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- W4367291503 abstract "Change detection is one of the major applications of remotely sensed data. With the huge amount of satellite data that is available today, a large amount of significant information can be extracted. Several research has been conducted in the area of change detection on the Earth’s surface intending to analyze the change in vegetation area, land cover and land use, forest degradation, etc., and utilize the result to predict the change for the future. A wide range of methods have been developed and new methods are being introduced from time to time. This paper describes the recent and significant approaches used for change detection in satellite images using different types of indexing. The approaches discussed in this paper include the utilization of supervised learning, unsupervised learning, spectral indices, and time series analysis. The results of indexing with respect to a multispectral dataset and the application of machine learning algorithms are also discussed." @default.
- W4367291503 created "2023-04-29" @default.
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- W4367291503 date "2023-01-01" @default.
- W4367291503 modified "2023-09-27" @default.
- W4367291503 title "Change Detection on Earth’s Surface Using Machine Learning: A Survey" @default.
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- W4367291503 doi "https://doi.org/10.1007/978-981-19-7892-0_30" @default.
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