1Unsupervised Change Detection in Multitemporal Remote Sensing Images

Sicong LIU1, Francesca BOVOLO2, Lorenzo BRUZZONE3, Qian DU4 and Xiaohua TONG1

1Tongji University, Shanghai, China

2Fondazione Bruno Kessler, Trento, Italy

3University of Trento, Italy

4Mississippi State University, Starkville, USA

1.1. Introduction

Remote sensing satellites have a great potential to recurrently monitor the dynamic changes of the Earth’s surface in a wide geographical area, and contribute substantially to our current understanding of the land-cover and land-use changes (Bruzzone and Bovolo 2013; Song et al. 2018; Liu et al. 2019c). Scientifically understanding land changes is also essential for analyzing environmental evolution and anthropic phenomena, especially when studying the global change and its impact on human society. Thanks to the satellite revisit property, both long-term (e.g. yearly) and short-term (e.g. daily) satellite observations produce a huge amount of multitemporal images in the data archive (Liu et al. 2015). Based on the analysis of multitemporal data, land-cover changes can be automatically discovered and detected, where knowledge of changes can also be acquired. This becomes an important and complementary way of optimizing the traditional in situ investigation, which is often very costly and labor-intensive. In particular, in some cases such as in a natural disaster scenario, it is very difficult or even impossible to conduct field investigations. Change detection (CD) ...

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