## 2 Image Processing in Remote Sensing
### Image Fusion  
Image fusion involves combining two or more images to form a new image. With the advancement of multiresolution analysis, it is possible to extract high-frequency components and then inject into multispectral image [24; 25]. The wavelet transform is commonly used for pan-sharpening. Apart from the wavelet transform, the other prominent frequency filtering methods used are High Pass Filter Additive Method (HPFA) [26], High-Frequency-Addition Method (HFA), High Frequency Modulation Method (HFM) [27]. Another category is image fusion using statistical methods such as Local Mean Matching (LMM) [28], Local Mean and Variance Matching (LMVM) [29], Regression variable substitution (RVS) and Local Correlation Modeling (LCM) [30]. Image fusion can be broadly classified as probabilistic fusion, statistical fusion, unmixing based fusion, semiphysical fusion and hybrid fusion [31]. Probabilistic fusion involves fusion with probability based approach such as Bayesian and maximum likelihood method. The main application of using this method is to analyze the statistical relationship between images containing varying features. A Bayesian method to fuse multispectral and panchromatic images is mainly used in pan sharpening to enhance the spatial resolution of the image [32]. Bayesian fusion is also applied to predict the air pollutant concentrations [33] and fusion of Synthetic Aperture Radar (SAR) images for flood detection. Statistical fusion involves statistical relation between pixels. This method is based on pixel based approach and spatial and temporal adaptive reflectance fusion method (STARFM) is the most prominent among them. This fusion produces an output image with high spatial and temporal resolution. It is applied to flood mapping and to monitor water quality [34]. Unmixing based fusion is mainly applied to enhance the spatial resolution of coarse images. Spatial and Temporal Reflectance Unmixing Model (STRUM) is one technique used in applications involving surface reflectance such as cloud masking and gap filling. Semiphysical fusion involves models with physical structures for image fusion. These methods can accommodate temporal changes due to surface variations. It is used in applications such as ocean color observations and atmospherically correct the satellite images. Hybrid fusion involves integrating different fusion techniques improve fusion accuracy. There are some geometric analysis tools for image fusion which are based on curvelet, contourlet, wedgelet and Non Subsampled Contourlet Transform (NSCT). In contourlet transform, the shift-invariance is not present. The NSCT can overcome this at the tradeoff of high time cost. Shearlet transform is mathematically similar to wavelet. Analysis of geometric structure is extremely important in satellite images and hence Shift-Invariant Shearlet transform (SIST) is proposed for remote sensing image fusion [35]. The proposed method involves the fusion of the high frequency sub bands based on their similarities. Finally the fused image is obtained by inverse SIST and inverse Entropy Component Analysis (ECA). Quantitative analysis is carried out by computing Spatial Correlation Coefficient (SCC) and mean gradient (G). Higher the value of G, better the quality. From visual analysis it is inferred that spectral distortion is extremely less. A framework that combining the advantages of wavelet transform and sparse representation is another method which is being used presently. First the intensity components in multispectral images is separated using Intensity-Hue-Saturation(IHS) transform and the wavelet transform is applied to generate a multiscale representation which contains the low and high frequency sub-images which are processed using varying techniques depending on the applications. Figure 2 shows the block diagram of image fusion. One of the major limitations in image fusion is the presence of some noise components in the fused image which makes it visually unappealing.