In the context of multi-temporal synthetic aperture radar (SAR) images for earth monitoring applications, one critical issue is the detection of changes occurring after a natural or an-thropic disaster. In this paper, we propose a new similarity measure for automatic change detection based on a divisive normalization image representation. The divisive normalization transform (DNT) has been recognized as a successful methodology to model the perceptual sensitivity of biological vision and a useful image representation that significantly reduces statistical dependence of natural images. In this work, we exploit the fact that the histogram of DNT coefficients within wavelet subbands can often be well fitted with a zero-mean Gaussian density function, which is a one-parameter function that allows efficient change detection of SAR images. The proposed change detector is compared to other recent modelbased approaches. Tests on real data show that our detector outperforms previously suggested methods in terms of the rate of false alarm rate and the total error rate.

The Canny edge detector is one of the most widely used edge detection algorithms due to its superior performance. Unfortunately, not only is it computationally more intensive as compared with other edge detection algorithms, but it also has a higher latency because it is based on frame-level statistics. In this paper, we propose a mechanism to implement the Canny algorithm at the block level without any loss in edge detection performance compared with the original frame-level Canny algorithm. Directly applying the original Canny algorithm at the block-level leads to excessive edges in smooth regions and to loss of significant edges in high-detailed regions since the original Canny computes the high and low thresholds based on the frame-level statistics. To solve this problem, we present a distributed Canny edge detection algorithm that adaptively computes the edge detection thresholds based on the block type and the local distribution of the gradients in the image block. In addition, the new algorithm uses a nonuniform gradient magnitude histogram to compute block-based hysteresis thresholds. The resulting block-based algorithm has a significantly reduced latency and can be easily integrated with other block-based image codecs. It is capable of supporting fast edge detection of images and videos with high resolutions, including full-HD since the latency is now a function of the block size instead of the frame size. In addition, quantitative conformance evaluations and subjective tests show that the edge detection performance of the proposed algorithm is better than the original frame-based algorithm, especially when noise is present in the images. Finally, this algorithm is implemented using a 32 computing engine architecture and is synthesized on the Xilinx Virtex-5 FPGA. The synthesized architecture takes only 0.721 ms (including the SRAM READ/WRITE time and the computation time) to detect edges of 512 × 512 images in the USC SIPI database when clocked at 100 MHz and is faster than existing FPGA and GPU implementations.

In the context of multi-temporal synthetic aperture radar (SAR) images for earth monitoring applications, one critical issue is the detection of changes occurring after a natural or anthropic disaster. In this paper, we propose a new similarity measure for automatic change detection using a pair of SAR images acquired at different dates. This measure is based on the evolution of the local statistics of the image between two dates. The local statistics are modeled as a Gaussian Mixture Model (GMM), which approximates the probability density function in the neighborhood of each pixel in the image. The degree of evolution of the local statistics is measured using the Kullback-Leibler (KL) divergence. One analytical expression for approximating the KL divergence between GMMs is given and is compared with the Monte Carlo sampling method. The proposed change detector is compared to the classical mean ratio detector and also to other recent model-based approaches. Tests on the real data show that our detector outperforms previously suggested methods in terms of the rate of missed detections and the total error rates.