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


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.
Como Citar

Selecione um Formato
VINHOLI, João Gabriel; SILVA, Danilo; MACHADO, Renato; PETTERSSON, Mats I.. CNN-Based Change Detection Algorithm for Wavelength-Resolution SAR Images. In: CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 33. , 2020, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 493-497.