Markov Random Fields (MRF) are a powerful and wide adopted tool in image processing. MRFS together with Bayesian Estimation Theory can be used for detecting edges of man made structures in Synthetic Aperture Radar images. In this paper we present a method developed in the Markovian-Bayesian framework which is particularly suited to work in case of high coherence SAR interferometric images pairs, such as TanDEM-X Mission data. A real case study, consisting of a pair of complex TerraSAR-X images, is presented, showing the performances of the algorithm.
A constant-false-alarm-rate (CFAR) edge detector based on the ratio between pixel values is described. The probability distribution of the image obtained by applying the edge detector is derived. Hence, the decision threshold can be theoretically determined for a given probability of false alarm as a function of the number of looks of the image under study and the size of the processing neighborhood. For a better and finer detection, the edge detector operates along the four usual directions over windows of increasing sizes. A test performed, for a given direction, on a radar image of an agricultural scene shows good agreement with the theoretical study. The operator is compared with the CFAR edge detectors suitable for radar images. >
Berzelius failed to make use of Faraday's electrochemical laws in his laborious determination of equivalent weights.