Hybrid Algorithm of Cuckoo Search & Firefly Algorithm for Natural Terrain Feature Extraction

Natural Computing is the field of research that works with computational techniques that deal with natural inspiration. It attempts to understand the world around us in terms of information processing. Digital image classification techniques group pixels to represent land cover topographies. Land cover could possibly be agricultural, forested, urban, and other types of features. Mapping and classification of ordinary vegetation are foremost issues for biodiversity administration and preservation. Satellite data or remotely sensed data with very high spatial resolution are used for classification and study of vegetation, but generally satellite sensors are incomplete to several spectral bands, which is inadequate to classify and identify some natural vegetation formations. The objective is to show favouritism for natural vegetation and to classify the natural vegetation formations from the highly sensed data or satellite image. There are varieties of known satellite image classification techniques but in proposed work, Hybrid method with the combination of Firefly (FFA) and Cuckoo Search method to achieve the better classification rate is used. From simulation of proposed work, we have observed the value of kappa coefficient to be 0.96. After that, we have compared the obtained results with some previous existing methods like Fuzzy set, BBO, PSO, ABC, CS, hybrid Rough/BBO, hybrid Fuzzy/BBO, Hybrid FPAB/BBO, Hybrid ACO/SOFM, Hybrid ACO/BBO, Hybrid ABC/BBO and Hybrid of CS/ACO. We have implemented the hybrid algorithm with the combination of firefly and cuckoo search technique for satellite image classification of natural vegetation trained features for the betterment of results. The main benefit of hybrid method is to train the datasets of satellite image according to the globally best optimised feature. In the classification step, we can classify the image region according to their bands and detects the natural vegetation.