{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:11:12Z","timestamp":1760145072379,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T00:00:00Z","timestamp":1718755200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62006246","959202413100"],"award-info":[{"award-number":["62006246","959202413100"]}]},{"name":"Xi\u2019an Association for Science and Technology Youth Support Program","award":["62006246","959202413100"],"award-info":[{"award-number":["62006246","959202413100"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Currently, complex scene classification strategies are limited to high-definition image scene sets, and low-quality scene sets are overlooked. Although a few studies have focused on artificially noisy images or specific image sets, none have involved actual low-resolution scene images. Therefore, designing classification models around practicality is of paramount importance. To solve the above problems, this paper proposes a two-stage classification optimization algorithm model based on MPSO, thus achieving high-precision classification of low-quality scene images. Firstly, to verify the rationality of the proposed model, three groups of internationally recognized scene datasets were used to conduct comparative experiments with the proposed model and 21 existing methods. It was found that the proposed model performs better, especially in the 15-scene dataset, with 1.54% higher accuracy than the best existing method ResNet-ELM. Secondly, to prove the necessity of the pre-reconstruction stage of the proposed model, the same classification architecture was used to conduct comparative experiments between the proposed reconstruction method and six existing preprocessing methods on the seven self-built low-quality news scene frames. The results show that the proposed model has a higher improvement rate for outdoor scenes. Finally, to test the application potential of the proposed model in outdoor environments, an adaptive test experiment was conducted on the two self-built scene sets affected by lighting and weather. The results indicate that the proposed model is suitable for weather-affected scene classification, with an average accuracy improvement of 1.42%.<\/jats:p>","DOI":"10.3390\/s24123983","type":"journal-article","created":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T11:47:17Z","timestamp":1718797637000},"page":"3983","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploration of MPSO-Two-Stage Classification Optimization Model for Scene Images with Low Quality and Complex Semantics"],"prefix":"10.3390","volume":"24","author":[{"given":"Kexin","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Information Engineering, Engineering University of PAP, Xi\u2019an 710086, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rong","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Engineering University of PAP, Xi\u2019an 710086, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoou","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Engineering University of PAP, Xi\u2019an 710086, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaobing","family":"Deng","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Engineering University of PAP, Xi\u2019an 710086, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingchao","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, Engineering University of PAP, Xi\u2019an 710086, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,19]]},"reference":[{"key":"ref_1","first-page":"10","article-title":"A Study on Image Posting Behaviors on Social Media Platforms","volume":"40","author":"Deng","year":"2024","journal-title":"Book Inf. Sci. Knowl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.neucom.2021.01.085","article-title":"Image scene geometry recognition using low-level features fusion at multi-layer deep CNN","volume":"440","author":"Khan","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_3","first-page":"1782","article-title":"TEXNET: A deep convolutional neural network model to recognize text in natural scene images","volume":"16","author":"Kavitha","year":"2021","journal-title":"J. Eng. Sci. Technol."},{"key":"ref_4","first-page":"101","article-title":"Discussion on the degree of influence on the performance of scene semantic classification after applying SRCNN reconstruction model","volume":"Volume 12175","author":"Kexin","year":"2022","journal-title":"Proceedings of the International Conference on Network Communication and Information Security (ICNIS 2021)"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Dodge, S., and Karam, L. (2016, January 6\u20138). Understanding how image quality affects deep neural networks. Proceedings of the 2016 Eighth International Conference on Quality of Multimedia Experience (QoMEX), Lisbon, Portugal.","DOI":"10.1109\/QoMEX.2016.7498955"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ye, A., Zhou, X., and Miao, F. (2022). Innovative Hyperspectral Image Classification Approach Using Optimized CNN and ELM. Electronics, 11.","DOI":"10.3390\/electronics11050775"},{"key":"ref_7","unstructured":"da Costa, G.B.P., Contato, W.A., Nazare, T.S., Neto, J.E., and Ponti, M. (2016). An empirical study on the effects of different types of noise in image classification tasks. arXiv."},{"key":"ref_8","first-page":"102577","article-title":"DRSNet: Novel architecture for small patch and low-resolution remote sensing image scene classification","volume":"104","author":"Fc","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_9","unstructured":"Hendrycks, D., and Dietterich, T. (2019). Benchmarking Neural Network Robustness to Common Corruptions and Perturbations. arXiv."},{"key":"ref_10","unstructured":"Jian, Z. (2020). A Target Classification Method for Complex Scenarios. [Ph.D. Thesis, Hangzhou University of Electronic Science and Technology]."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1016\/j.neucom.2017.08.062","article-title":"A hybrid deep learning CNN\u2013ELM for age and gender classification","volume":"275","author":"Duan","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1109\/LPT.2023.3243392","article-title":"Low Image Contrast Detection in a Bright Light Interference HDR Scene Using Smart CAOS Camera","volume":"35","author":"Riza","year":"2023","journal-title":"IEEE Photon-Technol. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, Y., Cao, Y., Zha, Z.J., Zhang, J., and Xiong, Z. (2020, January 13\u201319). Deep Degradation Prior for Low-Quality Image Classification. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01106"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"117008","DOI":"10.1016\/j.image.2023.117008","article-title":"No-reference blurred image quality assessment method based on structure of structure features","volume":"118","author":"Chen","year":"2023","journal-title":"Signal Process. Image Commun."