Extracting Rural Residential Areas from High-Resolution Remote Sensing Images in the Coastal Area of Shandong, China Based on Fast Acquisition of Training Samples and Fully Convoluted Network

Abstract

Automatic extraction of rural residential areas from high-resolution remote sensing images in large regions is a challenging task, because all kinds of background features, such as roads, green houses, and urban areas, must be excluded effectively by an extraction method. For the unsupervised methods of rural residential areas extraction, it is difficult to manually design features which are only sensitive to residential areas. At the same time, the supervised methods utilize training samples to obtain the discrimination between rural residential areas and the background features. However, manual labeling in large regions is tedious and time-consuming. The drawbacks of the existing methods for extracting rural residential areas limit their application in large regions. Therefore, we proposed a novel methodology for extracting rural residential areas in large regions based on fast acquisition of training samples and the fully convoluted network (FCN). A block-based method was proposed to extract rural residential areas rapidly and acquire training samples. Then, the large amount of training samples were used to train the FCN for rural residential area extraction. Finally, all ZY-3 satellite images in in the coastal area of Shandong, China were feed into the FCN, and the extraction result were obtained.