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.

分析在地球表面上拍摄的卫星图像，以识别自然或人为发生的时空变化。实时的变化预测提供了与土地覆盖，环境变化，生境破碎化，沿海变化，城市蔓延等有关的理解。在当前的研究中，提出了各种数字变化检测方法及其构成方法。发现：（i）改变矢量分析方法在基于代数的变化检测方法中提供了更好的准确性；（ii）离散小波变换在变换技术中更好；（iii）考虑使用人工神经网络和基于模糊的方法进行分析传统的最先进方法的预测性能，（iv）分析深度学习技术产生的有希望的结果，以便与在不同时间实例捕获的图像相关的差异分析。在本研究中讨论了不同变化检测方法的简要概述，并提出了对开发用于检测遥感社区变化的方法进行改进的需求。