高光谱遥感影像异常目标检测研究进展

屈博; 郑向涛; 钱学明; 卢孝强

doi:10.11834/jrs.20232405

综述 | 浏览量 : 0 下载量: 1473 CSCD: 1

R-PDF
PDF
导出
分享
收藏
专辑

高光谱遥感影像异常目标检测研究进展
Research progress on hyperspectral anomaly detection
2024年28卷第1期页码：42-54
纸质出版日期： 2024-01-07 ，
DOI： 10.11834/jrs.20232405
稿件说明：

移动端阅览

屈博，郑向涛，钱学明，卢孝强.2024.高光谱遥感影像异常目标检测研究进展.遥感学报，28（1）： 42-54

Qu B，Zheng X T，Qian X M and Lu X Q. 2024. Research progress on hyperspectral anomaly detection. National Remote Sensing Bulletin， 28（1）：42-54
屈博，郑向涛，钱学明，卢孝强.2024.高光谱遥感影像异常目标检测研究进展.遥感学报，28（1）： 42-54 DOI： 10.11834/jrs.20232405.

Qu B，Zheng X T，Qian X M and Lu X Q. 2024. Research progress on hyperspectral anomaly detection. National Remote Sensing Bulletin， 28（1）：42-54 DOI： 10.11834/jrs.20232405.

摘要

随着航空航天技术与遥感技术的不断发展，遥感影像在诸多领域的应用不断拓展，其中高光谱分辨率遥感影像具有“图谱合一”的特点，即该数据既包含了具有强大区分性的地物光谱信息，又包含了丰富的地物空间位置信息，因此高光谱数据具有非常大的应用潜力。高光谱异常目标检测问题，是在对目标先验信息未知的前提下，根据光谱与空间信息实现对区域中的异常目标的进行“盲”检测，因此其在资源调查、灾害救援等领域发挥了巨大的作用，是遥感领域非常重要的研究课题。本文针对高光谱遥感影像异常目标检测研究方向，首先总结阐述了目前高光谱异常目标检测问题的主要研究进展，根据算法原理的不同对现有主流算法进行了分类与总结，主要分成了基于统计学、基于数据表达、基于数据分解、基于深度学习等不同的种类的方法，并对每类方法的特点进行分析。随后通过对现有方法的调研、分析与总结，提出了数据库拓展、多源数据融合、算法实用化等高光谱异常检测研究未来发展的3个方向。

Abstract

The applications of remote sensing images in numerous fields have been increasing with the continuous development of aerospace and remote sensing technologies. HyperSpectral Image (HSI) is a common type of remote sensing image that comprises a series of two-dimensional remote sensing images as a 3D data cube. Each two-dimensional image in HSI can reveal the reflection/radiation intensity of different wavelengths of electromagnetic waves

and each pixel of HSI corresponds to the spectral curve reflecting the spectral information in different wavelengths. Therefore

the hyperspectral remote sensing images are characterized by “spatial-spectral integration

” which contains not only spectral information with strong discriminant but also rich spatial information. Therefore

the hyperspectral data have considerable application potential.

Hyperspectral anomaly detection aims to detect pixels in a scene with different characteristics from surrounding pixels and determines them as anomalous targets without any previous knowledge of the target. Hyperspectral anomaly detection is an unsupervised process that does not require any priori information regarding the target to be measured in advance; thus

this type of detection plays a crucial role in real life. For example

anomaly target detection technology can be used to search and rescue people after a disaster

quickly determine the fire point of a forest fire

and search mineral points in mineral resource exploration. Hyperspectral anomaly detection has been a popular research direction in the area of remote sensing image processing in recent years

and a numerous researchers have conducted extensive research and achieved rich research results.

However

hyperspectral anomaly detection still encounters many difficult problems. For example

the targets of the same material may exhibit various spectral characteristics due to the different imaging equipment and environment

which may interfere with the detection results and lead to the problem of “same object with different spectra.” Meanwhile

the targets of different materials may also exhibit the problem of “different objects with different spectra.” Then

most of the existing hyperspectral anomaly detection algorithms are only in the laboratory stage and with low technology maturity. Furthermore

the hyperspectral data may have numerous spectral bands that contain a considerable amount of redundant information

which increases the difficulty of data processing. Moreover

the number of publicly available hyperspectral anomaly detection datasets is insufficient and mostly old.

