Multiscale and Multitemporal Road Detection from High Resolution SAR Images Using Attention Mechanism

Abstract

Road detection from images has emerged as an important way to obtain road information, thereby gaining much attention in recent years. However, most existing methods only focus on extracting road information from single temporal intensity images, which may cause a decrease in image resolution due to the use of spatial filter methods to avoid coherent speckle noises. Some newly developed methods take into account the multi-temporal information in the preprocessing stage to filter the coherent speckle noise in the SAR imagery. They ignore the temporal characteristic of road objects such as the temporal consistency for the road objects in the multitemporal SAR images that cover the same area and are taken at adjacent times, causing the limitation in detection performance. In this paper, we propose a multiscale and multitemporal network (MSMTHRNet) for road detection from SAR imagery, which contains the temporal consistency enhancement module (TCEM) and multiscale fusion module (MSFM) that are based on attention mechanism. In particular, we propose the TCEM to make full use of multitemporal information, which contains temporal attention submodule that applies attention mechanism to capture temporal contextual information. We enforce temporal consistency constraint by the TCEM to obtain the enhanced feature representations of SAR imagery that help to distinguish the real roads. Since the width of roads are various, incorporating multiscale features is a promising way to improve the results of road detection. We propose the MSFM that applies learned weights to combine predictions of different scale features. Since there is no public dataset, we build a multitemporal road detection dataset to evaluate our methods. State-of-the-art semantic segmentation network HRNetV2 is used as a baseline method to compare with MSHRNet that only has MSFM and the MSMTHRNet. The MSHRNet(TAF) whose input is the SAR image after the temporal filter is adopted to compare with our proposed MSMTHRNet. On our test dataset, MSHRNet and MSMTHRNet improve over the HRNetV2 by 2.1% and 14.19%, respectively, in the IoU metric and by 3.25% and 17.08%, respectively, in the APLS metric. MSMTHRNet improves over the MSMTHRNet(TAF) by 8.23% and 8.81% in the IoU metric and APLS metric, respectively.