Analysis on change detection techniques for remote sensing applications: A review

Satellite images taken on the earth's surface are analyzed to identify the spatial and temporal changes that have occurred naturally or manmade. Real-time prediction of change provides an understanding related to the land cover, environmental changes, habitat fragmentation, coastal alteration, urban sprawl, etc. In the current study, various digital change detection approaches and their constituent methods are presented. It was found that (i) change vector analysis method provides better accuracy among the algebra-based change detection approach, (ii) discrete wavelet transformation is better among transformation techniques, (iii) considering the artificial neural network and fuzzy-based approaches to analyze the prediction performance over the traditional state-of-the-art approaches, (iv) analyzing the promising outcomes generated by deep learning techniques for difference analysis related to the images captured at a different instance of time. The brief outlines of different change detection approaches are discussed in this study and addressed the need for improvement in the methods that are developed for the detection of a change in the remote sensing community. • Change vector analysis method provides better accuracy among the algebra-based change detection approach. • Discrete wavelet transformation is better among transformation techniques. • Considering the modern approaches to analyze the performance over the traditional state-of-the-art approaches. • Analyzing the time instance-based outcomes generated by deep learning techniques for difference analysis.