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Change Detection and Feature Extraction Using High-Resolution Remote Sensing Images


Change detection using high temporal resolution remote sensing satellite data for identifying changes on the Earth’s surface is critical in urban applications, including vacant land site monitoring. Physical ground surveys, for monitoring the vacant site, are a time-consuming process. Results of analysis of satellite data for identifying changes vary, based on the image interpretation skills and satellite data resolution. The application of computer vision tools and libraries for change detection using image interpretation has shown some excellent results. It can be further enhanced by adding machine learning techniques. This study focuses on integration of binary change detection with machine learning techniques for identifying the change detection and for monitoring the vacant sites in an urban area. Edge detection technique coupled with principal component analysis and k-means clustering for generating change map successfully depicts the changes. Change detection results are further enhanced by adding feature type information derived using machine learning–based classifiers. Random forest classifiers are used to classify and identify different land use classes within the urban area: water bodies, cropland, built-up, roads, and bare land. The approach is evaluated on different areas, giving an overall accuracy of 88.2%, precision of 84.8%, and an F1 score of 81.6% for classification. The classification results are integrated with change detection results to identify changes where the bare land is transformed into built-up by identifying buildings/houses. The work will be helpful in urban planning bodies having multiple vacant land sites for monitoring.

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Data Availability

The data that support the findings of this study are available from NRSC but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the NRSC.


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Correspondence to Vinod K. Sharma.

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Sharma, V.K., Luthra, D., Mann, E. et al. Change Detection and Feature Extraction Using High-Resolution Remote Sensing Images. Remote Sens Earth Syst Sci 5, 154–164 (2022).

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  • Remote sensing satellite data
  • Canny edge detection
  • Principal component analysis
  • K-Means clustering
  • Random forest classifier