## 2 Image Processing in Remote Sensing
### Image Segmentation  
Image segmentation involves division of the image into different segments such that objects and boundaries can be located [13]. Cuckoo search (CS) algorithm is applied for color image segmentation. A modification of the CS method using [PERSON]'s method is suggested in Reference [14]. This method uses Levy flight in CS method. Most commonly applied heuristic algorithms such as Particle Swarm Optimization (PSO), Artificial Bee Colony optimization (ABC) and CS suffers from the limitation of the exponential increase in execution time with the increasing number of thresholds for segmentation. The main difference from the other models is that it uses [PERSON]'s method to form stable random numbers. This work can be extended to incorporate multi-level threshold for better segmentation. Remote sensing images being purely illuminated can contain many ambiguous areas. Pixel based clustering finds wide application in such cases. Hopfield Neural Network clustering strategy is one such method. This method gives better result when large number of clusters is used. Table 3 shows various segmentation algorithms and their performance measures. One of the most widely used segmentation technique is the histogram based thresholding approach. Bi-level thresholding involves selection of a single threshold for segmenting into two classes while multilevel thresholding involves selection of more than one thresholds for segmenting into multiple classes [19]. To select the threshold, the method optimizes criteria such as maximum between class variance and minimum classification error [20]. The performance comparison of both these methods suggests that the multilevel thresholding outperforms the bi level thresholding. The classification can be enhanced using a Markov Random Field (MRF) as explained in Reference [15]. The method uses lesser clusters for quantization, clustering and finding the likelihood. The classification performance is evaluated using Random forest Method where it is seen that even if the features are minimum, the classification can be good. MRF finds the local pixel interactions which helps the minimum distance classification. Supervised Random Forest method is further applied on each of the extracted segments. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Test Images** & **Performance Metrics** \\ \hline
1. Cuckoo Search, McCulloch’s & Pleiades satellite images & \begin{tabular}{c} _PSNR_ = 21.9833, _MSE_ = 411.8573, \\ FSIM = 0.9595 \\ \end{tabular} \\ method [14] & \
\begin{tabular}{c} QuickBird bands with four \\ spectral bands \\ \end{tabular} &
\begin{tabular}{c} F-measure = 0.7427, \\ Precision = 0.7170, \\ Recall = 0.7704 \\ \end{tabular} \\ \hline
3. Deep convolutional Neural & \
\begin{tabular}{c} Accuracy = 89.9\%, \\ Confusion matrix \\ \end{tabular} \\ Network [16] & IR images &
\begin{tabular}{c} FSM = 0.66278, _PSNR_ = 13.7037, \\ _MSE_ = 3376.1616, _SSIM_ = 0.92448 \\ \end{tabular} \\ \hline
4. Levy flight firefly algorithm [17] & \
\begin{tabular}{c} Multiband satellite images \\ from NASA \\ \end{tabular} &
\begin{tabular}{c} FSM = 0.66278, _PSNR_ = 13.7037, \\ _MSE_ = 3376.1616, _SSIM_ = 0.92448 \\ \end{tabular} \\ \hline
5. Graph based segmentation, & \begin{tabular}{c} Completeness = 92.5\%, \\ Correctness = 91\%, \\ Quality = 84.7\% \\ \end{tabular} \\ Gabor filter [18] & \
\begin{tabular}{c} Quickbird satellite images \\ \end{tabular} &
\begin{tabular}{c} Completeness = 92.5\%, \\ Correctness = 91\%, \\ Quality = 84.7\% \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Image segmentation methods and their performance metrics. Deep Convolution Neural Network (DCNN) for segmentation in high resolution satellite images is presented in Reference [16]. Here, a novel framework is formed by combining boundary detection with SEGNET encoder. SEGNET is a neural network based on encoder-decoder architecture for multiclass segmentation. It performs pixel wise segmentation on image data.