|
|
|
|
|
|
Hyperspectral Unmixing Based on Deep Stacked Autoencoders Network |
ZHU Ling, QIN Kai*, SUN Yu, LI Ming, ZHAO Ying-jun |
National Key Laboratory of Remote Sensing Information and Imagery Analyzing Technology, Beijing Research Institute of Uranium Geology, Beijing 100029, China
|
|
|
Abstract With the launch of high-resolution series satellites and the development of UAV hyperspectral, the available hyperspectral data are further expanded. Hyperspectral unmixing is a crucial task to improve hyperspectral images’ fine utilization value. With the rapid development of computer and artificial intelligence technology, deep learning theory has been introduced into the image processing field. The autoencoder network has been taken into hyperspectral unmixing because of its great feature extraction ability. This study improves the autoencoder structure and proposes a deep stack autoencoder network (DSAE) for hyperspectral image unmixing. The network consists of endmember extraction (EDSAE) and abundance estimation(ADSAE). Firstly, the EDSAE network is constructed by adding batch normalization, sparse constraint, “sum-to-one” constraint and deleting the bias term. Then unsupervised training is carried out for endmember extraction. Secondly, the obtained endmember spectral data are enhanced based on the HAPKE and LINEAR models. Finally, the supervised training network ADSAE is constructed based on the original stack autoencoder network, and the activation function of the last layer is set as the Softmax function. The simulated dataset is used as a training set, and the hyperspectral images are used as a test set. Based on the DSAE method proposed in this study, end member extraction and abundance estimation are carried out on three hyperspectral images, including Samson, Jasper Ridge and Urban. The results are compared with those obtained by the traditional methods N-FINDR, VCA, MVC-NMF and other deep learning methods SNSA and EndNet. The experimental results show that theD SAE method has obvious advantages over the other five methods in endmember extraction for the three real hyperspectral data set. It also shows the best abundance estimation results based on the synthetic datasets generated by the HAPKE mixing model. The DSAE method has good stability and robustness, which provides a new idea for the quantitative analysis and utilization of hyperspectral images.
|
Received: 2022-03-08
Accepted: 2022-06-06
|
|
Corresponding Authors:
QIN Kai
E-mail: h_rs_qk@163.com
|
|
[1] TONG Qing-xi, ZHANG Bing, ZHANG Li-fu(童庆禧, 张 兵, 张立福). National Remote Sensing Bulletin(遥感学报), 2016, 20(5): 689.
[2] CHEN Jin, MA Lei, CHEN Xue-hong, et al(陈 晋, 马 磊, 陈学泓, 等). National Remote Sensing Bulletin(遥感学报), 2016, 20(5): 1102.
[3] YAN Yang, HUA Wen-shen, ZHANG Yan, et al(严 阳, 华文深, 张 炎, 等). Laser Technology(激光技术), 2019, 43(4): 574.
[4] Liu Rong, Du Bo, Zhang Liangpei. Journal of Applied Remote Sensing, 2014, 8(1): 085093-1.
[5] Pu Hanye, Chen Zhao, Wang Bin, et al. IEEE Geoscience and Remote Sensing, 2015, 53(3): 1287.
[6] Miao Lida, Qi Hairong. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(3): 765.
[7] Su Yuanchao, Li Jun, Plaza A, et al. IEEE Geoscience and Remote Sensing Letters, 2018, 15(9): 1427.
[8] Su Yuanchao, Li Jun, Plaza A, et al. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(7): 4309.
[9] Ozkan S, Kaya B and Akar G B. IEEE Transactions on Geoscience and Remote Sensing, 2019,57(1): 482.
[10] Palsson B, Ulfarsson M O,Sveinsson J R. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(1): 535.
[11] HAN Zhu,GAO Lian-ru,ZHANG Bing,et al(韩 竹, 高连如, 张 兵, 等). National Remote Sensing Bulletin(遥感学报),2020, 24(4): 388.
[12] ZHU Ling, QIN Kai, LI Ming, et al(朱 玲, 秦 凯, 李 明, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2021, 41(4): 1288.
