Object-Based Distinction between Building Shadow and Water in High-Resolution Imagery Using Fuzzy-Rule Classification and Artificial Bee Colony Optimization

Abstract

Due to the high similarity of the spectra of urban water and building shadows, high-resolution satellite imagery often confuses and wrongly classifies these features. To address this problem, we propose an object-based method for distinguishing building shadow from water using an artificial bee colony algorithm. In the method, four spectral ratio bands are first calculated as additional input parameters for improving the accuracy of segmentation results. During the segmentation, a series of statistical factors, such as spectrum, ratio, and sharp features, are calculated to make up for defects in the high-resolution imagery. Finally, we propose a fuzzy-rule-based classifier to generate extraction rules. The classifier is based on artificial bee colony optimization, which employs the geometric mean (G-mean) as fitness function. The proposed method was carried out on two test sites in Xiamen City. The experimental results based on GF-1 satellite date show that, compared with SVM method, the proposed method improved the overall accuracy of extraction by approximately 6% to 15% and the kappa coefficient values by approximately 0.1 to 0.2. The analysis of the extraction rules also proves that the red/NIR band and the length-width ratio band are significantly influenced by the distinction between building shadow and water.