{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:38:14Z","timestamp":1760233094625,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T00:00:00Z","timestamp":1671148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Civil Aerospace Project of China","award":["D040102","2021MH0AC01"],"award-info":[{"award-number":["D040102","2021MH0AC01"]}]},{"name":"Key Research Project of Zhejiang Laboratory","award":["D040102","2021MH0AC01"],"award-info":[{"award-number":["D040102","2021MH0AC01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In push-broom hyperspectral imaging systems, the sensor rotation to the optical plane leads to linear spatial misregistration (LSM) in hyperspectral images (HSIs). To compensate for hardware defects through software, this paper develops four methods to detect LSM in HSIs. Different from traditional methods for grayscale images, the method of fitting the sum of abundance (FSAM) and the method of searching for equal abundance (SEAM) are achieved by hyperspectral unmixing for a selected rectangular transition areas containing an edge, which makes good use of spatial and spectral information. The method based on line detection for band-interleaved-by-line (BIL) images (LDBM) and the method based on the Fourier transform of BIL images (FTBM) aim to characterize the slope of line structure in BIL images and get rid of the dependence on scene and wavelength. A full strategy is detailed from aspects of data selection, LSM detection, and image correction. The full spectrum airborne hyperspectral imager (FAHI) is China\u2019s new generation push-broom scanner. The HSIs obtained by FAHI are tested and analyzed. Experiments on simulation data compare the four proposed methods with traditional methods and prove that FSAM outperforms other methods in terms of accuracy and stability. In experiments on real data, the application of the full strategy on FAHI verifies its effectiveness. This work not only provides reference for other push-broom imagers with similar problems, but also helps to reduce the requirement for hardware calibration.<\/jats:p>","DOI":"10.3390\/s22249932","type":"journal-article","created":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T09:31:01Z","timestamp":1671442261000},"page":"9932","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Linear Spatial Misregistration Detection and Correction Based on Spectral Unmixing for FAHI Hyperspectral Imagery"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6814-3903","authenticated-orcid":false,"given":"Xiangyue","family":"Zhang","sequence":"first","affiliation":[{"name":"Key Laboratory of Space Active Opto-Electronics Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyu","family":"Cheng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Space Active Opto-Electronics Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianru","family":"Xue","sequence":"additional","affiliation":[{"name":"Key Laboratory of Space Active Opto-Electronics Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0791-4836","authenticated-orcid":false,"given":"Yueming","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Space Active Opto-Electronics Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China"},{"name":"Research Center for Intelligent Sensing Systems, Zhejiang Laboratory, Hangzhou 311100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103115","DOI":"10.1016\/j.infrared.2019.103115","article-title":"Status and application of advanced airborne hyperspectral imaging technology: A review","volume":"104","author":"Jia","year":"2020","journal-title":"Infrared Phys. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1388","DOI":"10.1109\/TGRS.2003.812908","article-title":"Comparison of airborne hyperspectral data and EO-1 Hyperion for mineral mapping","volume":"41","author":"Kruse","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/S0034-4257(02)00196-7","article-title":"Spectral discrimination of vegetation types in a coastal wetland","volume":"85","author":"Schmidt","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2759","DOI":"10.1080\/01431160802555820","article-title":"Broadleaf species recognition with in situ hyperspectral data","volume":"30","author":"Pu","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"033513","DOI":"10.1117\/1.3098964","article-title":"Misregistration impacts on hyperspectral target detection","volume":"3","author":"Casey","year":"2009","journal-title":"J. Appl. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4568","DOI":"10.1364\/AO.49.004568","article-title":"Detection and correction of spectral and spatial misregistrations for hyperspectral data using phase correlation method","volume":"49","author":"Naoto","year":"2010","journal-title":"Appl. Opt."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"808","DOI":"10.1117\/1.602431","article-title":"Pushbroom imaging spectrometer with high spectroscopic data fidelity: Experimental demonstration","volume":"39","author":"Mourouloulis","year":"2000","journal-title":"Opt. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wei, L., Yuan, L., Li, C., Lv, G., Xie, F., Han, G., Shu, R., and Wang, J. (2016, January 9\u201311). New generation VNIR\/SWIR\/TIR airborne imaging spectrometer. Proceedings of the International Symposium on Optoelectronic Technology and Application 2016, Beijing, China.","DOI":"10.1117\/12.2245541"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, D., Yuan, L., Wang, S., Yu, H., Zhang, C., He, D., Han, G., Wang, J., and Wang, Y. (2019). Wide swath and high resolution airborne hyperspectral imaging system and flight validation. Sensors, 19.","DOI":"10.3390\/s19071667"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yokoya, N., Miyamura, N., and Iwasaki, A. (2010, January 11\u201314). Preprocessing of hyperspectral imagery with consideration of smile and keystone properties. Proceedings of the SPIE: Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications III, Incheon, Republic of Korea.","DOI":"10.1117\/12.870437"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"053102","DOI":"10.1117\/1.OE.54.5.053102","article-title":"Method for quantifying image quality in push-broom hyperspectral cameras","volume":"54","author":"Hoye","year":"2015","journal-title":"Opt. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"084103","DOI":"10.1117\/1.OE.59.8.084103","article-title":"Spatial misregistration in hyperspectral cameras: Lab characterization and impact on data quality in real-world images","volume":"59","author":"Hoye","year":"2020","journal-title":"Opt. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1364\/AO.37.000683","article-title":"Spectral calibration requirement for Earth-looking imaging spectrometers in the solar-reflected spectrum","volume":"37","author":"Green","year":"1998","journal-title":"Appl. Opt."