Artificial Bee Colony Algorithm for Classification of Semi-urban LU/LC Features Using High-Resolution Satellite Data

Attempts to classify high-resolution satellite data with conventional classifier show limited success since the traditional-per-pixel classifiers examine only the spectral variance ignoring the spatial distribution of the pixels corresponding to the land use/land cover classes. The work is carried out in two stages on panchromatic sharpened IRS P-6 LISS-IV (2.5 m) multispectral (MS) imagery of the year 2014 of Mangalore coastal zone along the west coast of Karnataka state of India. In the first stage, in order to overcome the limitations experienced in the parametric and nonparametric classifications, the swarm intelligence optimisation technique based on Artificial Bee Colony (ABC) algorithm has been studied for twelve land cover classes that are mapped. In the second stage, to bring out a greater separability between the spectrally overlapping classes, a texture-based image classification approach has been introduced and a methodology is developed to determine the optimal window size, interpixel distance and the best combinations of texture bands in multispectral data. The five texture measures, viz. entropy (ENT), angular second moment (ASM), contrast (CON), MEAN and homogeneity (Hmg) derived from the grey-level co-occurrence matrix (GLCM), are investigated in the study. The major observations and contributions of this work are as follows: in the first stage, the image classifier employing the ABC algorithm exhibits higher classification accuracy when compared with maximum likelihood classifier. In the second stage, the results show that combining textural features and spectral bands in classification approach has proven very useful in delineating the spectrally overlapping classes, particularly at higher class hierarchy level.