## 2 Image Processing in Remote Sensing
### Image Feature Detection  
There has been an increase in the satellite images which helps to understand and analyze various applications. Feature recognition is gaining importance with the advancement in deep learning. Table 6 represents the different feature detection methods and performance metrics. The ability to differentiate features such as buildings, roads, vegetation and so forth is important in areas such as environmental analysis, urban monitoring and finding the disaster affected areas and so on. In order to find correspondences among a set of images, where feature correspondences between two or more images are needed, it is necessary to identify a set of salient points in each image. In a classification task, feature descriptors are matched with all trained image features and the trained image giving maximum correspondence is considered the best match. The use of global and texture features are successful for finding similar images in a database, while the local structure oriented features are considered adequate for classification. Non-linear features such as urban settlements, oil spills and so forth are difficult to be classified and identification of salient points in the no-linear features is essential for efficient classification. Hence feature detection plays a very significant role in image classification and proper detection of features guarantee proper image classification. Edge detection is very important in image processing. Different edge detection techniques used are Preweitt, Laplacian and Laplacian of Gaussian. The main limitation of this method is the fixed edge thickness and difficulty in threshold implementation. An alternate technique is linear time invariant filter which considers edge as a change in the intensity of the grey scale pixel and is computationally effective. Edge detection using fuzzy logic is developed in Reference [78]. For the cloud detection using Digital Elevation Model (DEM), a Digital Surface Model (DSM) is developed through image matching [82]. The developed DSM is compared with the DEM and the elevations are compared to get the seed point of the clouds. The cloudy areas are classified using the information about the image and height. Another important factor which affects the image quality is the presence of jitter in the image. Hence it is necessary to detect them and compensate its presence in the image. A model based on the displacement data is used in the detection of the high altitude jitter in the satellite image [83]. Bidirectional Kalman filter and smoothing filter are used to reduce the effect of the detected jitter and a reimaging model is used to get the corrected image. Fully convolutional network (FCN) has the capability to learn information regarding pixel dependencies [79]. Distinguishing between the formal and informal settlements is done on the basis of morphological characteristics. To detect informal settlements, the spatial information contained in the image need to be extracted. Wavelet transforms are mainly used for the same. An efficient oil spill detection in sea is used by the International Maritime Organization to evaluate the trade routes through sea. A feature selection using machine learning algorithm is employed for this [84]. The SAR images can provide images of oil spills on the surface of the ocean with wide coverage and good clarity. A novel transfer learning method is used for vehicle detection from aerial images [85]. This transfer learning method is based on a super resolution algorithm which learns the sparse coefficients which are highly correlated between the patches that have high and low resolution. A linear SVM based search technique is used to detect the vehicle positions. But the matching performance is very less since it is difficult to select similar image sections. This limitation is overcome using dictionary learning and sparse coding. It creates low-resolution sections with the help of most similar high-resolution sections utilizing shared coefficients obtained through dictionary learning.