A New Algorithm for Detection of Vegetation Regions in High Resolution Aerial and Satellite Images

Evaluation of vegetation cover by using the remote sensing data can provide enhanced results with less time and expense. In this paper, we propose a new automatic algorithm for detection of vegetation regions in high-resolution satellite/aerial images. It uses only color channels of the image and involves two modeling and evaluation phases. In the modeling phase, after extracting color and texture features for the pixels within a few manually-indicated vegetation areas, a model is obtained based on principle component analysis (PCA). In the evaluation phase, the same color and texture features are primarily computed for every pixel of the specified image. Then, an error value is computed for each pixel which determines the model mismatch score. Finally, by thresholding the error image consisting of all error values, the vegetation regions can be distinguished from the background. Experimental results demonstrated outstanding solution quality for the proposed algorithm (so-called PCAM, standing for PCA-based modeling) compared to a number of counterpart methods.