Rural Built-Up Area Extraction from Remote Sensing Images Using Spectral Residual Methods with Embedded Deep Neural Network

Abstract

A rural built-up area is one of the most important features of rural regions. Rapid and accurate extraction of rural built-up areas has great significance to rural planning and urbanization. In this paper, the spectral residual method is embedded into a deep neural network to accurately describe the rural built-up areas from large-scale satellite images. Our proposed method is composed of two processes: coarse localization and fine extraction. Firstly, an improved Faster R-CNN (Regions with Convolutional Neural Network) detector is trained to obtain the coarse localization of the candidate built-up areas, and then the spectral residual method is used to describe the accurate boundary of each built-up area based on the bounding boxes. In the experimental part, we firstly explored the relationship between the sizes of built-up areas and the kernels in the spectral residual method. Then, the comparing experiments demonstrate that our proposed method has better performance in the extraction of rural built-up areas.

A New High-Resolution Rural Built-Up Land Extraction Method Based on Artificial Surface Index with Short-Wave Infrared Downscaling

“…Machine learning algorithms have been widely used to acquire distribution and dynamical changes of built-up areas using medium-to high-resolution images [15][16][17]. In recent years, deep learning techniques have received much attention for building extraction from high-resolution or very-high-resolution (VHR) images [4,18,19]. Deep learning models achieve high accuracy, but due to the requirement of abundant high-quality training data, the demand for computational resources is large and data processing work is heavy [20,21].…”

“…For example, the spectral residual (SR) method was applied to GF-1 satellite images to extract rural residential [47]. Li et al [18] improved the performance of SR on large-scale rural areas by applying the faster R-CNN framework. Wang et al [48] designed a two-layer clustering deep learning network to extract rural buildings.…”

Training data augmentations for improving hyperbola recognition in ground penetrating radar B-scan image for tree roots detection

“…YOLOv3 and Faster R-CNN (Ren et al 2017) with anchor-base are the most versatile target detection methods. YOLOv3 and Faster R-CNN have been successfully applied in agriculture (Liu et al 2020;Thanh Le et al 2021), geology (Ma et al 2019;Davletshin et al 2021), remote sensing Li et al 2022), and medicine (Rosati et al 2020;Yao et al 2020). YOLOv3 is widely used in forestry fields, such as tree health classification (Yarak et al 2021), forest census (Zheng et al 2019), and tree species identification for detection.…”