In this paper

the main research progress of hyperspectral anomaly detection is first summarized. The existing mainstream algorithms are then classified and summarized. These algorithms are mainly divided into five categories: statistics-based anomaly detection methods

data expression-based anomaly detection methods

data decomposition-based anomaly detection methods

deep learning-based anomaly detection methods

and other methods. Through the investigation

analysis

and summary of the existing methods

three future development directions of hyperspectral anomaly detection are proposed. (1) Database expansion: new datasets with additional images and highly sophisticated remote sensing sensors are introduced. (2) Multisource data combination: the advantages of different imaging sensors and various types of remote sensing data are maximized. (3) Algorithm practicality: the anomaly detection algorithms are relayed for application on real platforms.

关键词

遥感高光谱遥感高光谱异常检测深度学习矩阵分解

Keywords

remote sensinghyperspectral remote sensinghyperspectral anomaly detectiondeep learningmatrix factorization

references

Candès E J, Li X D, Ma Y and Wright J. 2011. Robust principal component analysis?. Journal of the ACM, 58(3): 11 [DOI: 10.1145/1970392.1970395http://dx.doi.org/10.1145/1970392.1970395]

Chen Y, Nasrabadi N M and Tran T D. 2011. Sparse representation for target detection in hyperspectral imagery. IEEE Journal of Selected Topics in Signal Processing, 5(3): 629-640 [DOI: 10.1109/JSTSP.2011.2113170http://dx.doi.org/10.1109/JSTSP.2011.2113170]

Cheng T K and Wang B. 2020. Graph and total variation regularized low-rank representation for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing, 58(1): 391-406 [DOI: 10.1109/TGRS.2019.2936609http://dx.doi.org/10.1109/TGRS.2019.2936609]

Feng S, Tang S L, Zhao C H and Cui Y. 2022. A hyperspectral anomaly detection method based on low-rank and sparse decomposition with density peak guided collaborative representation. IEEE Transactions on Geoscience and Remote Sensing, 60: 5501513 [DOI: 10.1109/TGRS.2021.3054736http://dx.doi.org/10.1109/TGRS.2021.3054736]

Fu X Y, Jia S, Zhuang L N, Xu M, Zhou J and Li Q Q. 2021. Hyperspectral anomaly detection via deep plug-and-play denoising CNN regularization. IEEE Transactions on Geoscience and Remote Sensing, 59(11): 9553-9568 [DOI: 10.1109/TGRS.2021.3049224http://dx.doi.org/10.1109/TGRS.2021.3049224]

Geng X R, Sun K, Ji L Y and Zhao Y C. 2014. A high-order statistical tensor based algorithm for anomaly detection in hyperspectral imagery. Scientific Reports, 4(1): 6869 [DOI: 10.1038/srep06869http://dx.doi.org/10.1038/srep06869]

Guo Q D, Zhang B, Ran Q, Gao L R, Li J and Plaza A. 2014. Weighted-RXD and linear filter-based RXD: improving background statistics estimation for anomaly detection in hyperspectral imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6): 2351-2366 [DOI: 10.1109/JSTARS.2014.2302446http://dx.doi.org/10.1109/JSTARS.2014.2302446]

Herweg J A, Kerekes J P, Weatherbee O, Messinger D, van Aardt J, Ientilucci E, Ninkov Z, Faulring J, Raqueño N and Meola J. 2012. SpecTIR hyperspectral airborne Rochester experiment data collection campaign//Proceedings Volume 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII. Baltimore: SPIE: 717-726 [DOI: 10.1117/12.919268http://dx.doi.org/10.1117/12.919268]

Hu J, Zhang Y J, Zhao M H and Li P. 2022. Spatial-spectral extraction for hyperspectral anomaly detection. IEEE Geoscience and Remote Sensing Letters, 19: 6004605 [DOI: 10.1109/LGRS.2021.3130908http://dx.doi.org/10.1109/LGRS.2021.3130908]

