A multi-task learning method for extraction of newly constructed areas based on bi-temporal hyperspectral images

被引:8
作者
Tu, Lilin [1 ]
Huang, Xin [1 ]
Li, Jiayi [1 ]
Yang, Jie [1 ]
Gong, Jianya [1 ]
机构
[1] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430079, Peoples R China
基金
中国国家自然科学基金;
关键词
Unsupervised change detection; Multivariate alternation detection (MAD); Semantic segmentation; Multi-task learning; Hyperspectral images; BUILT-UP AREA; GOOGLE EARTH; TIME-SERIES; DEEP; LAND; CLASSIFICATION; SEGMENTATION; NETWORK; FUSION;
D O I
10.1016/j.isprsjprs.2024.01.016
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
Newly constructed areas (NCA) are continuously emerging with the development of urbanization, which, however, triggered a series of ecological and environmental issues. Therefore, monitoring NCA is of great significance for sustainable urbanization. Extraction of NCA from remote sensing images involves semantic segmentation of constructed areas and change detection. However, the acquisition of samples for training change detection models is time-consuming. In this study, we proposed a multi -task learning framework including unsupervised change detection and supervised semantic segmentation for NCA extraction. The main contributions of this study are: (1) A deep multivariate alternation detection (DMAD) algorithm was proposed for unsupervised change detection of hyperspectral images. By introducing the optimization objective of Multivariate Alternation Detection (MAD) in the loss functions, the features of changed areas can be extracted more effectively; (2) A multi -task learning framework was proposed for the joint training of DMAD change detection and supervised semantic segmentation, where the two modules were mutually optimized via semantic masks and consistency loss. Based on Orbita Hyperspectral Satellites (OHS) images, we conducted experiments in two cities (Qinzhou and Wuzhou) of Guangxi province. The experimental results indicated that the proposed method can achieve the F1 -score better than existing unsupervised change detection methods (e.g., DCCA and DSFA) by an average of 15%, and the OA better than existing semantic segmentation methods (e.g., U -Net and FreeNet) by an average of 1%. The F1 -score and Kappa for NCA extraction reached above 0.80 for both study areas. The implementation of this paper will be made available at https://github.com/tulilin/Multitask_NCA.
引用
收藏
页码:308 / 323
页数:16
相关论文
共 69 条
[1]  
Andrew G., 2013, INT C MACHINE LEARNI, V28, P1247
[2]  
[Anonymous], 2017, Stable and controllable neural texture synthesis and style transfer using histogram losses
[3]   SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation [J].
Badrinarayanan, Vijay ;
Kendall, Alex ;
Cipolla, Roberto .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2017, 39 (12) :2481-2495
[4]  
Bai S., 2018, CoRR
[5]   The Time Variable in Data Fusion: A Change Detection Perspective [J].
Bovolo, Francesca ;
Bruzzone, Lorenzo .
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE, 2015, 3 (03) :8-26
[6]   A coarse-to-fine weakly supervised learning method for green plastic cover segmentation using high-resolution remote sensing images [J].
Cao, Yinxia ;
Huang, Xin .
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2022, 188 :157-176
[7]   Change Detection in Multisource VHR Images via Deep Siamese Convolutional Multiple-Layers Recurrent Neural Network [J].
Chen, Hongruixuan ;
Wu, Chen ;
Du, Bo ;
Zhang, Liangpei ;
Wang, Le .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2020, 58 (04) :2848-2864
[8]   DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs [J].
Chen, Liang-Chieh ;
Papandreou, George ;
Kokkinos, Iasonas ;
Murphy, Kevin ;
Yuille, Alan L. .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2018, 40 (04) :834-848
[9]   Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks [J].
Chen, Yushi ;
Jiang, Hanlu ;
Li, Chunyang ;
Jia, Xiuping ;
Ghamisi, Pedram .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2016, 54 (10) :6232-6251
[10]   Promotion incentives for local officials and the expansion of urban construction land in China: Using the Yangtze River Delta as a case study [J].
Chen, Zhigang ;
Tang, Jing ;
Wan, Jiayu ;
Chen, Yi .
LAND USE POLICY, 2017, 63 :214-225