Partly Uncoupled Siamese Model for Change Detection from Heterogeneous Remote Sensing Imagery

This paper addresses the problematic of detecting changes in bitemporal heterogeneous remote sensing image pairs. In different disciplines, multimodality is the key solution for performance enhancement in a collaborative sensing context. Particularly, in remote sensing imagery there is still a research gap to fill with the multiplication of sensors, along with data sharing capabilities, and multitemporal data availability. This study is aiming to explore the multimodality in a multi-temporal set-up for a better understanding of the collaborative sensor wide information completion; we propose a pairwise learning approach consisting on a pseudo-Siamese network architecture based on two partly uncoupled parallel network streams. Each stream represents itself a Convolutional Neural Network (CNN) that encodes the input patches. The overall Change Detector (CD) model includes a fusion stage that concatenates the two encodings in a single multimodal feature representation which is then reduced to a lower dimension using fully connected layers and finally a loss function based on the binary cross entropy is used as a decision layer. The proposed pseudo-Siamese pairwise learning architecture allows to the CD model to capture the spatial and the temporal dependencies between multimodal input image pairs. The model processes the two multimodal input patches at one-time under different spatial resolutions. The evaluation performances on different real multimodal datasets reflecting a mixture of CD conditions with different spatial resolutions, confirm the effectiveness of the proposed CD architecture.