Building Change Detection in VHR SAR Images via Unsupervised Deep Transcoding

• Published in: IEEE transactions on geoscience and remote sensing Vol. 59; no. 3; pp. 1917 - 1929
• Main Authors: Saha, Sudipan, Bovolo, Francesca, Bruzzone, Lorenzo
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Buildings; Change detection; Change detection (CD); deep change vector analysis (DCVA); Detection; Domains; Feature extraction; generative adversarial network (GAN); multitemporal images; Neural networks; Optical imaging; Optical sensors; Radar imaging; Radar polarimetry; remote sensing; SAR (radar); Synthetic aperture radar; synthetic aperture radar (SAR); Training; Transcoding; Transfer learning; Vector analysis; very high-resolution images; Italy

Building change detection (CD), important for its application in urban monitoring, can be performed in near real time by comparing prechange and postchange very-high-spatial-resolution (VHR) synthetic-aperture-radar (SAR) images. However, multitemporal VHR SAR images are complex as they show high sp...