{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T05:50:38Z","timestamp":1742968238011,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811912528"},{"type":"electronic","value":"9789811912535"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-981-19-1253-5_22","type":"book-chapter","created":{"date-parts":[[2022,3,23]],"date-time":"2022-03-23T17:03:29Z","timestamp":1648055009000},"page":"293-302","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-feature Fusion Based Deep Forest for Hyperspectral Image Classification"],"prefix":"10.1007","author":[{"given":"Xiaobo","family":"Liu","sequence":"first","affiliation":[]},{"given":"Mostofa Zaman","family":"Mohammad","sequence":"additional","affiliation":[]},{"given":"Chaochao","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Gong","sequence":"additional","affiliation":[]},{"given":"Zhihua","family":"Cai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,24]]},"reference":[{"key":"22_CR1","doi-asserted-by":"publisher","first-page":"147494","DOI":"10.1109\/ACCESS.2020.3015808","volume":"8","author":"Y Tang","year":"2020","unstructured":"Tang, Y., et al.: Apple bruise grading using piecewise nonlinear curve fitting for hyperspectral imaging data. IEEE Access 8, 147494\u2013147506 (2020)","journal-title":"IEEE Access"},{"issue":"2","key":"22_CR2","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1109\/MGRS.2019.2902525","volume":"7","author":"M Shimoni","year":"2019","unstructured":"Shimoni, M., Haelterman, R., Perneel, C.: Hypersectral imaging for military and security applications: combining myriad processing and sensing techniques. IEEE Geosci. Remote Sens. Mag. 7(2), 101\u2013117 (2019)","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"issue":"3","key":"22_CR3","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1109\/TBME.2015.2468578","volume":"63","author":"R Pike","year":"2016","unstructured":"Pike, R., Lu, G., Wang, D., Chen, Z.G., Fei, B.: A Minimum spanning forest-based method for noninvasive cancer detection with hyperspectral imaging. IEEE Trans. Biomed. Eng. 63(3), 653\u2013663 (2016)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"2","key":"22_CR4","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","volume":"4","author":"L Zhang","year":"2016","unstructured":"Zhang, L., Zhang, L., Du, B.: Deep learning for remote sensing data: a technical tutorial on the state of the art. IEEE Geosci. Remote Sens. 4(2), 22\u201340 (2016)","journal-title":"IEEE Geosci. Remote Sens."},{"key":"22_CR5","doi-asserted-by":"crossref","unstructured":"Yang, C., Li, Y., Peng, B., Cheng Y., Tong, L.: Road material information extraction based on multi-feature fusion of remote sensing image. In: 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). pp. 3943\u20133946, IEEE (2019)","DOI":"10.1109\/IGARSS.2019.8899029"},{"key":"22_CR6","doi-asserted-by":"publisher","first-page":"167547","DOI":"10.1109\/ACCESS.2019.2953078","volume":"7","author":"J Lu","year":"2019","unstructured":"Lu, J., Ma, C., Zhou, Y., Luo, M., Zhang, K.: Multi-feature fusion for enhancing image similarity learning. IEEE Access 7, 167547\u2013167556 (2019)","journal-title":"IEEE Access"},{"key":"22_CR7","doi-asserted-by":"crossref","unstructured":"Zhao, S., Nie, W., Zhang, B.: Multi-feature fusion using collaborative residual for hyperspectral palmprint recognition. In: 2018 IEEE 4th International Conference on Computer and Communications (ICCC), Chengdu, China, pp. 1402\u20131406, IEEE (2018)","DOI":"10.1109\/CompComm.2018.8780748"},{"key":"22_CR8","doi-asserted-by":"crossref","unstructured":"He, M., Li, B., Chen, H.: Multi-scale 3D deep convolutional neural network for hyperspectral image classification. In: 2017 IEEE International Conference on Image Processing (ICIP). pp. 3904\u20133908. IEEE (2018)","DOI":"10.1109\/ICIP.2017.8297014"},{"issue":"6","key":"22_CR9","doi-asserted-by":"publisher","first-page":"3235","DOI":"10.1109\/TGRS.2015.2514161","volume":"54","author":"Y Gu","year":"2016","unstructured":"Gu, Y., Liu, T., Jia, X., et al.: Nonlinear multiple kernel learning with multiple-structure-element extended morphological profiles for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 54(6), 3235\u20133247 (2016)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"5","key":"22_CR10","doi-asserted-by":"publisher","first-page":"1638","DOI":"10.1109\/TNNLS.2019.2921564","volume":"31","author":"S Jia","year":"2019","unstructured":"Jia, S., Lin, Z., Deng, B., et al.: Cascade superpixel regularized gabor feature fusion for hyperspectral image classification. IEEE Trans. Neural Netw. Learn. Syst. 31(5), 1638\u20131652 (2019)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"2","key":"22_CR11","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1109\/TGRS.2017.2754511","volume":"56","author":"S Jia","year":"2017","unstructured":"Jia, S., Deng, B., Zhu, J., et al.: Local binary pattern-based hyperspectral image classification with superpixel guidance. IEEE Trans. Geosci. Remote Sens. 56(2), 749\u2013759 (2017)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"7","key":"22_CR12","doi-asserted-by":"publisher","first-page":"4193","DOI":"10.1109\/TGRS.2018.2828612","volume":"56","author":"Z He","year":"2018","unstructured":"He, Z., Li, J., Liu, K., et al.: Kernel low-rank multitask learning in variational mode decomposition domain for multi-hyperspectral classification. IEEE Trans. Geosci. Remote Sens. 56(7), 4193\u20134208 (2018)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"22_CR13","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1016\/j.optlastec.2018.08.044","volume":"110","author":"F Li","year":"2019","unstructured":"Li, F., Wang, J., Lan, R., et al.: Hyperspectral image classification using multi-feature fusion. Opt. Laser Technol. 