**2.5 Segmentation**

Texture is an important characteristic for analyzing many types of images, including natural scenes and medical images. With the unique property of spatial-frequency localization, wavelet functions provide an ideal representation for texture analysis. Experimental evidence on human and mammalian vision support the notion of spatial-frequency analysis that maximizes a simultaneous localization of energy in both spatial and frequency domain.These psychophysical and physiological findings lead to several research works on texture-based segmentation methods based on multi-scale analysis. One important feature of wavelet transform is its ability to provide a representation of the image data in a multiresolution fashion. Such hierarchical decomposition of the image information provides the possibility of analyzing the coarse resolution first, and then sequentially refine the segmentation result at more detailed scales. In general, such practice provides additional robustness to noise and local maxima (Mallat,1989).

Image Denoising Based on Wavelet Analysis for Satellite Imagery 455

corrupt pixels and then to apply filtering on those pixels alone (Trygve &Hakon 1999). One of the main problems with impulse noise detection is that it is difficult to differentiate between an edge and an impulse noise. In the intensity space, both these stand as peaks in their neighborhood. The difference between the center pixel with the minimum and maximum gray value in the filtering window is taken and if greater than a certain threshold, the center pixel is considered as noise. The disadvantage of this method is that the false positive rate is very high and most of the edges also get detected as noise. Coherent processing of synthetic aperture radar (SAR) data makes images susceptible to speckles (Lee,Jukervish 1994). Basically, the speckles are signal-dependent and, therefore, act like multiplicative noise. This report develops a statistical technique to define a noise model, and then successfully applies a local statistics noise filtering algorithm to a set of actual SEASAT SAR images. The smoothed images permit observers to resolve fine detail with an enhanced

The standard algorithm shows very good performance removing the additive noise. In SAR images, on the contrary, the noise is multiplicative. In particular three procedures are there

1. Use of a logarithmic transformation in order to translate the noise from multiplicative to

3. Use of non-symmetric membership functions optimized by using a genetic algorithm. Regarding the first point, the logarithmic transformation allowed to apply the standard

The repeated application of the filtering algorithm permits to reduce the speckle noise granularity without degradation of the sharpness. This is very important for the subsequent

The fundamental objective in image enhancement is to improve or accentuate subsequent processing tasks such as detection or recognition (Chang,2000,2006). Classical image enhancement techniques consider the use of spatial-invariant operators either in the spatial or in the fourier domain. Examples of techniques in the spatial domain are related with the histogram modification by a predetermined transformation as in histogram equalization and stretching. These methods are global in the sense that the pixels are modified in the entire image. However, it is often necessary to perform the enhancement process over small patches of the image. Examples of such techniques include local histogram stretching (in overlapping or non-overlapping windows), smoothing and sharpening . In the fourier domain, most methods are based in supressing low spatial frequencies relative to high spatial frequencies as in homomorphic filtering. Local image enhancement can also be performed by means of a multiscale image representation.Fourier transform based spectral analysis is the dominant analytical tool for frequency domain analysis. However, fourier transform cannot provide information of the spectrum changes with respect to time. Fourier transform assumes the signal as stationary, but PD signal is always non-stationary. To overcome this deficiency, a modified method-short time fourier transform allows

fuzzy filtering algorithm obtaining a significant removal of the multiplicative noise.

edge effect. Several SEASAT SAR images are used for demonstration.

to obtain the speckle noise reduction:

2. Use of repeated applications of filtering algorithm.

recognition of the pattern present in the image.

**3.3 Wavelet denoising in images** 

additive.
