Full text

Turn on search term navigation

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Shadow detection is an essential research topic in the remote-sensing domain, as the presence of shadow causes the loss of ground-object information in real areas. It is hard to define specific threshold values for the identification of shadow areas with the existing unsupervised approaches due to the complexity of remote-sensing scenes. In this study, an adaptive unsupervised-shadow-detection method based on multichannel features is proposed, which can adaptively distinguish shadow in different scenes. First, new multichannel features were designed in the hue, saturation, and intensity color space, and the shadow properties of high hue, high saturation, and low intensity were considered to solve the insufficient feature-extraction problem of shadows. Then, a dynamic local adaptive particle swarm optimization was proposed to calculate the segmentation thresholds for shadows in an adaptive manner. Finally, experiments performed on the Aerial Imagery dataset for Shadow Detection (AISD) demonstrated the superior performance of the proposed approach in comparison with traditional unsupervised shadow-detection and state-of-the-art deep-learning methods. The experimental results show that the proposed approach can detect the shadow areas in remote-sensing images more accurately and efficiently, with the F index being 82.70% on the testing images. Thus, the proposed approach has better application potential in scenarios without a large number of labeled samples.

Details

Title
Adaptive Unsupervised-Shadow-Detection Approach for Remote-Sensing Image Based on Multichannel Features
Author
He, Zhanjun 1 ; Zhang, Zhizheng 2 ; Guo, Mingqiang 2   VIAFID ORCID Logo  ; Wu, Liang 2   VIAFID ORCID Logo  ; Huang, Ying 3 

 School of Computer Science, China University of Geosciences, Wuhan 430074, China; [email protected] (Z.H.); [email protected] (Z.Z.); [email protected] (L.W.); Artificial Intelligence School, Wuchang University of Technology, Wuhan 430223, China; China National Engineering Research Centre for Geographic Information System, Wuhan 430074, China 
 School of Computer Science, China University of Geosciences, Wuhan 430074, China; [email protected] (Z.H.); [email protected] (Z.Z.); [email protected] (L.W.); China National Engineering Research Centre for Geographic Information System, Wuhan 430074, China 
 Wuhan Zondy Advanced Technology Institute Co., Ltd., Wuhan 430074, China; [email protected] 
First page
2756
Publication year
2022
Publication date
2022
Publisher
MDPI AG
e-ISSN
20724292
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2679794268
Copyright
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.