{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T08:48:19Z","timestamp":1765961299145,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2020,9,2]],"date-time":"2020-09-02T00:00:00Z","timestamp":1599004800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Relying on large scale labeled datasets, deep learning has achieved good performance in image classification tasks. In agricultural and biological engineering, image annotation is time-consuming and expensive. It also requires annotators to have technical skills in specific areas. Obtaining the ground truth is difficult because natural images are expensive. In addition, images in these areas are usually stored as multichannel images, such as computed tomography (CT) images, magnetic resonance images (MRI), and hyperspectral images (HSI). In this paper, we present a framework using active learning and deep learning for multichannel image classification. We use three active learning algorithms, including least confidence, margin sampling, and entropy, as the selection criteria. Based on this framework, we further introduce an \u201cimage pool\u201d to make full advantage of images generated by data augmentation. To prove the availability of the proposed framework, we present a case study on agricultural hyperspectral image classification. The results show that the proposed framework achieves better performance compared with the deep learning model. Manual annotation of all the training sets achieves an encouraging accuracy. In comparison, using active learning algorithm of entropy and image pool achieves a similar accuracy with only part of the whole training set manually annotated. In practical application, the proposed framework can remarkably reduce labeling effort during the model development and upadting processes, and can be applied to multichannel image classification in agricultural and biological engineering.<\/jats:p>","DOI":"10.3390\/s20174975","type":"journal-article","created":{"date-parts":[[2020,9,2]],"date-time":"2020-09-02T09:29:28Z","timestamp":1599038968000},"page":"4975","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Active Learning Plus Deep Learning Can Establish Cost-Effective and Robust Model for Multichannel Image: A Case on Hyperspectral Image Classification"],"prefix":"10.3390","volume":"20","author":[{"given":"Fangyu","family":"Shi","sequence":"first","affiliation":[{"name":"Institute of Image Communication and Information Processing, Shanghai Jiao Tong University, Shanghai 200240, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3550-3287","authenticated-orcid":false,"given":"Zhaodi","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Image Communication and Information Processing, Shanghai Jiao Tong University, Shanghai 200240, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8557-8930","authenticated-orcid":false,"given":"Menghan","family":"Hu","sequence":"additional","affiliation":[{"name":"Institute of Image Communication and Information Processing, Shanghai Jiao Tong University, Shanghai 200240, China"},{"name":"Shanghai Key Laboratory of Multidimensional Information Processing, East China Normal University, Shanghai 200241, China"},{"name":"Key Laboratory of Artificial Intelligence, Ministry of Education, Shanghai 200240, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8165-9322","authenticated-orcid":false,"given":"Guangtao","family":"Zhai","sequence":"additional","affiliation":[{"name":"Institute of Image Communication and Information Processing, Shanghai Jiao Tong University, Shanghai 200240, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_2","unstructured":"Krasin, I., Duerig, T., Alldrin, N., Ferrari, V., Abu-El-Haija, S., Kuznetsova, A., Rom, H., Uijlings, J., Popov, S., and Veit, A. (2020, July 05). OpenImages: A Public Dataset for Large-Scale Multi-Label and Multi-Class Image Classification. Available online: https:\/\/github.com\/openimages."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","article-title":"The pascal visual object classes challenge: A retrospective","volume":"111","author":"Everingham","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_4","unstructured":"Settles, B. (2009). Active Learning Literature Survey, University of Wisconsin-Madison. Computer Sciences Technical Report 1648."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lewis, D.D., and Gale, W.A. (1994, January 3\u20136). A sequential algorithm for training text classifiers. Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Dublin, Ireland.","DOI":"10.1007\/978-1-4471-2099-5_1"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Lewis, D.D., and Catlett, J. (1994). Heterogeneous uncertainty sampling for supervised learning. Machine Learning Proceedings 1994, Elsevier.","DOI":"10.1016\/B978-1-55860-335-6.50026-X"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Scheffer, T., Decomain, C., and Wrobel, S. (2001). Active hidden markov models for information extraction. International Symposium on Intelligent Data Analysis, Springer.","DOI":"10.1007\/3-540-44816-0_31"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1145\/584091.584093","article-title":"A mathematical theory of communication","volume":"5","author":"Shannon","year":"2001","journal-title":"ACM SIGMOBILE Mob. Comput. Commun. Rev."