## 2 Image Processing in Remote Sensing
### Change Detection  
Change detection is done to understand the changes in a particular area given that satellite data over that area is available easily [46]. Change detection techniques can be broadly classified as algebra based, transform based, Geographical Information System (GIS) and based on advanced models. Algebra based method uses mathematical operations to find the change in images. Most commonly used techniques involve Image Differencing, Change vector analysis (CVA) and Principal Component Analysis (PCA). Image differencing is mainly used to obtain details about the vegetation growth in a particular area. PCA finds use to study changes in water quality and vegetation types in an area. Transform based models use the transform to find the image changes. One such method is Tasseled Cap Transformation (TCT). TCT is mainly seen to be useful to understand the effect of deforestation in an area. Image differencing captures the changes in tropical areas efficiently while TCT can monitor changes in urban area. The change detection techniques are explained in details in Reference [47]. GIS gives a base for integrating and analyzing image data. It can integrate past and current image maps for classification and change detection. It has helped in finding the image changes. A change detection method involving GIS and remote sensing data for vegetation cover change analysis is presented in Reference [48]. Land cover change detection involving GIS is presented in Reference [49]. Advanced models for change detection involve spectral mixture and reflectance models. Here the reflectance value in the image is converted to physical parameter. From the physical parameters, information concerning remote sensing is extracted. Temporal unmixing for vegetation cover analysis employing spectral mixture model is proposed in Reference [50]. Digital surface and elevation models are digital representation of Earth's surface. DSM is used in urban planning and vegetation analysis.AN urban change detection using DSM is proposed in Reference [51]. Maximum likelihood supervised classification technique was initially done to get separate classes of each land cover type [52]. After ensuring that classified groups are indeed correct, image differencing, statistical change detection using a probability matrix is used to differentiate between the changes over the past and present images in the area. The maximum likelihood finds the different land types using a combination of past experience, the analysis of previous works and the satellite image maps. Change detection methods have the ability to predict the changes in time series images [53]. One such technique uses the Breaks For Additive Seasonal and Trend or BFAST method to find many changes in time series images by getting the magnitude and direction of change [54]. The two main parameters evaluated are mean and variance of the data to calculate the probability that a pixel belongs to a particular class. Main disadvantages of maximum likelihood is that the pixels remain misinterpreted and is placed in another class to which it does not belong. So the overall efficiency of the process can get reduced. A method called the Multi-Step Image Matching (MSIM) utilizes fast clustering, image detection and boundary matching for detecting the changes in disaster prone areas [55]. A method is developed to detect the spatial and temporal changes in a satellite image using a graph based method [56]. Usually classifier techniques are mainly used in these applications. But they cannot provide enough information regarding the temporal changes in the image. The graph based technique uses object based analysis. The quantitative analysis is done through the calculation of correlation coefficient.