Jiang K, Xie W Y, Li Y S, Lei J, He G and Du Q. 2020a. Semisupervised spectral learning with generative adversarial network for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing, 58(7): 5224-5236 [DOI: 10.1109/TGRS.2020.2975295http://dx.doi.org/10.1109/TGRS.2020.2975295]

Jiang T, Li Y S, Xie W Y and Du Q. 2020b. Discriminative reconstruction constrained generative adversarial network for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing, 58(7): 4666-4679 [DOI: 10.1109/TGRS.2020.2965961http://dx.doi.org/10.1109/TGRS.2020.2965961]

Jiang T, Xie W Y, Li Y S and Du Q. 2020c. Discriminative semi-supervised generative adversarial network for hyperspectral anomaly detection//2020 IEEE International Geoscience and Remote Sensing Symposium. Waikoloa: IEEE: 2420-2423 [DOI: 10.1109/IGARSS39084.2020.9323688http://dx.doi.org/10.1109/IGARSS39084.2020.9323688]

Kang X D, Zhang X P, Li S T, Li K L, Li J and Benediktsson J A. 2017. Hyperspectral anomaly detection with attribute and edge-preserving filters. IEEE Transactions on Geoscience and Remote Sensing, 55(10): 5600-5611 [DOI: 10.1109/TGRS.2017.2710145http://dx.doi.org/10.1109/TGRS.2017.2710145]

Kwon H and Nasrabadi N M. 2005. Kernel RX-algorithm: a nonlinear anomaly detector for hyperspectral imagery. IEEE transactions on Geoscience and Remote Sensing, 43(2): 388-397 [DOI: 10.1109/TGRS.2004.841487http://dx.doi.org/10.1109/TGRS.2004.841487]

Li J Y, Zhang H Y, Zhang L P and Ma L. 2015. Hyperspectral anomaly detection by the use of background joint sparse representation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6): 2523-2533 [DOI: 10.1109/JSTARS.2015.2437073http://dx.doi.org/10.1109/JSTARS.2015.2437073]

Li K, Ling Q, Qin Y, Wang Y Q, Cai Y M, Lin Z P and An W. 2022a. Spectral-spatial deep support vector data description for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing, 60: 5522316 [DOI: 10.1109/TGRS.2022.3144192http://dx.doi.org/10.1109/TGRS.2022.3144192]

Li L, Li W, Du Q and Tao R. 2021. Low-rank and sparse decomposition with mixture of Gaussian for hyperspectral anomaly detection. IEEE Transactions on Cybernetics, 51(9): 4363-4372 [DOI: 10.1109/TCYB.2020.2968750http://dx.doi.org/10.1109/TCYB.2020.2968750]

Li W and Du Q. 2015. Collaborative representation for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing, 53(3): 1463-1474 [DOI: 10.1109/TGRS.2014.2343955http://dx.doi.org/10.1109/TGRS.2014.2343955]

Li W, Wu G D and Du Q. 2017. Transferred deep learning for anomaly detection in hyperspectral imagery. IEEE Geoscience and Remote Sensing Letters, 14(5): 597-601 [DOI: 10.1109/LGRS.2017.2657818http://dx.doi.org/10.1109/LGRS.2017.2657818]

Li Y S, Jiang T, Xie W Y, Lei J and Du Q. 2022b. Sparse coding-inspired GAN for hyperspectral anomaly detection in weakly supervised learning. IEEE Transactions on Geoscience and Remote Sensing, 60: 5512811 [DOI: 10.1109/TGRS.2021.3102048http://dx.doi.org/10.1109/TGRS.2021.3102048]

Li Z, Zhang Y and Zhang J P. 2022d. Hyperspectral anomaly detection for spectral anomaly targets via spatial and spectral constraints. IEEE Transactions on Geoscience and Remote Sensing, 60: 5511515 [DOI: 10.1109/TGRS.2021.3091156http://dx.doi.org/10.1109/TGRS.2021.3091156]