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Seung, H.S., Opper, M., and Sompolinsky, H. (1992, January 27\u201329). Query by committee. Proceedings of the Fifth Annual Workshop on Computational Learning Theory, Pittsburgh, PA, USA.","DOI":"10.1145\/130385.130417"},{"key":"ref_10","unstructured":"Mamitsuka, N., and Abe, H. (1998). Query learning strategies using boosting and bagging. Machine Learning: Proceedings of the Fifteenth International Conference (ICML98), Madison, WI, USA, 24\u201327 July 1998, Morgan Kaufmann Pub."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1036","DOI":"10.1109\/JSTARS.2014.2302332","article-title":"A batch-mode active learning algorithm using region-partitioning diversity for SVM classifier","volume":"7","author":"Huo","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_12","first-page":"4085","article-title":"Semisupervised hyperspectral image segmentation using multinomial logistic regression with active learning","volume":"48","author":"Li","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4071","DOI":"10.1109\/TGRS.2012.2187906","article-title":"Active learning methods for biophysical parameter estimation","volume":"50","author":"Pasolli","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Md Noor, S.S., Ren, J., Marshall, S., and Michael, K. (2017). Hyperspectral Image Enhancement and Mixture Deep-Learning Classification of Corneal Epithelium Injuries. Sensors, 17.","DOI":"10.3390\/s17112644"},{"key":"ref_15","unstructured":"Cen, H., He, Y., and Lu, R. (2016, January 17\u201320). Hyperspectral imaging-based surface and internal defects detection of cucumber via stacked sparse auto-encoder and convolutional neural network. Proceedings of the 2016 ASABE Annual International Meeting, Orlando, FL, USA."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"258619","DOI":"10.1155\/2015\/258619","article-title":"Deep convolutional neural networks for hyperspectral image classification","volume":"2015","author":"Hu","year":"2015","journal-title":"J. Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1109\/TGRS.2016.2616355","article-title":"Hyperspectral image classification using deep pixel-pair features","volume":"55","author":"Li","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, D., and Shang, Y. (2014, January 6\u201311). A new active labeling method for deep learning. Proceedings of the 2014 International Joint Conference on Neural Networks (IJCNN), Beijing, China.","DOI":"10.1109\/IJCNN.2014.6889457"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2591","DOI":"10.1109\/TCSVT.2016.2589879","article-title":"Cost-effective active learning for deep image classification","volume":"27","author":"Wang","year":"2017","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_20","unstructured":"Sener, O., and Savarese, S. (May, January 30). Active Learning for Convolutional Neural Networks: A Core-Set Approach. Proceedings of the International Conference on Learning Representations, Vancouver, BC, Canada."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1016\/j.neucom.2013.04.017","article-title":"Active deep learning method for semi-supervised sentiment classification","volume":"120","author":"Zhou","year":"2013","journal-title":"Neurocomputing"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1109\/TPAMI.2017.2652459","article-title":"Active self-paced learning for cost-effective and progressive face identification","volume":"40","author":"Lin","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Shin, J., Zhang, L., Gurudu, S., Gotway, M., and Liang, J. (2017, January 21\u201326). Fine-tuning convolutional neural networks for biomedical image analysis: Actively and incrementally. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.506"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1109\/JSTARS.2016.2598859","article-title":"Active deep learning for classification of hyperspectral images","volume":"10","author":"Liu","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.ins.2016.01.082","article-title":"Deep learning approach for active classification of electrocardiogram signals","volume":"345","author":"Bazi","year":"2016","journal-title":"Inf. Sci."},{"key":"ref_26","unstructured":"Zhang, M., and Li, C. (2016, January 17\u201320). Blueberry bruise detection using hyperspectral transmittance imaging. Proceedings of the 2016 ASABE Annual International Meeting, Orlando, FL, USA."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.compag.2016.01.015","article-title":"Classification and characterization of blueberry mechanical damage with time evolution using reflectance, transmittance and interactance imaging spectroscopy","volume":"122","author":"Hu","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, Z., Hu, M., and Zhai, G. (2018). Application of Deep Learning Architectures for Accurate and Rapid Detection of Internal Mechanical Damage of Blueberry Using Hyperspectral Transmittance Data. Sensors, 18.","DOI":"10.3390\/s18041126"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.postharvbio.2015.03.014","article-title":"Estimating blueberry mechanical properties based on random frog selected hyperspectral data","volume":"106","author":"Hu","year":"2015","journal-title":"Postharvest Biol. Technol."},{"key":"ref_30","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/17\/4975\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:05:56Z","timestamp":1760177156000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/17\/4975"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,2]]},"references-count":30,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["s20174975"],"URL":"https:\/\/doi.org\/10.3390\/s20174975","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,9,2]]}}}