TRDet: Two-Stage Rotated Detection of Rural Buildings in Remote Sensing Images

Abstract

Fast and accurate acquisition of the outline of rural buildings on remote sensing images is an efficient method to monitor illegal rural buildings. The traditional object detection method produces useless background information when detecting rural buildings; the semantic segmentation method cannot accurately segment the contours between buildings; the instance segmentation method cannot obtain regular building contours. The rotated object detection methods can effectively solve the problem that the traditional artificial intelligence method cannot accurately extract the outline of buildings. However, the rotated object detection methods are easy to lose location information of small objects in advanced feature maps and are sensitive to noise. To resolve these problems, this paper proposes a two-stage rotated object detection network for rural buildings (TRDet) by using a deep feature fusion network (DFF-Net) and a pixel attention module (PAM). Specifically, TRDet first fuses low-level location and high-level semantic information through the DFF-Net and then reduces the interference of noise information to the network through the PAM. The experimental results show that the mean average precession (mAP), precision, recall rate, and F1 score of the proposed TRDet are 83.57%, 91.11%, 86.5%, and 88.74%, respectively, which outperform the R2CNN model by 15%, 15.54%, 4.01%, and 9.87%. The results demonstrate that the TRDet can achieve better detection in small rural buildings and dense rural buildings.

IFSrNet: Multi-Scale IFS Feature-Guided Registration Network Using Multispectral Image-to-Image Translation

“…In the field of medical image diagnosis [2], the registration captured at different times or in different modalities can assist physicians in formulating more accurate treatment plans. In the context of remote sensing [3], the registration of multispectral images can provide detailed information about the ground. Additionally, this technology can be employed in monitoring the distribution and dispersion of environmental pollutants [4] and in precision agricultural management [5].…”

Building Extraction From High Spatial Resolution Remote Sensing Images of Complex Scenes by Combining Region-Line Feature Fusion and OCNN

“…The complexity of the environment refers to the low contrast of buildings in the local area induced by the similarity in spectral features between buildings and their neighboring environment. A typical real world complex scene of this type is a rural area [7]. Furthermore, the spatial resolution of remote sensing images is not as high as the existing datasets (mostly aerial images), and there are many mixed pixels in remote sensing images, which increases the challenges of building extraction.…”

A Context Feature Enhancement Network for Building Extraction from High-Resolution Remote Sensing Imagery

“…In surveying and mapping engineering, building extraction is widely used as a means of acquiring data [3,4]. At the same time, it is also an important part of the fields of precision agriculture and environmental monitoring [5][6][7]. However, the buildings in high-resolution remote sensing images have variable shapes, rich details, and inconsistent texture colors.…”