Biogeography based Satellite Image Classification

TL;DR

This paper introduces a modified Biogeography Based Optimization algorithm for satellite image classification, demonstrating its effectiveness in accurately extracting land cover features from inaccessible regions.

Contribution

The paper proposes modifications to the original BBO algorithm to incorporate clustering, improving satellite image classification accuracy.

Findings

High accuracy in land cover feature extraction

Effective classification of satellite images using the modified BBO

Potential for improved land cover mapping in inaccessible areas

Abstract

Biogeography is the study of the geographical distribution of biological organisms. The mindset of the engineer is that we can learn from nature. Biogeography Based Optimization is a burgeoning nature inspired technique to find the optimal solution of the problem. Satellite image classification is an important task because it is the only way we can know about the land cover map of inaccessible areas. Though satellite images have been classified in past by using various techniques, the researchers are always finding alternative strategies for satellite image classification so that they may be prepared to select the most appropriate technique for the feature extraction task in hand. This paper is focused on classification of the satellite image of a particular land cover using the theory of Biogeography based Optimization. The original BBO algorithm does not have the inbuilt property of clustering which is required during image classification. Hence modifications have been proposed to the original algorithm and the modified algorithm is used to classify the satellite image of a given region. The results indicate that highly accurate land cover features can be extracted effectively when the proposed algorithm is used.