110, 176\u2013183 (2019)","journal-title":"Opt. Laser Technol."},{"key":"22_CR14","doi-asserted-by":"crossref","unstructured":"Zhang, C., Han, M., Xu, M.: Multi-feature classification of hyperspectral image via probabilistic SVM and guided filter. In: 2018 International Joint Conference on Neural Networks (IJCNN). pp. 1\u20137. IEEE (2018)","DOI":"10.1109\/IJCNN.2018.8489452"},{"issue":"11","key":"22_CR15","doi-asserted-by":"publisher","first-page":"1963","DOI":"10.1109\/LGRS.2019.2957851","volume":"17","author":"X Liu","year":"2019","unstructured":"Liu, X., Yin, X., Cai, Y., et al.: Visual saliency-based extended morphological profiles for unsupervised feature learning of hyperspectral images. IEEE Geosci. Remote Sens. Lett. 17(11), 1963\u20131967 (2019)","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"22_CR16","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Feng, J.: Deep forest: towards an alternative to deep neural networks. In: 2017 International Joint Conference on Artificial Intelligence Organization (IGCAI). pp. 3553\u20133559","DOI":"10.24963\/ijcai.2017\/497"},{"key":"22_CR17","doi-asserted-by":"crossref","unstructured":"Yin, X., Wang, R. et al.: Deep forest-based classification of hyperspectral images. In: Chinese Control Conference, pp. 10367\u201310372, IEEE (2018)","DOI":"10.23919\/ChiCC.2018.8483767"},{"issue":"10","key":"22_CR18","doi-asserted-by":"publisher","first-page":"8169","DOI":"10.1109\/TGRS.2019.2918587","volume":"57","author":"X Liu","year":"2019","unstructured":"Liu, X., Wang, R., Cai, Z., et al.: Deep multigrained cascade forest for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 57(10), 8169\u20138183 (2019). https:\/\/doi.org\/10.1109\/TGRS.2019.2918587","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"22_CR19","doi-asserted-by":"crossref","unstructured":"Luo, H., Tang, Y.Y., Yang, X., et al.: Autoencoder with extended morphological profile for hyperspectral image classification. In: 2017 IEEE International Conference on Cybernetics (CYBCONF). pp. 293\u2013296. IEEE (2017)","DOI":"10.1109\/CYBConf.2017.7985761"},{"key":"22_CR20","doi-asserted-by":"crossref","unstructured":"Zhang, J., Sclaroff, S.: Saliency detection: a Boolean map approach. In: 2013 IEEE International Conference on Computer Vision (ICCV). pp. 153\u2013160. IEEE (2013)","DOI":"10.1109\/ICCV.2013.26"},{"key":"22_CR21","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1007\/11881599_56","volume-title":"Fuzzy Systems and Knowledge Discovery","author":"G Guo","year":"2006","unstructured":"Guo, G., Neagu, D., Huang, X., Bi, Y.: An effective combination of multiple classifiers for toxicity prediction. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds.) Fuzzy Systems and Knowledge Discovery, pp. 481\u2013490. Springer Berlin Heidelberg, Berlin, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11881599_56"},{"issue":"9","key":"22_CR22","doi-asserted-by":"publisher","first-page":"5046","DOI":"10.1109\/TGRS.2018.2805286","volume":"56","author":"L Zhu","year":"2018","unstructured":"Zhu, L., Chen, Y., Ghamisi, P., Benediktsson, J.A.: Generative adversarial networks for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 56(9), 5046\u20135063 (2018)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"2","key":"22_CR23","doi-asserted-by":"publisher","first-page":"847","DOI":"10.1109\/TGRS.2017.2755542","volume":"56","author":"Z Zhong","year":"2018","unstructured":"Zhong, Z., Li, J., Luo, Z., Chapman, M.: Spectral-spatial residual network for hyperspectral image classification: A 3-D deep learning framework. IEEE Trans. Geosci. Remote Sens. 56(2), 847\u2013858 (2018)","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Communications in Computer and Information Science","Bio-Inspired Computing: Theories and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-1253-5_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,23]],"date-time":"2022-03-23T17:06:37Z","timestamp":1648055197000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-1253-5_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811912528","9789811912535"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-1253-5_22","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"24 March 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BIC-TA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Bio-Inspired Computing: Theories and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Taiyuan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bicta2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2021.bicta.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"211","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"67","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"32% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}