Task Distribution Method for Index Classification based on Color Segmentation in Remote Sensing

Abstract

Digital processing of remotely sensed image data has been great importance in recent times. This research work discusses task distribution method in parallel image processing and load balancing under the circumstance of multi-tasks and multi-processors in remote sensing. Task distribution method can speed up computation and improve efficiency and perform larger computations which are not possible on single processor system. The tasks are distributed based on the segmentation of color and the Support Vector Machine (SVM) is used to classify the indices of the input image and intends to design and improve the color segmentation based task distribution method for index classification using machine learning. In the system, the RGB satellite image is used as an input image and the output is the four indices of forest, building, road and land The results of the system are more accurate and less time consumption than non-distributed computing methods. It is implemented in MATLAB platform with parallel computation toolbox because the system can solve computationally and data-intensive tasks using multicore processors and clusters of computer.