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2210","DOI":"10.1364\/AO.39.002210","article-title":"Design of pushbroom imaging spectrometers for optimum recovery of spectroscopic and spatial information","volume":"39","author":"Mouroulis","year":"2000","journal-title":"Appl. Opt."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2803","DOI":"10.1364\/AO.46.002803","article-title":"Scene-based method for spatial misregistration detection in hyperspectral imagery","volume":"46","author":"Nieke","year":"2007","journal-title":"Appl. Opt."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Neville, R., Sun, L., and Staenz, K. (2004, January 12\u201316). Detection of keystone in imaging spectrometer data. Proceedings of the SPIE: Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X, Orlando, FL, USA.","DOI":"10.1117\/12.542806"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1109\/TGRS.2020.3002461","article-title":"Detection of subpixel targets on hyperspectral remote sensing imagery based on background endmember extraction","volume":"59","author":"Song","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"015022","DOI":"10.1117\/1.JRS.12.015022","article-title":"Improving hyperspectral subpixel target detection using hybrid detection space","volume":"12","author":"Li","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6883","DOI":"10.1080\/01431161.2013.810353","article-title":"Analysis of spatial distribution pattern of change-detection error caused by misregistration","volume":"34","author":"Shi","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1109\/TMI.2002.808359","article-title":"A subspace identification extension to the phase correlation method","volume":"22","author":"Hoge","year":"2003","journal-title":"IEEE Trans. Med. Imag."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Nagashima, S., Aoki, T., Higuchi, T., and Kobayashi, K. (2005, January 12\u201315). A subpixel image matching technique using phase-only correlation. Proceedings of the 2006 International Symposium on Intelligent Signal Processing and Communications, Yonago, Japan.","DOI":"10.1109\/ISPACS.2006.364751"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.rse.2010.08.012","article-title":"Sub-pixel precision image matching for measuring surface displacements on mass movements using normalized cross-correlation","volume":"115","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_23","first-page":"171","article-title":"Phase correlation based image alignment with subpixel accuracy","volume":"Volume 7629","author":"Batyrshin","year":"2012","journal-title":"Advances in Artificial Intelligence"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1558","DOI":"10.1016\/j.jvcir.2014.07.001","article-title":"Gradient-based subspace phase correlation for fast and effective image alignment","volume":"25","author":"Ren","year":"2014","journal-title":"J. Vis. Commun. Image R."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4044","DOI":"10.1364\/AO.49.004044","article-title":"Study of the performance of different subpixel image correlation methods in 3D digital image correlation","volume":"49","author":"Hu","year":"2010","journal-title":"Appl. Opt."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1080\/07038992.2019.1573137","article-title":"A sub-pixel multiple change detection approach for hyperspectral imagery","volume":"44","author":"Hasanlou","year":"2018","journal-title":"Can. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1080\/17538947.2010.550937","article-title":"Mapping alteration minerals using sub-pixel unmixing of ASTER data in the Sarduiyeh area, SE Kerman, Iran","volume":"4","author":"Hosseinjani","year":"2011","journal-title":"Int. J. Digit. Earth"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"6763","DOI":"10.1109\/TGRS.2018.2842748","article-title":"A new spectral-spatial sub-pixel mapping model for remotely sensed hyperspectral imagery","volume":"56","author":"Xu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3434","DOI":"10.1109\/TGRS.2010.2046671","article-title":"Spatial purity based endmember extraction for spectral mixture analysis","volume":"48","author":"Mei","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1109\/MSP.2013.2279177","article-title":"Endmember variability in hyperspectral analysis: Addressing spectral variability during spectral unmixing","volume":"31","author":"Zare","year":"2014","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1109\/JSTARS.2012.2194696","article-title":"Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches","volume":"5","author":"Plaza","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/79.974727","article-title":"Spectral unmixing","volume":"19","author":"Keshava","year":"2002","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3735403","DOI":"10.1155\/2020\/3735403","article-title":"An overview on linear unmixing of hyperspectral data","volume":"2020","author":"Wei","year":"2020","journal-title":"Math. Probl. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"15366","DOI":"10.1364\/OE.16.015366","article-title":"Mitigation of spectral mis-registration effects in imaging spectrometers via cubic spline interpolation","volume":"16","author":"Feng","year":"2008","journal-title":"Opt. Express."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4077","DOI":"10.1109\/TGRS.2018.2889731","article-title":"Destriping algorithms based on statistics and spatial filtering for visible-to-thermal infrared pushbroom hyperspectral imagery","volume":"57","author":"Jia","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Liu, H., Zhang, D., and Wang, Y. (2019). Preflight spectral calibration of airborne shortwave infrared hyperspectral imager with water vapor absorption characteristics. Sensors, 19.","DOI":"10.3390\/s19102259"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3071","DOI":"10.1109\/TGRS.2019.2947333","article-title":"Hyperspectral image denoising with total variation regularization and nonlocal low-rank tensor decomposition","volume":"58","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1940","DOI":"10.1109\/TGRS.2017.2771155","article-title":"Joint spatial and spectral low-rank regularization for hyperspectral image denoising","volume":"56","author":"Xue","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Xue, T., Wang, Y., Chen, Y., Jia, J., Wen, M., Guo, R., Wu, T., and Deng, X. (2021). Mixed noise estimation model for optimized kernel minimum noise fraction transformation in hyperspectral image dimensionality reduction. Remote Sens., 13.","DOI":"10.3390\/rs13132607"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/24\/9932\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:42:53Z","timestamp":1760146973000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/24\/9932"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,16]]},"references-count":39,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["s22249932"],"URL":"https:\/\/doi.org\/10.3390\/s22249932","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,12,16]]}}}