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The major issue of concern in change detection process is the accuracy of the algorithm to recover changed and unchanged pixels. The fusion rules presented in the existing methods could not integrate the features accurately which results in more number of false alarms and speckle noise in the output image. This paper proposes an algorithm which fuses two multi-temporal images through proposed set of fusion rules in stationary wavelet transform. In the first step, the source images obtained from log ratio and mean ratio operators are decomposed into three high frequency sub-bands and one low frequency sub-band by stationary wavelet transform. Then, proposed fusion rules for low and high frequency sub-bands are applied on the coefficient maps to get the fused wavelet coefficients map. The fused image is recovered by applying the inverse stationary wavelet transform (ISWT) on the fused coefficient map. Finally, the changed and unchanged areas are classified using Fuzzy c means clustering. The performance of the algorithm is calculated in terms of percentage correct classification (PCC), overall error (OE) and Kappa coefficient (K c ). The qualitative and quantitative results prove that the proposed method offers least error, highest accuracy and Kappa value as compare to its preexistences.

Contrast enhancement is one of the widely used techniques for image enhancement. In this technique, contrast of an image becomes better to make the image more acceptable for well human vision. There are several techniques that can be process for contrast enhancement but the most common one is the histogram equalization (HE) for its simplicity. The HE technique remaps gray levels of image according to probability distribution function (PDF). HE spreads the histogram and extends dynamic range of gray levels to accomplish overall contrast enhancement but the drawbacks are excessive change in brightness, excessive contrast enhancement, washed out appearance, loss of naturalness of an image, loss of image details, not displaying the actual appearance of the image so it is not suitable for consumer electronic applications. This paper shows the study of various histogram modifying techniques to overcome these drawbacks in a greater extend. General TermsHistogram equalization (HE)