Unsupervised Multiple-Change Detection in VHR Optical Images Using Deep Features

Change Detection (CD) using multi-temporal satellite images is a fundamental application of remote sensing. To effectively capture change information from Very High spatial Resolution (VHR) optical images, spatial context needs to be modelled as VHR images are characterized by high spatial correlation among pixels. We propose a context-sensitive framework for CD in multitemporal VHR images using pre-trained Convolutional-Neural-Network (CNN)-based feature extraction. Such a framework, while unsupervised, can effectively model the spatial relationship among neighbouring pixels in VHR images. A CNN, pre-trained for semantic segmentation, enables us to obtain multi-temporal deep features that are compared pixelwise to identify changed pixels. Changed pixels are further clustered for multiple change detection. Results obtained on multi-temporal datasets of Worldview-2 and Pleiades images demonstrate effectiveness of our approach.