| Radar target recognition systems are very important in modern wars. The adaptation of targets classing algorithms to variable environment, the intelligent standards of the systems, are of critical in the field of radar target recognition, even in the field of auto target recognition (ATR). With the development of SAR (synthetic aperture radar) technology, a large amount of images are made by space-borne and airborne SAR, it's important to use ATR technology in SAR images analysis and advance the level of target recognition of high-resolution radar.With the theory of wavelet analysis, the decomposition of the signal is localized. The characteristic of the signal can be analyzed in different levels, conforming to the basic methods of human brains' work style. Wavelet analysis is becoming a useful tool in radar signal processing now.Speckle in SAR images will affect the precision of recognition, should be removed before classing. While traditional filtering algorithms are not efficient to filter the speckle, combining wavelet analysis with other algorithms have the priority in keeping the edge of the objections.Though there are many methods in the field of speckle filtering, it lacks deep study of methods selection with different images and in different ATR missions. In this paper, both speckles filtering in space and wavelet based methods is used to reduce the speckle. Analysis is done to compare the result of filtering. The paper uses enhanced Lee filter, unbiased GMAP algorithm, shrinking of wavelet coefficients algorithm, wavelet analysis with Wiener filter, wavelet analysis with space filter to filter the speckle in four different SAR images, and gives a suggestion for the choice of right filter in different situation. With experimental threshold coefficient and approximate scale relation between sub-images of wavelet detail coefficients, an improved algorithm is used to get a faster speed with more precise threshold.With 2D-wavelet transform, the characters of image objects can be found in different resolution, texture features can be extracted form detail and approximate images of wavelet transformation. It's very useful in SAR images analysis. Dyadic wavelet transform is not shift-invariant, which is always requested in pattern recognition, so new 2D-wavelet transform method is needed. The paper compared the difference of ordinary 2D-wavelet transform and over-complete 2D-wavelet analysis, also studied the difference in characteristic extraction of ordinary, over-complete wavelet transform and 'a trous' method. In this article,vectors are composed of mean gray level of image and texture features in detail images of 2D-wavelet transform without down-sampling. Distances between each two objects' vectors are much larger than that of Seisuke Fukuda's. In the process of comparison, it is found that the new vector used by this paper is suited to be taken as statistic feature of SAR area objects.Artificial neural network (ANN) is always used in image classing. This thesis used BP network, RBF network and SOFM network to analyze SAR area objects, with gray level, average and wavelet analysis based features as the inputs. The precision of the result is high. In both BP and RBF network, the inputs are made up by OWATF (Over-complete wavelet analysis based texture feature) vectors, with small training samples, the result of the later one is much better than that of the previous one.In image classing process, the speed of the algorithms is an important factor. K-means algorithm, FCM, SOFM network, all deal image pixels as the object, which is the main reason of long working time. By introducing histogram to the algorithms, the computing quantity of the new algorithms is about K/(M X N) to the originals'. Accordingly, the amount of needed memory is also reduced. The classing procedure is sped up, and the result can be got in very short time. In the paper, saturation based experienced distance is used in K-Mean algorithm. With this method, color SAR images and scanned film SAR images are analyzed.
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