Optimal Synthetic Aperture Radar Image Detection Essay -- Technology

Introduction The Synthetic Aperture Radar (SAR) is a microwave active imagery system that has been largely used due to its possibility 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 texture that has an undesired multiplicative speckled 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 applied to SAR imagery classification [2][3][4][5]. Artificial Neural Network (ANN) algorithms have been increasingly applied to remote sensing for image classification in the last years [6][7][8][9]. SAR images have found many applications in the field of Automatic Target Recognition (ATR). Target detection is a signal processing problem whereby one attempts to detect a stationary target embedded in background clutter while minimizing the false alarm probability. The rapid increase of ANN applications in remote sensing imagery classification is mainly due to their ability to perform equally or more accurately than other classification techniques [10]. In a general way, the major advantages of the neural network method over traditional classifiers are: †¢ Easy adaptation to different 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 find the best nonlinear function, in the optimal case, between the input and the output data without any constraint of linearity or pre-specified nonl... ...e Galinhas, November 2002. 7. J.A. Benediktsson, P.H. Swain, O.K. Ersoy, â€Å"Neural Network approaches versus statistical methods in classification of multisource remote sensing data†, IEEE Transactions on Geoscience and. Remote Sensing, v.28, n.4, p.540-552, 1990. 8. H. Bischof, W. Schneider, A.J. Pinz, â€Å"Multispectral classification of landsat-images using neural networks†, IEEE Transactions 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, â€Å"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, â€Å"Classification of multifrequency polarimetric SAR imagery using a dynamic learning neural network†, IEEE Trans. Geoscience and Remote Sensing, v.34, n.3, p.814-820, 1996.