Vegetation Extraction from Free Google Earth Images of Deserts Using a Robust BPNN Approach in HSV Space

The high resolution and span diversity of colored Google Earth images are the main reasons for developing a vegetation extraction mechanism based on BPNN (Back Propagation Neural Networks) that can work efficiently with poor color images. This paper introduces a method based on neural networks that can efficiently recognize vegetation and discriminate its zone from the desert, urban, and roadstreet zones that surround it. Our method utilizes a large number of training images extracted from 10’s of images containing random samples from the same area of Google Earth. We then use the multi-layer perceptron, a type of supervised learning algorithm, to learn the relation between vegetation and desert areas based only on color. The proposed method was verified by experimentation on a real Google Images sequence taken for Qatar. Finally justified results were produced. Keywords— Remote sensing, neural networks, BPNN, digital image processing, classification, HSV color space.