The detection of multiple changes (i.e., different kinds of change) in multitemporal remote sensing images is a complex problem. When multispectral images havingBspectral 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.

In this paper a framework for the detection of multiple changes in multitemporal and multispectral remote sensing images is presented. The framework is based on: i) a compressed yet efficient (i.e., nearly lossless) 2-dimensional (2D) representation of the change information; and ii) a 2-step automatic decision strategy. At first, the original BD feature space to be explored for the solution of the change-detection (CD) problem is compressed to a 2D space in which the change information is clearly represented; then, the retrieved 2D space is explored for extracting in an automatic way the different kinds of change, thus generating the CD map. This procedure is conducted by applying a 2-step decision strategy based on the Bayes decision theory. Results obtained on a Landsat-5 and a QuickBird data sets confirm the effectiveness of the proposed approach in both representing the information in the 2D space and generating the CD map.

In this paper a novel method for registration of multitemporal very high geometrical resolution (VHR) remote sensing images is presented. It relies on the extraction of a large set of control points (CPs) used for the estimation of a disparity map exploited for the registration process. CPs are automatically identified in both the images through the estimation and analysis of the distribution of registration noise (RN) and used together with an interpolation procedure in the definition of the disparity map. This map contains for each pixel the estimated value of the displacement between the reference and the moving image. The warping of the moving image is performed according to the disparity map by using thin plate spline interpolation. Results obtained on simulated and real VHR data confirm the validity of the proposed technique, which is effective both in identifying CPs and in performing the image alignment.

In this letter, we investigate the effects of pansharpening (PS) applied to multispectral (MS) multitemporal images in change-detection (CD) applications. Although CD maps computed from pansharpened data show an enhanced spatial resolution, they can suffer from errors due to artifacts induced by the fusion process. The rationale of our analysis consists in understanding to which extent such artifacts can affect spatially enhanced CD maps. To this end, a quantitative analysis is performed which is based on a novel strategy that exploits similarity measures to rank PS methods according to their impact on CD performance. Many multiresolution fusion algorithms are considered, and CD results obtained from original MS and from spatially enhanced data are compared.

This paper presents an automatic context-sensitive technique robust to registration noise (RN) for change detection (CD) in multitemporal very high geometrical resolution (VHR) remote sensing images. Exploiting the properties of RN in VHR images, the proposed technique analyzes the distribution of the spectral change vectors (SCVs) computed according to the change vector analysis (CVA) in a quantized polar domain. The method studies the SCVs falling into each quantization cell at different resolution levels (scales) to automatically identify the effects of RN in the polar domain. This information is jointly exploited with the spatial context information contained in the neighborhood of each pixel for generating the final CD map. The spatial context information is modeled through the definition of adaptive regions homogeneous both in spatial and temporal domain (parcels). Experimental results obtained on real VHR remote sensing multitemporal images confirm the effectiveness of the proposed technique.

In this paper Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Kernel Independent Component Analysis (KICA) are studied and compared in the framework of unsupervised change detection in multitemporal remote sensing images. Different architectures for using the above-mentioned techniques in change detection are investigated, and their capability to discriminate true changes from the different sources of noise analyzed. Experimental results obtained on a pair of very high geometrical resolution Quickbird images point out the main properties of the different methods when applied to change detection.

This paper analyzes the problem of change detection in very high resolution (VHR) multitemporal images by studying the effects of residual misregistration [registration noise (RN)] between images acquired on the same geographical area at different times. In particular, according to an experimental analysis driven from a theoretical study, the main effects of RN on VHR images are identified and some important properties are derived and described in a polar framework for change vector analysis. In addition, a technique for an adaptive and unsupervised explicit estimation of the RN distribution in the polar domain is proposed. This technique derives the RN distribution according to both a multiscale analysis of the distribution of spectral change vectors and the Parzen windows method. Experimental results obtained on simulated and real multitemporal data sets confirm the validity of the proposed analysis, the reliability of the derived properties on RN, and the effectiveness of the proposed estimation technique. This technique represents a very promising tool for the definition of change-detection methods for VHR multitemporal images robust to RN.

In this paper an automatic context-sensitive technique robust to registration noise (RN) for change detection on multitemporal very high geometrical resolution (VHR) images is presented. Exploiting the properties of RN in VHR images, the proposed technique analyzes the distribution of the spectral change vectors (SCVs) computed according to the change vector analysis (CVA) in a quantized polar domain. The method studies the SCVs falling into each quantization cell at different resolution levels (scales) to automatically identify the effects of RN in the polar domain. In order to improve the change-detection accuracy also the spatial-contextual information contained in the neighborhood of each pixel is considered through the definition of adaptive regions homogeneous both in spatial and temporal domain (parcels). The final change-detection map is generated considering both the information from the multiscale analysis and the spatial-contextual information. Experimental results obtained on real VHR multitemporal images confirm the effectiveness of the proposed approach.