Hybrid bio-inspired techniques for land cover feature extraction: A remote sensing perspective
Recent advances in the theoretical and practical implementations of biogeography have led to the exploration of new bio-inspired techniques which can prove to be the building blocks of hybrid bio-inspired techniques. This aspect was discovered while considering the exploration of bio-inspired intelligence for developing generic optimization algorithms that can be adapted for performing the given land cover feature extraction task at hand. Certain bio-inspired techniques when integrated with the existing optimization techniques can drastically improve their optimization capability hence leading to better feature extraction. In this paper, we propose a generic architectural framework of a hybrid biologically inspired technique that is characterized by its capability to adapt according to the database of expert knowledge for a more efficient, focused and refined feature extraction. Since our hybrid feature extractor possesses intelligence for selective cluster identification for application of either of the constituent techniques which is in turn based on an inefficiency analysis, we term our classifier as the hybrid bio-inspired pattern analysis based intelligent classifier. Our hybrid classifier combines the strengths of the modified BBO Technique for land cover feature extraction with the Hybrid ACO2/PSO Technique for a more refined land cover feature extraction. The algorithm has been tested for for the remote sensing application of land cover feature extraction where we have applied it to the 7-Band carto-set satellite image of size 472546 of the Alwar area in Rajasthan and gives far better feature extraction results than the original biogeography based land cover feature extractor [20] and the other soft computing techniques such as ACO, Hybrid PSO-ACO2, Hybrid ACO-BBO Classifier, Fuzzy sets, Rough-Fuzzy Tie up etc.. The 7-band Alwar Image is a benchmark image for testing the performance of a bio-inspired classifier on multi-spectral satellite images since this image is a complete image in the sense that it contains all the land cover features that we need to extract and hence land cover feature extraction results are demonstrated and compared using this image as the standard image.
aco ant colony optimization bbo biogeography based optimization pso particle swarm optimization mdmc minimum distance to mean classifier mlc maximum likelihood classifier tsp travelling salesman problem liss linear imaging self scanning rs1 radarsat 1 rs2 radarsat 2 dn digital number i maximum immigration rate sar synthetic aperture radar ga genetic algorithm fcm fuzzy c-means rcbbo real coded biogeography based optimization hsi habitat suitability index siv suitability index variables dpso discrete particle swarm optimization nir near infra red mir middle infra red dem digital elevation model e maximum emigration rate