## 2 Image Processing in Remote Sensing
### Image Fusion
A spider optimization based algorithm produces two enhanced images, one with high contrast and the other with high peak signal-to-noise ratio (_PSNR_) image [36]. Table 4 represents the various image fusion techniques and their performance metrics. Figure 2: Image fusion block diagram. SAR images are usually affected by speckle noises. The main advantage of NSCT is the effective edge capturing in the fused image [38]. Multi-dimensional expansion of the image is an added attraction in this method. A high efficiency is obtained on using Maximum A Posteriori estimation for removing the noise components in the SAR image. After denoising, the SAR image is fused with the PAN image using an edge based fusion. The most prevalent limitation in fusion is the failure in capturing the smoothness among different contours. Analyzing the correlation between satellite images is important to attain a high accuracy of fusion. The correlation coefficient is evaluated on actual and synthetic time series images in a pixel-by-pixel basis [41]. Then the spatial and temporal fusion is done. The performance comparison was done using root mean square error calculation for different satellite images. This technique was mainly used for the validation of time series satellite images. It suffers from the drawback that as the land cover type changes, the accuracy of this method is seen to be decreasing. A method for the scene classification by fusion of local and global features in the image is proposed in Reference [42]. The image is differentiated into dense regions and then the k-means clustering method is applied. An image fusion based on multilevel morphological decomposition is proposed which decomposes the images into varying layers with varying morphological component so that the details in the source images are preserved [43]. Image fusion also finds application in radiometric normalization and there are various surface reflectance based fusion approaches. One such method that uses MODIS data to predict the Landsat reflectance for different viewing angle is described in Reference [44]. A fusion approach to understand the landscape changes at spatiotemporal resolutions using Spatial and Temporal Adaptive Reflectance Fusion Models is proposed in Reference [45]. But this method has the limitation of finding changes in sub-pixel ranges. There are many commonly used fusion algorithms. One such algorithm is the Ehler method, which is developed on the Intensity-Hue-Saturation (IHS) transform along with adaptive filtering. It is mainly done to enhance the spatial resolution in multi spectral images. First the spectral and spatial components are separated and then the spatial content is modified for the better enhancement of the images. Following this, Fast Fourier Transform is performed on PAN image. Another one is the wavelet transform fusion algorithm. Here the high frequency components are extracted from the PAN image and combined with the spectral information from the MS image. Then the PAN image is divided into low resolution image. These images are then replaced by the MS bands. Finally inverse wavelet is applied to each of them. University of New Brunswick fusion method uses the least square method to find the relationship of the gray values in PAN, MS and fused images. Some of the evaluation metrics for assessing the performance of image fusion include Root Mean Square Error which finds the difference between the source image and the fused image, Spectral Angle Mapper which finds the spectral angle between source and fused image, Signal to Noise Ratio which