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A Probabilistic Approach to Detect Urban Regions from Remotely Sensed Images Based on Combination of Local Features

Detecting urban regions from very high resolution aerial and satellite images provides very useful results for urban planning, and land use analysis. Since manual detection is very time consuming and prone to errors, automated systems to detection of urban regions from very high resolution aerial and 
satellite images are needed. Unfortunately, diverse characteristics of urban regions, and uncontrolled appearance of remote sensing images (illumination, viewing angle, etc.) increase difficulty to develop automated systems. In order to overcome these difficulties, herein we propose a novel urban region detection method using local features and a probabilistic framework. First, we introduce four different local feature extraction methods. Extracted local feature vectors serve as observations of the probability density function to be estimated. Using a variable kernel density estimation method, we estimate the corresponding probability function. Using modes of the estimated density, as well as other probabilistic properties, we detect urban region boundaries in the image.We also introduce data and decision fusion methods to fuse information coming from different feature extraction methods. Extensive tests on very high resolution grayscale aerial and panchromatic Ikonos satellite images indicate practical usefulness of proposed method to detect urban regions automatically in a robust and fast manner.