Li Z W, Shi S X, Wang L Q, Xu M M and Li L Y. 2022c. Unsupervised generative adversarial network with background enhancement and irredundant pooling for hyperspectral anomaly detection. Remote Sensing, 14(5): 1265 [DOI: 10.3390/rs14051265http://dx.doi.org/10.3390/rs14051265]

Ling Q, Guo Y L, Lin Z P and An W. 2019. A constrained sparse representation model for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing, 57(4): 2358-2371 [DOI: 10.1109/TGRS.2018.2872900http://dx.doi.org/10.1109/TGRS.2018.2872900]

Liu G C, Lin Z C, Yan S C, Sun J, Yu Y and Ma Y. 2013. Robust recovery of subspace structures by low-rank representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(1): 171-184 [DOI: 10.1109/TPAMI.2012.88http://dx.doi.org/10.1109/TPAMI.2012.88]

Liu W, Wu X, Qu H and Wang F. 2018. Improved collaborative representation for hyperspectral imagery anomaly detection algorithm. Application Research of Computers, 35(12): 3824-3827, 3840

刘万军, 武小杰, 曲海成, 王凤. 2018. 改进协同表示的高光谱图像异常检测算法. 计算机应用研究, 35(12): 3824-3827, 3840 [DOI: 10.3969/j.issn.1001-3695.2018.12.068http://dx.doi.org/10.3969/j.issn.1001-3695.2018.12.068]

Ma D D, Yuan Y and Wang Q. 2017. A sparse dictionary learning method for hyperspectral anomaly detection with capped norm//2017 IEEE International Geoscience and Remote Sensing Symposium. Fort Worth: IEEE: 648-651 [DOI: 10.1109/IGARSS.2017.8127037http://dx.doi.org/10.1109/IGARSS.2017.8127037]

Ma X X, Zhang X R, Huyan N, Gu J, Tang X and Jiao L C. 2022. Background representation learning with structural constraint for hyperspectral anomaly detection. IEEE Geoscience and Remote Sensing Letters, 19: 5505705 [DOI: 10.1109/LGRS.2021.3073176http://dx.doi.org/10.1109/LGRS.2021.3073176]

Matteoli S, Veracini T, Diani M and Corsini G. 2014. A locally adaptive background density estimator: an evolution for RX-based anomaly detectors. IEEE Geoscience and Remote Sensing Letters, 11(1): 323-327 [DOI: 10.1109/LGRS.2013.2257670http://dx.doi.org/10.1109/LGRS.2013.2257670]

Ning H Y, Zhang X R, Zhou H Y and Jiao L C. 2019. Hyperspectral anomaly detection via background and potential anomaly dictionaries construction. IEEE Transactions on Geoscience and Remote Sensing, 57(4): 2263-2276 [DOI: 10.1109/TGRS.2018.2872590http://dx.doi.org/10.1109/TGRS.2018.2872590]

Qu Y, Wang W, Guo R, Ayhan B, Kwan C, Vance S and Qi H R. 2018. Hyperspectral anomaly detection through spectral unmixing and dictionary-based low-rank decomposition. IEEE Transactions on Geoscience and Remote Sensing, 56(8): 4391-4405 [DOI: 10.1109/TGRS.2018.2818159http://dx.doi.org/10.1109/TGRS.2018.2818159]

Reed I S and Yu X. 1990. Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution. IEEE Transactions on Acoustics, Speech, and Signal Processing, 38(10): 1760-1770 [DOI: 10.1109/29.60107http://dx.doi.org/10.1109/29.60107]

Song S Z, Zhou H X, Yang Y X and Song J L Q. 2019. Hyperspectral anomaly detection via convolutional neural network and low rank with density-based clustering. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(9): 3637-3649 [DOI: 10.1109/JSTARS.2019.2926130http://dx.doi.org/10.1109/JSTARS.2019.2926130]

Su H J, Wu Z Y, Zhu A X and Du Q. 2020. Low rank and collaborative representation for hyperspectral anomaly detection via robust dictionary construction. ISPRS Journal of Photogrammetry and Remote Sensing, 169: 195-211 [DOI: 10.1016/j.isprsjprs.2020.09.008http://dx.doi.org/10.1016/j.isprsjprs.2020.09.008]

