other classifiers and it is more appropriate for detecting the multitemporal surface water changes.
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