## 2 Image Processing in Remote Sensing
### Image Compression  
The hardware resources available onboard in a satellite is limited and the satellite images are very large, which necessitates the need for satellite image compression [57; 58; 59]. This reduces onboard data storage and the bandwidth required to transmit the image from the satellite to the ground station as there is only a limited time during which the satellite passes over the ground station. Hence, it is an important technique which will reduce the dependence on the onboard resources without compromising the image quality and the information content which is required at the ground station for analysis. Image quality is a matter of concern since the compressed images need to be reconstructed at the ground station without missing relevant information. Table 5 gives the various compression techniques and the performance metrics for each of the techniques.  
Fractal compression methods have shown great potential in improving the compression ratio but they are not much preferred due to their computational complexity and distortions which affect the image quality. Another commonly used method of image compression is the differential method. Here the image pixels are predicted depending on the previous pixel values and then calculating the error in the pixel values. This method is very simple and less complex. But the main drawback is its very low compression ratio. A method of compression based on removing the sub bands in a multispectral image is proposed in Reference [61]. The main problem in multispectral image is based on how to store the different multispectral bands and how these are compressed effectively. The removed sub bands are found using the correlation coefficients between the various bands.  
A lossless compression method where the spatial and spectral domains are decorrelated and the side information of the coefficients computed recursively are removed to improve compression [64]. Due to the huge size of the aurora spectral images, they are not transmitted as a single image but in the form of frames due to the limited bandwidth of the transmission channel. Here a Differential Pulse Code Modulation (DPCM) based scheme is proposed for the lossless compression of the image. Adaptive Spatial Dispersion Clustering (ASDC) is used in the spatial data compression [63]. This method outputs a new data which acts as the input to a spatial prediction method called Fixed Rank Kriging which uses only a subset of the entire data since using the entire data can increase the complexity of computation. In order to enhance compression rates and reduce the complexity of these methods, novel transform based compression method called Discrete Tchebichef transform or DTT is used [65]. It is an orthogonal transform. In this method, the DTT matrix obtained into a single-row elementary matrix such that there are minimum rounding errors.  
Some of the evaluation metrics for analyzing compression include compression ratio which is the ratio of the number of bits in the original image to the number of bits used in the compressed image, mean square error for lossy compression which is mean square error between the source and the reconstructed image and structural similarity index which defines the variation in structure between the source and the distorted image.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Test Images** & **Performance Metrics** \\ \hline
1. DWT-DCT [60] & Landsat 8 images & _PSNR_, Compression Ratio \\ \hline
2. DWT, entropy encoder [61] &
\begin{tabular}{c} ETM+ satellite \\ multispectral images \\ \end{tabular} & _PSNR_, Spectral Angle Mapper(SAM) \\ \hline
3. Content based compression [62] & SAR image & _PSNR_, Mean Absolute Error(MAE) \\ \hline
4. Adaptive dispersion clustering & \
\begin{tabular}{c} AMSR-2 dataset \\ \end{tabular} &
\begin{tabular}{c} Mean Prediction Error Variance \\ Ratio(MPEVR), Root Mean Square \\ Prediction Error(RMSPE) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Image compression methods and their performance metrics.