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The objective of synthetic aperture radar (SAR) edge detection is the identification of contours across the investigated scene, exploiting SAR complex data. Edge detectors available in the literature exploit singularly amplitude and interferometric phase information, looking for reflectivity or height difference between neighboring pixels, respectively. Recently, more performing detectors based on the joint processing of amplitude and interferometric phase data have been presented. In this paper, we propose a novel approach based on the exploitation of real and imaginary parts of single-look complex acquired data. The technique is developed in the framework of stochastic estimation theory, exploiting Markov random fields. Compared to available edge detectors, the technique proposed in this paper shows useful advantages in terms of model complexity, phase artifact robustness, and scenario applicability. Experimental results on both simulated and real TerraSAR-X and COSMO-SkyMed data show the interesting performances and the overall effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

Anomaly detection has been an important topic in hyperspectral image analysis. This technique is sometimes more preferable than the supervised target detection because it requires no a priori information for the interested materials. Many efforts have been made in this topic; however, they usually suffer from excessive time cost and a high false-positive rate. There are two major problems that lead to such a predicament. First, the construction of the background model and affinity estimation are often overcomplicated. Second, most of these methods have to impose a stringent assumption on the spectrum distribution of background; however, these assumptions cannot hold for all practical situations. Based on this consideration, this paper proposes a novel method allowing for fast yet accurate pixel-level hyperspectral anomaly detection. We claim the following main contributions: construct a high-order 2-D crossing approach to find the regions of rapid change in the spectrum, which runs without any a priori assumption; and design a low-complexity discrimination framework for fast hyperspectral anomaly detection, which can be implemented by a series of filtering operators with linear time cost. Experiments on three different hyperspectral images containing several pixel-level anomalies demonstrate the superiority of the proposed detector compared with the benchmark methods. [ABSTRACT FROM PUBLISHER]

This paper presents a new automatic change detection technique for synthetic aperture radar (SAR) time series, i.e., Method for generalIzed Means Ordered Series Analysis (MIMOSA). The method compares only two different temporal means between the amplitude images, whatever the length of the time series. The method involves three different steps: 1) estimation of the amplitude distribution parameters over the images; 2) computation of the theoretical joint probability density function between the two temporal means; and 3) automatic thresholding according to a given false alarm rate, which is the only change detection parameter. The procedure is executed with a very low computational cost and does not require any spatial speckle filtering. Indeed, the full image resolution is used. Due to the temporal means, the data volume to process is reduced, which is very helpful. Moreover, the two means can be simply updated using the new incoming images only. Thus, the full time series is not processed again. Change detection results between image pairs are presented with the airborne sensor CARABAS-II, using a public data release, and with TerraSAR-X data. In the case of time series, change detection results are illustrated using a TerraSAR-X time series. In every case, the MIMOSA method produces very good results. [ABSTRACT FROM PUBLISHER]