Sun W W, Liu C, Li J L, Lai Y M and Li W Y. 2014. Low-rank and sparse matrix decomposition-based anomaly detection for hyperspectral imagery. Journal of Applied Remote Sensing, 8(1): 083641 [DOI: 10.1117/1.JRS.8.083641http://dx.doi.org/10.1117/1.JRS.8.083641]

Taghipour A and Ghassemian H. 2019. Unsupervised hyperspectral target detection using spectral residual of deep autoencoder networks//2019 4th International Conference on Pattern Recognition and Image Analysis. Tehran: IEEE: 52-57 [DOI: 10.1109/PRIA.2019.8785982http://dx.doi.org/10.1109/PRIA.2019.8785982]

Taghipour A and Ghassemian H. 2021. A bottom-up and top-down human visual attention approach for hyperspectral anomaly detection. Journal of Visual Communication and Image Representation, 77: 103113 [DOI: 10.1016/j.jvcir.2021.103113http://dx.doi.org/10.1016/j.jvcir.2021.103113]

Tao R, Zhao X D, Li W, Li H C and Du Q. 2019. Hyperspectral anomaly detection by fractional Fourier entropy. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(12): 4920-4929 [DOI: 10.1109/JSTARS.2019.2940278http://dx.doi.org/10.1109/JSTARS.2019.2940278]

Tu B, Yang X C, Li N Y, Zhou C L and He D B. 2020a. Hyperspectral anomaly detection via density peak clustering. Pattern Recognition Letters, 129: 144-149 [DOI: 10.1016/j.patrec.2019.11.022http://dx.doi.org/10.1016/j.patrec.2019.11.022]

Tu B, Yang X C, Zhou C L, He D B and Plaza A. 2020b. Hyperspectral anomaly detection using dual window density. IEEE Transactions on Geoscience and Remote Sensing, 58(12): 8503-8517 [DOI: 10.1109/TGRS.2020.2988385http://dx.doi.org/10.1109/TGRS.2020.2988385]

Vafadar M and Ghassemian H. 2018. Anomaly detection of hyperspectral imagery using modified collaborative representation. IEEE Geoscience and Remote Sensing Letters, 15(4): 577-581 [DOI: 10.1109/LGRS.2018.2796083http://dx.doi.org/10.1109/LGRS.2018.2796083]

Wang Y L, Wang F C, Zhu Q Y, Song M P and Yu C Y. 2021. Transferred tensor decomposition-based deep learning for hyperspectral anomaly detection//2021 IEEE International Geoscience and Remote Sensing Symposium. Brussels: IEEE: 5279-5282 [DOI: 10.1109/IGARSS47720.2021.9555078http://dx.doi.org/10.1109/IGARSS47720.2021.9555078]

Xiang P, Song J L Q, Qin H L, Tan W, Li H and Zhou H X. 2021. Visual attention and background subtraction with adaptive weight for hyperspectral anomaly detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 2270-2283 [DOI: 10.1109/JSTARS.2021.3052968http://dx.doi.org/10.1109/JSTARS.2021.3052968]

Xie W Y, Jiang T, Li Y S, Jia X P and Lei J. 2019. Structure tensor and guided filtering-based algorithm for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing, 57(7): 4218-4230 [DOI: 10.1109/TGRS.2018.2890212http://dx.doi.org/10.1109/TGRS.2018.2890212]

Xie W Y, Liu B Z, Li Y S, Lei J and Du Q. 2020. Autoencoder and adversarial-learning-based semisupervised background estimation for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing, 58(8): 5416-5427 [DOI: 10.1109/TGRS.2020.2965995http://dx.doi.org/10.1109/TGRS.2020.2965995]

Xu Y, Wu Z B, Li J, Plaza A and Wei Z H. 2016. Anomaly detection in hyperspectral images based on low-rank and sparse representation. IEEE Transactions on Geoscience and Remote Sensing, 54(4): 1990-2000 [DOI: 10.1109/TGRS.2015.2493201http://dx.doi.org/10.1109/TGRS.2015.2493201]

