Optimal Synthetic Aperture Radar Image Detection Essay -- Technology

IntroductionThe Synthetic Aperture Radar (SAR) is a microwave oven active mental imagery system that has been largely used receivable to its conjecture of day-and-night operation in all weather conditions. The SAR system generates images by the coherent processing of the scattering signals this results in a scene cereal that has an undesired multiplicative pieced noise, drastically reduces the ability to distinguish the features of the classes 1. The rejection of the speckle noise motivated many works where ANN algorithms have been employ to SAR imagery classification 2345. Artificial Neural meshing (ANN) algorithms have been increasingly applied to remote sensing for image classification in the last years 6789.SAR images have found many applications in the field of Automatic fall guy Recognition (ATR). Target detection is a signal processing puzzle whereby one attempts to detect a stationary target embedded in background clutter while minimizing the false alarm probabilit y. The rapid cast up of ANN applications in remote sensing imagery classification is mainly due to their ability to perform equally or more accurately than otherwise classification techniques 10. In a general way, the major advantages of the neural mesh topology method over traditional classifiers are Easy adaptation to divergent types of data and input configuration, Simple incorporation of ancillary data sources, as textural information, which can be difficult or impossible with conventional techniques,Does not use or need a priori knowledge about parameters of distributions. ANN algorithms fancy the best nonlinear function, in the optimal case, between the input and the outfit data without any constraint of linearity or pre-specified nonl... ...e Galinhas, November 2002.7. J.A. Benediktsson, P.H. Swain, O.K. Ersoy, Neural Network approaches versus statistical methods in classification of multisource remote sensing data, IEEE proceedings on Geoscience and. Remote Sensing, v.28, n.4, p.540-552, 1990.8. H. Bischof, W. Schneider, A.J. Pinz, Multispectral classification of landsat-images using neural networks, IEEE proceedings on Geoscience and Remote Sensing, v.30, n.3, p.482-490, 1992.9. Y. Hara, R.G. Atkins, S.H. Yueh, R.T. Shin, J.A. Kong, Application of neural networks to radar image classification, IEEE Transactions on Geoscience and Remote Sensing, v.32, n.1, p.100-109, 1994.10. K.S. Chen, W.P. Huang, T.H. Tsay, F. Amar, Classification of multifrequency polarimetric SAR imagery using a combat-ready learning neural network, IEEE Trans. Geoscience and Remote Sensing, v.34, n.3, p.814-820, 1996.