## 2 Image Processing in Remote Sensing
### Image Enhancement  
The Satellite images comprise of low brightness level. This calls for the importance of image enhancement with the preservation of important details without loss of information. Contrast is an important parameter considered on subjectively evaluating an image in terms of its quality. Contrast, from human perception, is what differentiates object-to-object with background. In other words, it is the color and brightness difference between the objects and background. Many algorithms have been designed and developed to accomplish contrast enhancement and solve various brightness related problems in image processing. Also it is one of the primary steps followed before proceeding with any of the other image processing techniques such as segmentation, object identification and so forth. Image enhancement techniques categorized as spatial and frequency domain methods [1]. Histogram Equalization is one of the widely used image enhancement methods which can be performed on the complete image or some parts in it. It can enhance the overall image quality. But HE has the limitation that it cannot retain the average intensity in an image. Several modifications were developed over the HE such as Bi-Histogram Equalization (BHE), Recursive Mean Separate HE (RMSHE) and so forth. The enhancement methods and their evaluation metrics are shown in Table 1.  
There were some unnatural occurrences in the resultant image after RMSHE which could be avoided using another method which is called the Histogram Planting (HP) [2]. It is a distribution based algorithm and it was seen to provide a reliable image enhancement technique. There are mainly two types of distribution techniques involved which are equality-spaced distribution (HP-ESD) and proportionality-spaced distribution (HP-PSD). Even though all the variations of HP show some acceptable level of enhancement, there are some unwanted artifacts introduced. To avoid these artifacts, a modification of the differential evolution algorithm combined with the Cuckoo Search (CS) algorithm for image enhancement is proposed [3]. From visual observation of the obtained results, the algorithm could clearly differentiate the minor details in the image without the presence of the artifacts. Also the average intensity of the image was also preserved. The main aim was to develop a transformation which can map intensity values to a new set of values effectively. Here the transform is such that maps the intensity by local feature extraction in the image by applying a sliding window whose size is predetermined. But since such a local feature extraction takes a lot of time, the Modified Differential Evolution (MDE) algorithm utilizes combination of global and local feature extractions. A technique for integrating multi-resolution satellite images from multiple sources is used for improving accuracy by avoiding Ground Control Points (GCPs) [5]. Here not all the rational polynomial coefficients are utilized. The tie points identified from the image are manually determined. The analysis is done by determining the root mean square error value and compared with different multi resolution images from different sensors. The future scope of the \begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{1}{c}{**Method**} & \multicolumn{1}{c}{**Test Images**} & \multicolumn{1}{c}{**Performance Metrics**} \\ \hline
1. Histogram Planting [2] & Phobos Images of aircraft & Mean, Average Information \\ \hline
2. Modified differential evolution [3] & Images from NASA, Satpalda Geospatial Services and Satellite Imaging Corp. & Peak Signal to Noise Ratio (_PSNR_), Mean Square Error (_MSE_), Universal Quality Index (UQI), Normalized Absolute Error (NAE), Structural Contrast-Quality Index (SC-QI): \\ \hline
3. Fractional Differential (FD) & Infrared Images & Average brightness (B), Average contrast (V), Average discrete information content (or entropy, E), Sharpness (S), Colorfulness (C), Correlation (R), Energy (E) and Homogeneity (M) \\ \hline \hline \end{tabular}
\end{table}