Yuan Y, Wang Q and Zhu G K. 2015. Fast hyperspectral anomaly detection via high-order 2-D crossing filter. IEEE Transactions on Geoscience and Remote Sensing, 53(2): 620-630 [DOI: 10.1109/TGRS.2014.2326654http://dx.doi.org/10.1109/TGRS.2014.2326654]

Zhang L L and Cheng B Z. 2022. Fractional Fourier transform and transferred CNN based on tensor for hyperspectral anomaly detection. IEEE Geoscience and Remote Sensing Letters, 19: 5505505 [DOI: 10.1109/LGRS.2021.3072249http://dx.doi.org/10.1109/LGRS.2021.3072249]

Zhang L L, Cheng B Z, Tan S M and Wang Y M. 2022a. Fractional Fourier transform based joint adaptive subspace detection for hyperspectral anomaly detection. IEEE Geoscience and Remote Sensing Letters, 19: 6007005 [DOI: 10.1109/LGRS.2022.3150929http://dx.doi.org/10.1109/LGRS.2022.3150929]

Zhang L L, Ma J C, Cheng B Z and Lin F. 2022b. Fractional Fourier transform-based tensor RX for hyperspectral anomaly detection. Remote Sensing, 14(3): 797 [DOI: 10.3390/rs14030797http://dx.doi.org/10.3390/rs14030797]

Zhang L L and Zhao C H. 2017. Hyperspectral anomaly detection based on spectral-spatial background joint sparse representation. European Journal of Remote Sensing, 50(1): 362-376 [DOI: 10.1080/22797254.2017.1331697http://dx.doi.org/10.1080/22797254.2017.1331697]

Zhang X, Wen G J and Dai W. 2016a. A tensor decomposition-based anomaly detection algorithm for hyperspectral image. IEEE Transactions on Geoscience and Remote Sensing, 54(10): 5801-5820 [DOI: 10.1109/TGRS.2016.2572400http://dx.doi.org/10.1109/TGRS.2016.2572400]

Zhang Y X, Du B, Zhang L P and Wang S G. 2016b. A low-rank and sparse matrix decomposition-based Mahalanobis distance method for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing, 54(3): 1376-1389 [DOI: 10.1109/TGRS.2015.2479299http://dx.doi.org/10.1109/TGRS.2015.2479299]

Zhao C H, Li C, Feng S and Jia X P. 2022. Enhanced total variation regularized representation model with endmember background dictionary for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing, 60: 5518312 [DOI: 10.1109/TGRS.2021.3128183http://dx.doi.org/10.1109/TGRS.2021.3128183]

Zhao R, Du B and Zhang L P. 2017. Hyperspectral anomaly detection via a sparsity score estimation framework. IEEE Transactions on Geoscience and Remote Sensing, 55(6): 3208-3222 [DOI: 10.1109/TGRS.2017.2664658http://dx.doi.org/10.1109/TGRS.2017.2664658]

Zhou J, Kwan C, Ayhan B and Eismann M T. 2016. A novel cluster kernel RX algorithm for anomaly and change detection using hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 54(11): 6497-6504 [DOI: 10.1109/TGRS.2016.2585495http://dx.doi.org/10.1109/TGRS.2016.2585495]

Zhou T Y and Tao D C. 2011. GoDec: randomized low-rank and sparse matrix decomposition in noisy case//Proceedings of the 28th International Conference on Machine Learning. Bellevue: Omnipress: 33-40

Zhu L X, Wen G J, Qiu S H and Zhang X. 2019. A hybrid statistics and representation-based anomaly detector for hyperspectral images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(9): 3650-3664 [DOI: 10.1109/JSTARS.2019.2930147http://dx.doi.org/10.1109/JSTARS.2019.2930147]

文章被引用时，请邮件提醒。

提交

机载多维度SAR图像地物数据集构建及融合分类方法

基于深度学习的自监督单目动态场景深度估计综述

基于多尺度注意力机制的PolSAR深度学习超分辨率模型

时空协同的农业种植结构遥感精细制图