Edge detection in remote sensing images via lattice filters based subband decomposition

2009 ◽  
Author(s):  
N.H. Kaplan ◽  
I. Erer ◽  
S. Kent
Keyword(s):  
Remote Sensing ◽  
Edge Detection ◽  
Subband Decomposition ◽  
Remote Sensing Images ◽  
Lattice Filters

An Improved Multi-scale Probabilistic Edge Detection for Urban remote sensing images

2021 ◽  
Author(s):  
Xuyang Teng ◽  
Yiming Pan ◽  
Meilin He ◽  
Meihua Bi ◽  
Zhaoyang Qiu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Edge Detection ◽  
Remote Sensing Images ◽  
Multi Scale ◽  
Urban Remote Sensing

scholarly journals ERN: Edge Loss Reinforced Semantic Segmentation Network for Remote Sensing Images

2018 ◽  
Vol 10 (9) ◽  
pp. 1339 ◽  
Author(s):  
Shuo Liu ◽  
Wenrui Ding ◽  
Chunhui Liu ◽  
Yu Liu ◽  
Yufeng Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Edge Detection ◽  
Qualitative Evaluation ◽  
Semantic Segmentation ◽  
Model Complexity ◽  
Semantic Ambiguity ◽  
Remote Sensing Images ◽  
Future Networks ◽  
Spatial Boundary ◽  
Boundary Information

The semantic segmentation of remote sensing images faces two major challenges: high inter-class similarity and interference from ubiquitous shadows. In order to address these issues, we develop a novel edge loss reinforced semantic segmentation network (ERN) that leverages the spatial boundary context to reduce the semantic ambiguity. The main contributions of this paper are as follows: (1) we propose a novel end-to-end semantic segmentation network for remote sensing, which involves multiple weighted edge supervisions to retain spatial boundary information; (2) the main representations of the network are shared between the edge loss reinforced structures and semantic segmentation, which means that the ERN simultaneously achieves semantic segmentation and edge detection without significantly increasing the model complexity; and (3) we explore and discuss different ERN schemes to guide the design of future networks. Extensive experimental results on two remote sensing datasets demonstrate the effectiveness of our approach both in quantitative and qualitative evaluation. Specifically, the semantic segmentation performance in shadow-affected regions is significantly improved.

A Fast Algorithm for Road Recognition in Remote Sensing Image

2010 ◽  
Vol 108-111 ◽  
pp. 1344-1347
Author(s):  
Li Li Li ◽  
Yong Xin Liu
Keyword(s):  
Remote Sensing ◽  
Edge Detection ◽  
Fast Algorithm ◽  
Remote Sensing Image ◽  
Extraction Methods ◽  
Feature Integration ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
The Road ◽  
Simulation Results

In general, the road extraction methods in remote sensing images mainly are edge detection, feature integration, and so on. A fast road recognition arithmetic is presented in this paper. First using adaptive binarization arithmetic, the path on remote sensing images is extracted. Then morphological method is used to process image. Finally, the extracted image superimposed with the original and get clear road. Simulation results shows that this algorithm is efficiency, the anti-noise ability is enhance, and more precision.

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