Unsupervised Change Detection in Multitemporal Remote Sensing Images

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. This chapter focuses on the unsupervised change detection (CD) problem in multitemporal multispectral images. It investigates the spectral–spatial change representation for addressing the important multiclass CD problem. Depending on the purpose of unsupervised CD tasks, two main categories of methods are defined: binary change detection and multiclass change detection. Deep learning-based CD approaches have shown great potential in extracting more high-level deep features, which represents a popular direction in CD research. The chapter introduces a proposed multiscale morphological compressed change vector analysis method. Owing to the automatic and unsupervised nature, unsupervised CD always represents a very interesting and important CD research and application frontier.