RoadSeg-CD: A Network With Connectivity Array and Direction Map for Road Extraction From SAR Images

Road extraction from synthetic aperture radar (SAR) images has attracted much attention in the field of remote sensing image processing. General road extraction algorithms, affected by shadows of buildings and trees, are prone to producing fragmented road segments. To improve the accuracy and completeness of road extraction, we propose a neural network-based algorithm, which takes the connectivity and direction features of roads into consideration, named RoadSeg-CD. It consists of two branches: one is the main branch for road segmentation; the other is the auxiliary branch for learning road directions. In the main branch, a connectivity array is designed to utilize local contextual information and construct a connectivity loss based on the predicted probabilities of neighboring pixels. In the auxiliary branch, we proposed a novel road direction map, which is used for learning the directions of roads. The two branches are connected by specific feature fusion process, and the output from the main branch is taken as the road extraction result. Experiments on real radar images are implemented to validate the effectiveness of our method. The experimental results demonstrate that our method can obtain more continuous and more complete roads than several state-of-the-art road extraction algorithms

RoadSeg-CD: A Network With Connectivity Array and Direction Map for Road Extraction From SAR Images

Road extraction from synthetic aperture radar (SAR) images has attracted much attention in the field of remote sensing image processing. General road extraction algorithms, affected by shadows of buildings and trees, are prone to producing fragmented road segments. To improve the accuracy and completeness of road extraction, we propose a neural network-based algorithm, which takes the connectivity and direction features of roads into consideration, named RoadSeg-CD. It consists of two branches: one is the main branch for road segmentation; the other is the auxiliary branch for learning road directions. In the main branch, a connectivity array is designed to utilize local contextual information and construct a connectivity loss based on the predicted probabilities of neighboring pixels. In the auxiliary branch, we proposed a novel road direction map, which is used for learning the directions of roads. The two branches are connected by specific feature fusion process, and the output from the main branch is taken as the road extraction result. Experiments on real radar images are implemented to validate the effectiveness of our method. The experimental results demonstrate that our method can obtain more continuous and more complete roads than several state-of-the-art road extraction algorithms

Airport Detection in SAR Images via Salient Line Segment Detector and Edge-Oriented Region Growing

Airport detection in synthetic aperture radar (SAR) images has attracted much concern in the field of remote sensing. Affected by other salient objects with geometrical features similar to those of airports, traditional methods often generate false detections. In order to produce the geometrical features of airports and suppress the influence of irrelevant objects, we propose a novel method for airport detection in SAR images. First, a salient line segment detector is constructed to extract salient line segments in the SAR images. Second, we obtain the airport support regions by grouping these line segments according to the commonality of these geometrical features. Finally, we design an edge-oriented region growing (EORG) algorithm, where growing seeds are selected from the airport support regions with the help of edge information in SAR images. Using EORG, the airport region can be mapped by performing region growing with these seeds. We implement experiments on real radar images to validate the effectiveness of our method. The experimental results demonstrate that our method can acquire more accurate locations and contours of airports than several state-of-the-art airport detection algorithms