Unsupervised land use - land cover classification for multispectral images

Abstract

In this paper, land use/land cover classification of multispectral imagery with unsupervised approaches are presented. Primarily, a pixel based recognition algorithm is applied in three stages. At the first stage, water bodies are classified by using the NIR band histogram. At the second stage, combination of several vegetation indices are used to locate vegetation and at the third stage, by using Gabor filter man-made structures are classified and the unclassified fields are left. Followingly in order to increase the success rate, pixel based classification results are combined with meanshift segmentation results and a homogeneity test is applied for each segment. The segments that passed the homogeneity test are classified to corresponding class and for the rest, pixel based results are assigned. Compared to the similar works, this approach gives successful results.