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1023\/A:1011139631724","article-title":"Modeling the shape of the scene: A holistic representation of the spatial envelope","volume":"42","author":"Oliva","year":"2001","journal-title":"Int. J. Comput. Vis."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.ins.2016.02.021","article-title":"Scene classification using local and global features with collaborative representation fusion","volume":"348","author":"Zou","year":"2016","journal-title":"Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Khan, A., Chefranov, A., and Demirel, H. (2020). Image-level structure recognition using image features, templates, and ensemble of classifiers. Symmetry, 12.","DOI":"10.3390\/sym12071072"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Janowski, \u0141., Pydyn, A., Popek, M., Gajewski, J., and Gmi\u0144ska-Nowak, B. (2024). Towards better differentiation of archaeological objects based on geomorphometric features of a digital elevation model, the case of the Old Oder Canal. Archaeol. Prospect., 1\u201312.","DOI":"10.1002\/arp.1927"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"8261","DOI":"10.1007\/s00521-020-04961-0","article-title":"Indoor scene segmentation algorithm based on full convolutional neural network","volume":"33","author":"Zhu","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.neucom.2021.09.002","article-title":"Geometric and Semantic Analysis of Road Image Sequences for Traffic Scene Construction","volume":"465","author":"Li","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"102840","DOI":"10.1016\/j.ipm.2021.102840","article-title":"A scene segmentation algorithm combining the body and the edge of the object","volume":"59","author":"Ou","year":"2022","journal-title":"Inf. Process. Manag."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.neucom.2019.01.090","article-title":"A novel scene classification model combining ResNet based transfer learning and data augmentation with a filter","volume":"338","author":"Liu","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.neucom.2016.11.023","article-title":"G-MS2F: GoogLeNet based multi-stage feature fusion of deep CNN for scene recognition","volume":"225","author":"Tang","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. (2016, January 12\u201317). Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning. Proceedings of the AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lu, K., Cheng, J., Li, H., and Ouyang, T. (2023). MFAFNet: A Lightweight and Efficient Network with Multi-Level Feature Adaptive Fusion for Real-Time Semantic Segmentation. Sensors, 23.","DOI":"10.3390\/s23146382"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1007\/s42979-021-00906-z","article-title":"Comparative Analysis of Diferent Classifcation Techniques","volume":"3","author":"Shetty","year":"2022","journal-title":"SN Comput. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"100863","DOI":"10.1016\/j.swevo.2021.100863","article-title":"Hybrid MPSO-CNN: Multi-level Particle Swarm optimized Hyperparameters of Convolutional Neural Network","volume":"63","author":"Singh","year":"2021","journal-title":"Swarm Evol. Comput."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., and Lo, W.-Y. (2023, January 1\u20136). Segment Anything. Proceedings of the 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Shen, Z., and Jiao, R. (2024). Segment anything model for medical image segmentation: Current applications and future directions. Comput. Biol. Med., 171.","DOI":"10.1016\/j.compbiomed.2024.108238"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Agrafiotis, P., Skarlatos, D., Georgopoulos, A., and Karantzalos, K. (2019). DepthLearn: Learning to Correct the Refraction on Point Clouds Derived from Aerial Imagery for Accurate Dense Shallow Water Bathymetry Based on SVMs-Fusion with LiDAR Point Clouds. Remote Sens., 11.","DOI":"10.3390\/rs11192225"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Liu, D., Wen, B., Liu, X., Wang, Z., and Huang, T. (2018). When image denoising meets high-level vision tasks: A deep learning approach. In Proceedings of the International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization. arXiv.","DOI":"10.24963\/ijcai.2018\/117"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., and Krishnan, D. (2017, January 21\u201326). Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks. Proceedings of the Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.18"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Tan, W., Yan, B., and Bare, B. (2018, January 18\u201323). Feature super-resolution: Make machine see more clearly. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00420"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"103819","DOI":"10.1016\/j.infrared.2021.103819","article-title":"Infrared star image denoising using regions with deep reinforcement learning","volume":"117","author":"Zhang","year":"2021","journal-title":"Infrared Phys. Technol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"103963","DOI":"10.1016\/j.infrared.2021.103963","article-title":"Blind infrared images reconstruction using covariogram regularization from a regular pentagon","volume":"120","author":"Zhao","year":"2022","journal-title":"Infrared Phys. Technol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1109\/MSP.2017.2765695","article-title":"Model Compression and Acceleration for Deep Neural Networks: The Principles, Progress, and Challenges","volume":"35","author":"Cheng","year":"2018","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"103440","DOI":"10.1109\/ACCESS.2021.3096548","article-title":"RBDN: Residual Bottleneck Dense Network for Image Super-Resolution","volume":"9","author":"An","year":"2021","journal-title":"IEEE Access"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"115685","DOI":"10.1016\/j.eswa.2021.115685","article-title":"Low-rank sparse feature selection for image classification","volume":"189","author":"Wang","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.swevo.2019.06.002","article-title":"cPSO-CNN: An efficient PSO-based algorithm for fine-tuning hyper-parameters of convolutional neural networks","volume":"49","author":"Wang","year":"2019","journal-title":"Swarm Evol. Comput."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/12\/3983\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:01:16Z","timestamp":1760108476000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/12\/3983"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,19]]},"references-count":40,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["s24123983"],"URL":"https:\/\/doi.org\/10.3390\/s24123983","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2024,6,19]]}}}