A Framework for Automatic and Unsupervised Detection of Multiple Changes in Multitemporal Images

• Published in: IEEE transactions on geoscience and remote sensing Vol. 50; no. 6; pp. 2196 - 2212
• Main Authors: Bovolo, Francesca, Marchesi, S., Bruzzone, L.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.06.2012; Institute of Electrical and Electronics Engineers
• Subjects: Accuracy; Applied geophysics; Bayes decision rule; change detection (CD); change vector analysis (CVA); Data mining; Earth sciences; Earth, ocean, space; Exact sciences and technology; Feature extraction; Geologic measurements; Image coding; Internal geophysics; low-dimensional representation; multiple changes; multitemporal images; Remote sensing; thresholding procedure; Vectors; ground truth; detection; multispectral remote sensing; multitemporal images; remote sensing; thresholding procedure; Bayes decision rule; change detection (CD); low-dimensional representation; channels; solution; standard samples; change vector analysis (CVA); multiple changes; strategy; frame structure
• DOI: 10.1109/TGRS.2011.2171493
• ISSN: 0196-2892

The detection of multiple changes (i.e., different kinds of change) in multitemporal remote sensing images is a complex problem. When multispectral images having B spectral bands are considered, an effective solution to this problem is to exploit all available spectral channels in the framework of supervised or partially supervised approaches. However, in many real applications, it is difficult/impossible to collect ground truth information for either multitemporal or single-date images. On the opposite, unsupervised methods available in the literature are not effective in handling the full information present in multispectral and multitemporal images. They usually consider a simplified subspace of the original feature space having small dimensionality and, thus, characterized by a possible loss of change information. In this paper, we present a framework for the detection of multiple changes in bitemporal and multispectral remote sensing images that allows one to overcome the limits of standard unsupervised methods. The framework is based on the following: 1) a compressed yet efficient 2-D representation of the change information and 2) a two-step automatic decision strategy. The effectiveness of the proposed approach has been tested on two bitemporal and multispectral data sets having different properties. Results obtained on both data sets confirm the effectiveness of the proposed approach.