CNN-Based Change Detection Algorithm for Wavelength-Resolution SAR Images

• João Gabriel Vinholi UFSC
• Danilo Silva UFSC
• Renato Machado ITA
• Mats I. Pettersson Blekinge Institute of Technology

Resumo

This letter presents an incoherent change detection algorithm (CDA) for wavelength-resolution synthetic aperture radar (SAR) based on convolutional neural networks (CNNs). The proposed CDA includes a segmentation CNN, which localizes potential changes, and a classification CNN, which further analyzes these candidates to classify them as real changes or false alarms. Compared to state-of-the-art solutions on the CARABAS-II data set, the proposed CDA shows a significant improvement in performance, achieving, in a particular setting, a detection probability of 99% at a false alarm rate of 0.0833/km².

Palavras-chave: CARABAS-II, change detection, change detection algorithm (CDA), convolutional neural network (CNN), deep learning, synthetic aperture radar (SAR), ultrawideband (UWB), very high frequency (VHF), wavelength-resolution.