[13] Qin Kai, Ge Fangyuan, Zhao Yingjun, et al. IEEE Geoscience and Remote Sensing Letters, 2021, 18(5): 886.
|
[1] |
SONG Ruo-xi1, 3, FENG Yi-ning3, CHENG Wei2, WANG Xiang-hai2, 3*. Advance in Hyperspectral Images Change Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2354-2362. |
[2] |
WANG Jin-hua, DAI Jia-le*, LI Meng-qian, LIU Wei, MIAO Ruo-fan. Blind Separation Algorithm of Mixed Minerals Hyperspectral Base on NMF Mode[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2458-2466. |
[3] |
LI Zhi1, WANG Xia1*, XU Can1*, LI Peng2, HUO Yu-rong1, FU Jing-yu1, WANG Pei1, FENG Fei3. Review of Spectral Characterization and Identification of Unresolved Space Objects[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1329-1339. |
[4] |
KONG Yu-ru1, 2, WANG Li-juan1*, FENG Hai-kuan2, XU Yi1, LIANG Liang1, XU Lu1, YANG Xiao-dong2*, ZHANG Qing-qi1. Leaf Area Index Estimation Based on UAV Hyperspectral Band Selection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(03): 933-939. |
[5] |
XU Zhao-jin, LI Dong-liang, SHEN Li*. Study on Diffuse Reflection and Absorption Spectra of Organic and Inorganic Chinese Painting Pigments[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(12): 3915-3921. |
[6] |
YANG Bao-hua, GAO Zhi-wei, QI Lin, ZHU Yue, GAO Yuan. Prediction Model of Soluble Solid Content in Peaches Based on Hyperspectral Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(11): 3559-3564. |
[7] |
LI Tian-zi2, LIU Shan-jun1*, SONG Liang3, WANG Dong1, HUANG Jian-wei4, YU Mo-li1. Experimental Study on the Effect of Observation Angle on Thermal Infrared Spectral Unmixing of Rock[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(06): 1769-1774. |
[8] |
ZHU Ling, QIN Kai*, LI Ming,ZHAO Ying-jun. Research on Improved Stacked Sparse Autoencoders for Mineral Hyperspectral Endmember Extraction[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(04): 1288-1293. |
[9] |
LI Yang-yu1, MA Jian-guang2*, LI Da-cheng1, CUI Fang-xiao1, WANG An-jing1, WU Jun1. Research on Spatial Offset Raman Spectroscopy and Data Processing Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(01): 71-74. |
[10] |
WANG Jin-hua1, CAO Lan-jie1, XU Guo-qiang2*, FENG Xiao-xin3, WU Bing1, ZHANG Bo1. Research on Hyper-Spectral Test of Concrete Corrosion Product under Sodium Sulfate Attack[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(06): 1724-1730. |
[11] |
YI Li-na1, XU Xiao1, ZHANG Gui-feng2,3*, MING Xing2, GUO Wen-ji2, LI Shao-cong1, SHA Ling-yu1. Light and Small UAV Hyperspectral Image Mosaicking[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(06): 1885-1891. |
[12] |
GAN Yu-quan1, 2, LIU Wei-hua1, FENG Xiang-peng1, YU Tao1, HU Bing-liang1, WEN De-sheng1. A Sparse Constrained Graph Regularized Nonnegative Matrix Factorization Algorithm for Hyperspectral Unmixing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(04): 1118-1127. |
[13] |
LI Tian-zi2, 3,LIU Shan-jun1, 2*. A Study on the Effects of Roughness on Thermal Infrared Spectral Unmixing of Rock[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(10): 3051-3057. |
[14] |
WANG Cai-ling1, 2, WANG Hong-wei3, HU Bing-liang1, WEN Jia4, XU Jun5, LI Xiang-juan2 . A Novel Spatial-Spectral Sparse Representation for Hyperspectral Image Classification Based on Neighborhood Segmentation [J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(09): 2919-2924. |
[15] |
WANG Cai-ling1,2, WANG Hong-wei3, HU Bing-liang1, WEN Jia4, XU Jun5,LI Xiang-juan2 . A New Spectral-Spatial Algorithm Method for Hyperspectral Image Target Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(04): 1163-1169. |
|
|
|
|