Publication

Abstract : Increasing demands for unmanned systems and the availability of high resolution satellite images have been promoting researchers to contribute innovations to increase the robustness and efficiency of the optimal path planning. An effectively classified satellite image and a robust path planning strategy are highly desirable in finding an optimal path. In this paper, satellite images from ISPRS are classified to identify the traversable areas using a Deep Convolutional Encoder-Decoder architecture-Seg Net and cost map is generated. Using the cost map, Modified Gain based Ant Colony optimization(MGACO) is introduced to find an energy efficient path. The path is finally smoothened using Bezier Spline approximation. MGACO has been compared with up-to-date algorithms and results outperform existing methods in terms of run time and length of the path.