{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T11:48:54Z","timestamp":1772020134572,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,9,24]],"date-time":"2023-09-24T00:00:00Z","timestamp":1695513600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"social service project completed in collaboration between Beijing Dadi Kaiyuan Geology Engineering Co., Ltd. and China University of Geosciences (Beijing)","award":["H02691"],"award-info":[{"award-number":["H02691"]}]},{"name":"social service project completed in collaboration between Beijing Dadi Kaiyuan Geology Engineering Co., Ltd. and China University of Geosciences (Beijing)","award":["H02697"],"award-info":[{"award-number":["H02697"]}]},{"name":"State Administration of Science, Technology and Industry for National Defence, PRC, Subproject of major projects","award":["H02691"],"award-info":[{"award-number":["H02691"]}]},{"name":"State Administration of Science, Technology and Industry for National Defence, PRC, Subproject of major projects","award":["H02697"],"award-info":[{"award-number":["H02697"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The influence of some seemingly anomalous samples on modeling is often ignored in the quantitative prediction of soil composition modeling with hyperspectral data. Soil spectral transformation based on wavelet packet technology only performs pruning and threshold filtering based on experience. The feature bands selected by the Pearson correlation coefficient method often have high redundancy. To solve these problems, this paper carried out a study of the prediction of soil total iron composition based on a new method. First, regarding the problem of abnormal samples, the Monte Carlo method based on particle swarm optimization (PSO) is used to screen abnormal samples. Second, feature representation based on Shannon entropy is adopted for wavelet packet processing. The amount of information held by the wavelet packet node is used to decide whether to cut the node. Third, the feature bands selected based on the correlation coefficient and the competitive adaptive reweighted sampling (CARS) algorithm using the least squares support vector regression (LSSVR) are applied to the soil spectra before and after wavelet packet processing. Finally, the Fe content was calculated based on a 1D convolutional neural network (1D-CNN). The results show that: (1) The Monte Carlo method based on particle swarm optimization and modeling multiple times was able to handle the abnormal samples. (2) Based on the Shannon entropy wavelet packet transformation, simple operations could simultaneously preserve the spectral information while removing high-frequency noise from the spectrum, effectively improving the correlation between soil spectra and content. (3) The 1D-CNN with added residual blocks could also achieve better results in soil hyperspectral modeling with few samples.<\/jats:p>","DOI":"10.3390\/rs15194681","type":"journal-article","created":{"date-parts":[[2023,9,24]],"date-time":"2023-09-24T10:48:31Z","timestamp":1695552511000},"page":"4681","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Research on Hyperspectral Modeling of Total Iron Content in Soil Applying LSSVR and CNN Based on Shannon Entropy Wavelet Packet Transform"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3392-6582","authenticated-orcid":false,"given":"Weichao","family":"Liu","sequence":"first","affiliation":[{"name":"School of Geosciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5843-3106","authenticated-orcid":false,"given":"Hongyuan","family":"Huo","sequence":"additional","affiliation":[{"name":"Faculty of Architecture, Transportation and Civil Engineering, Beijing University of Technology, Beijing 100124, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2120-434X","authenticated-orcid":false,"given":"Ping","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Geosciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China"}]},{"given":"Mingyue","family":"Li","sequence":"additional","affiliation":[{"name":"School of Geosciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China"}]},{"given":"Yuzhen","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Geosciences and Resources, China University of Geosciences (Beijing), Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,24]]},"reference":[{"key":"ref_1","first-page":"F4","article-title":"Mapping iron oxides and the color of Australian soil using visible\u2013near-infrared reflectance spectra","volume":"115","author":"Bui","year":"2010","journal-title":"J. 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