Unsupervised Deep Change Vector Analysis for Multiple-Change Detection in VHR Images

• Published in: IEEE transactions on geoscience and remote sensing Vol. 57; no. 6; pp. 3677 - 3693
• Main Authors: Saha, Sudipan, Bovolo, Francesca, Bruzzone, Lorenzo
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Change detection; Change detection (CD); Computer architecture; Conditional probability; Context modeling; deep change vector analysis (DCVA); deep features; Detection; Feature extraction; Image detection; Image processing; Image segmentation; multitemporal images; Neural networks; Optical imaging; Optical sensors; Pixels; Probability theory; Remote sensing; Satellite imagery; Spaceborne remote sensing; Spatial data; Spatial resolution; Vector analysis; Vectors; very high-resolution images

Change detection (CD) in multitemporal images is an important application of remote sensing. Recent technological evolution provided very high spatial resolution (VHR) multitemporal optical satellite images showing high spatial correlation among pixels and requiring an effective modeling of spatial...