## 2 Image Processing in Remote Sensing
### Feature Extraction  
Feature extraction involves generating features for selection and classification. These are differentiable features and forwarded towards classification phase. Feature extraction plays a very critical role in deciding the efficiency of image classification process [6]. The various features can be categorized as--general and domain specific ones. General features are those which include color, shape and texture while domain specific features are those pertaining to specific applications such as conceptual features. A feature matching algorithm involving graph theory is proposed in Reference [7]. This technique suffers from non-linear intensity differences. To overcome this, Mutual Information (MI) based similarity measure has been used for feature matching. But it has high computational complexity. Some robust descriptors such as Local Self-Similarity (LSS) have also been proposed which are much easier than the MI based method. To overcome the problem of quality of feature distribution, Uniform Robust (UR) Scale Invariant Feature Transform (SIFT) based algorithms is used. Recall is the ratio of correct match to the total match whereas precision is the ratio of the correct match to the sum of correct and false match. Table 2 shows the different feature extraction methods and their performance measures. Local feature extraction based on particular filter designs were used mostly for extracting features such as roads which were slowly replaced by boundary detection methods due to its better performance. But Machine Learning is the most prominent technique in use today. A framework combining machine learning and Euclidean distance is used for feature extraction [9]. It utilizes the satellite image as input and extracts features such as river. The main goal is to identify the feature blocks from the image by applying machine learning. Here, REPTree is utilized to develop trees by taking multiple iterations. Saliency analysis effectively suppresses the background information such that the features are extracted without any unwanted features in the extracted image. In multispectral images, k-means clustering is employed to get the region of interest and saliency maps are obtained for the same. The same procedure is repeated for the panchromatic images using co-occurrence histogram thus improving the saliency of the selected regions and the saliency maps are determined. Now a fusion method is used so as to combine the saliency maps. The fusion technique is such that the unwanted \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Test Images** & **Feature Extracted** & **Performance Metrics** \\ \hline
1. Uniform Competency Feature Extraction [8] & Synthetic Images: ETM+, World View 2, Real Images: SOT4, SPOT6, Quick Bird, GeoEyel, ASTER & Rotation and scale invariant Local features & Recall, Precision, Root Mean Square Error \\ \hline
2. RepTree, Machine Learning and Euclidean distance [9] & Envisat images & Continues features such as roads, rivers & Efficiency and processing time \\ \hline
3. Multi image saliency analysis [10] & SPOT 5 images & ROI extraction such as clouds & Recall = 0.8, precision = 0.75, F-measure = 0.75 \\ \hline
4. Digital Surface Models [11] & ZY-3 satellite images & Pixel and feature level extraction of urban scenes & Overall accuracy = 96\%, kappa coefficient = 0.862 \\ \hline
5. Reversible jump Markov chain Monte Carlo sampler [12] & Synthetic image & Extraction of rivers, channels and roads & Completeness = 98.8, correctness = 94\% and quality = 92.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Feature extraction and their performance metrics. background information is suppressed and the region of interest is enhanced to improve the image quality. A method to classify urban area on the basis of angular properties is used as part of high resolution images [11]. Three distinct Angular Difference Features (ADFs) are used to understand the scene at pixel based, feature based levels.