Principal Components of Gradient Distribution for Aerial Images Segmentation

Aerial images segmentation is a principal task in many applications of remote sensing such as natural disaster monitoring, residential area detection and etc. This paper presents a new method for aerial images segmentation. The method can distinct urban terrains from non-urban terrains using a supervised learning algorithm. Extracted feature for image description is based on principal components analysis of gradient distribution. The proposed method tested on several aerial images of Google Earth taken by satellite and results show that it can segment these images with high accuracy and very fast speed