Similarly, for pj(x��j,y��j), the 3D object point Pj(xj,yj,zj) can be obtained.Figure 4.Acquisition of the 3D points, Pi(x
The synthetic aperture radar (SAR) system is a powerful tool for observing the Earth under all weather conditions. In recent years, SAR imaging has been rapidly gaining prominence in applications such as remote sensing, surface surveillance and automatic target recognition. Segmentation of SAR images is a critical preliminary operation in various SAR images processing applications, such as target detection, recognition, and image compression.SAR images characteristically have a particular kind of noise, called speckle, which occurs by random interferences, either constructive or destructive, between electromagnetic waves from different reflections in the imaged area.

This makes SAR segmentation a difficult task, though several different segmentation methods designed specifically for SAR images have been proposed. Three common methods are optical image segmentation after speckle filter, the multiscale method [1�C3], and the neural networks method [4,5].Artificial neural networks (ANNs) are a class of computational architectures that are composed of interconnected, simple processing nodes with weighted interconnections. Neural networks have proven to be a popular tool for knowledge extraction, pattern matching, and classification due to their capability of learning from examples with both linear and nonlinear relationships between the input and output signals.

However, ANNs have limited ability to characterize local features, such as discontinuities in curvature, jumps in value or other edges, so these algorithma are not well suited for speckled SAR images. The wavelet transform, Entinostat on the other hand, is efficient in AV-951 representing and detecting local features in images due to the spatial and frequency localization properties of wavelet bases [6]. With the detection of local features, an object can be easily recognized. Many new algorithms based on wavelet transform have been developed to solve SAR image segmentation problems [7,8]. However, the feature-matching of these algorithms have some shortcomings. In order to ensure the reliability of the matching results, they all require an enormous number of scales to construct the time-frequency features at various scales during the classification process. Each scale corresponds to convolving the signal with a wavelet function; hence a large number of convolutions are needed for these algorithms, which make them computationally inefficient.