## 2 Image Processing in Remote Sensing
### Image Classification  
Efficient image classification methods are necessary for categorizing the satellite images [66; 67]. Image classification is a method of pattern recognition in which images/pixels are classified based on some similarity measures. It is divided into supervised, unsupervised and post-classification approaches. Supervised method needs the training data to teach the classifier and recognize the instances of necessary details in the image. It is divided into parametric and non-parametric methods where parametric method use algebraic method for assignment to classes. Bayesian and decision tree come under this category. Non-parametric method is used when density function is not present. K-Nearest Neighbor and Logical Regression come under this category [68].Unsupervised method finds group of similar instances with no information about class label. Unsupervised methods can be hierarchical and partition clustering. Image classification of satellite images suffer from two major drawbacks. The first is the issue of mixed pixels and second is the difficulty in handling large amount of data in the satellite images. Random Forest Classifier is popularly used for land use and land cover classification. It is an ensemble learning approach based on a combination of tree predictors. It combines the votes from different trees to decide the final class. A random forest classification of wetland covers is presented in Reference [69]. Geometric and spectral features were extracted from the multi-sensor images were optimized and a random forest model is created for classification. A random forest classifier with optimized parameters for land cover mapping is presented in Reference [67]. Here Genetic algorithm optimizes the number of features selected and decision trees to develop optimal land cover classification. Mixed pixel classification is a significant problem in remote sensing. Spectral unmixing finds the composition of each pixel to extract information which is also referred to as soft classification. Spectral unmixing in image classification to address the mixed pixel problem is proposed in Reference [70]. Here spectral unmixing is employed to find the fractional abundances of various classes. A hybrid unmixing for land cover classification is presented in Reference [71]. Supervised and unsupervised unmixing is employed for multispectral image classification. A Multiple End member Spectral Mixture Analysis (MESMA) for urban land cover analysis is explained in Reference [72]. A single class is described using multiple end members. Radial Basis Function Neural Network (RBFNN) is prominent neural network with many tunable parameters [73]. The main advantage of this method is its immunity to noise signals. A post classification algorithm for those pixels which are wrongly classified is also developed using the Classification Errors Correction (CEC) [74]. In the first stage the adjacent correlated bands are found and are merged so that the number of correlated bands are reduced. This is done using Discrete Wavelet Transform (DWT). Using the pixel averaging the bands are merged. A deep feature learning for satellite image classification is proposed in Reference [75]. Initially the image is separated into different scales and images in each scale are used to train DCNN. The training process is speeded up using spatial pyramid pooling (SPP). The SPP nets use different image scales and they have similar weight parameters. A Bayesian network classifier model is done on the satellite image to obtain the presence of clouds [76]. For the cloud classification problem, a set of attributes corresponding to the cloud type are considered. In order to understand how effectively the classification can perform is analyzed using k-fold cross validation. It uses all the observations available both for learning and evaluation. It is necessary to assess how accurate the applied classification method is. This is done using an integration of fuzzy evaluation and Dempster-Shafer theory is proposed (FSE-DST) in Reference [77].