No abstract

The 2009-2010 Data Fusion Contest organized by the Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society was focused on the detection of flooded areas using multi-temporal and multi-modal images. Both high spatial resolution optical and synthetic aperture radar data were provided. The goal was not only to identify the best algorithms (in terms of accuracy), but also to investigate the further improvement derived from decision fusion.This paper presents the four awarded algorithms and the conclusions of the contest, investigating both supervised and unsupervised methods and the use of multi-modal data for flood detection. Interestingly, a simple unsupervised change detection method provided similar accuracy as supervised approaches, and a digital elevation model-based predictive method yielded a comparable projected change detection map without using post-event data.

The analysis of multitemporal very high spatial resolution imagery is too often limited to the sole use of pixel digital numbers which do not accurately describe the observed targets between the various collections due to the effects of changing illumination, viewing geometries, and atmospheric conditions. This paper demonstrates both qualitatively and quantitatively that not only physically based quantities are necessary to consistently and efficiently analyze these data sets but also the angular information of the acquisitions should not be neglected as it can provide unique features on the scenes being analyzed. The data set used is composed of 21 images acquired between 2002 and 2009 by QuickBird over the city of Denver, Colorado. The images were collected near the downtown area and include single family houses, skyscrapers, apartment complexes, industrial buildings, roads/highways, urban parks, and bodies of water. Experiments show that atmospheric and geometric properties of the acquisitions substantially affect the pixel values and, more specifically, that the raw counts are significantly correlated to the atmospheric visibility. Results of a 22-class urban land cover experiment show that an improvement of 0.374 in terms of Kappa coefficient can be achieved over the base case of raw pixels when surface reflectance values are combined to the angular decomposition of the time series.

No abstract

No abstract

In this letter, we propose a simple yet effective unsupervised change detection approach for multitemporal synthetic aperture radar images from the perspective of clustering. This approach jointly exploits the robust Gabor wavelet representation and the advanced cascade clustering. First, a log-ratio image is generated from the multitemporal images. Then, to integrate contextual information in the feature extraction process, Gabor wavelets are employed to yield the representation of the log-ratio image at multiple scales and orientations, whose maximum magnitude over all orientations in each scale is concatenated to form the Gabor feature vector. Next, a cascade clustering algorithm is designed in this discriminative feature space by successively combining the first-level fuzzy c-means clustering with the second-level nearest neighbor rule. Finally, the two-level combination of the changed and unchanged results generates the final change map. Experimental results are presented to demonstrate the effectiveness of the proposed approach.Index Terms-Fuzzy c-means (FCM), Gabor wavelets, multitemporal synthetic aperture radar (SAR) images, two-level clustering, unsupervised change detection.

No abstract

Anomalous change detection is an important problem in remote sensing image processing. Detecting not only pervasive but anomalous or extreme changes has many applications for which methodologies are available. This paper introduces a nonlinear extension of a full family of anomalous change detectors based on covariance operators. In particular, this paper focuses on algorithms that utilize Gaussian and elliptically contoured distributions, and extend them to their nonlinear counterparts based on the theory of reproducing kernels in Hilbert spaces. The presented methods generalize their linear counterparts, based on the assumption of either Gaussian or elliptically-contoured distribution. We illustrate the performance of the introduced kernel methods in both pervasive and anomalous change detection problems involving both real and simulated changes in multi and hyperspectral imagery with different resolutions (AVIRIS, Sentinel-2, WorldView-2, Quickbird). A wide range of situations are studied, involving droughts, wildfires, and urbanization in real examples. Excellent performance in terms of detection accuracy compared to linear formulations is achieved, resulting in improved detection accuracy and reduced false alarm rates. Results also reveal that the ellipticallycontoured assumption may be still valid in Hilbert spaces. We provide an implementation of the algorithms as well as a database of natural anomalous changes in real scenarios.