Remote Sensing Image Super-Resolution Using Sparse Representation and Coupled Sparse Autoencoder

被引:99
作者
Shao, Zhenfeng [1 ,2 ]
Wang, Lei [1 ]
Wang, Zhongyuan [3 ]
Deng, Juan [4 ]
机构
[1] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Hubei, Peoples R China
[2] Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Hubei, Peoples R China
[3] Natl Engn Res Ctr Multimedia Software, Wuhan 430079, Hubei, Peoples R China
[4] Wuhan Univ, Sch Elect Informat, Wuhan 430072, Hubei, Peoples R China
基金
中国国家自然科学基金;
关键词
Coupled sparse autoencoder (CSAE); image super-resolution (SR); remote sensing image; sparse representation; RECONSTRUCTION; DICTIONARIES;
D O I
10.1109/JSTARS.2019.2925456
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Remote sensing image super-resolution (SR) refers to a technique improving the spatial resolution, which in turn benefits to the subsequent image interpretation, e.g., target recognition, classification, and change detection. In popular sparse representation-based methods, due to the complex imaging conditions and unknown degradation process, the sparse coefficients of low-resolution (LR) observed images are hardly consistent with the real high-resolution (HR) counterparts, which leads to unsatisfactory SR results. To address this problem, a novel coupled sparse autoencoder (CSAE) is proposed in this paper to effectively learn the mapping relation between the LR and HR images. Specifically, the LR and HR images are first represented by a set of sparse coefficients, and then, a CSAE is established to learn the mapping relation between them. Since the proposed method leverages the feature representation ability of both sparse decomposition and CSAE, the mapping relation between the LR and HR images can be accurately obtained. Experimentally, the proposed method is compared with several state-of-the-art image SR methods on three real-world remote sensing image datasets with different spatial resolutions. The extensive experimental results demonstrate that the proposed method has gained solid improvements in terms of average peak signal-to-noise ratio and structural similarity measurement on all of the three datasets. Moreover, results also show that with larger upscaling factors, the proposed method achieves more prominent performance than the other competitive methods.
引用
收藏
页码:2663 / 2674
页数:12
相关论文
共 37 条
[1]   Fully spatially adaptive smoothing parameter estimation for Markov random field super-resolution mapping of remotely sensed images [J].
Aghighi, H. ;
Trinder, J. ;
Lim, S. ;
Tarabalka, Y. .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2015, 36 (11) :2851-2879
[2]   K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation [J].
Aharon, Michal ;
Elad, Michael ;
Bruckstein, Alfred .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2006, 54 (11) :4311-4322
[3]  
Bareja M. N., 2012, Proceedings of the 2012 International Conference on Communication Systems and Network Technologies (CSNT 2012), P95, DOI 10.1109/CSNT.2012.30
[4]   A survey on object detection in optical remote sensing images [J].
Cheng, Gong ;
Han, Junwei .
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2016, 117 :11-28
[5]   Hyperspectral image super-resolution via non-local sparse tensor factorization [J].
Dian, Renwei ;
Fang, Leyuan ;
Li, Shutao .
30TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2017), 2017, :3862-3871
[6]   Accelerating the Super-Resolution Convolutional Neural Network [J].
Dong, Chao ;
Loy, Chen Change ;
Tang, Xiaoou .
COMPUTER VISION - ECCV 2016, PT II, 2016, 9906 :391-407
[7]   Image Super-Resolution Using Deep Convolutional Networks [J].
Dong, Chao ;
Loy, Chen Change ;
He, Kaiming ;
Tang, Xiaoou .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2016, 38 (02) :295-307
[8]   Hyperspectral Image Super-Resolution via Non-Negative Structured Sparse Representation [J].
Dong, Weisheng ;
Fu, Fazuo ;
Shi, Guangming ;
Cao, Xun ;
Wu, Jinjian ;
Li, Guangyu ;
Li, Xin .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2016, 25 (05) :2337-2352
[9]   Sparse Representation Based Image Interpolation With Nonlocal Autoregressive Modeling [J].
Dong, Weisheng ;
Zhang, Lei ;
Lukac, Rastislav ;
Shi, Guangming .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2013, 22 (04) :1382-1394
[10]   Compressed sensing [J].
Donoho, DL .
IEEE TRANSACTIONS ON INFORMATION THEORY, 2006, 52 (04) :1289-1306