{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T06:30:06Z","timestamp":1768804206197,"version":"3.49.0"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T00:00:00Z","timestamp":1662422400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T00:00:00Z","timestamp":1662422400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1604153"],"award-info":[{"award-number":["U1604153"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2023,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Many residual network-based methods have been proposed to perform object detection. However, most of them may lead to overfitting or cannot perform well in small object detection and alleviate the problem of overfitting. We propose a multiple spatial residual network (MSRNet) for object detection. Particularly, our method is based on central point detection algorithm. Our proposed MSRNet employs a residual network as the backbone. The resulting features are processed by our proposed residual channel pooling module. We then construct a multi-scale feature transposed residual fusion structure consists of three overlapping stacked residual convolution modules and a transpose convolution function. Finally, we use the Center structure to process the high-resolution feature image for obtaining the final prediction detection result. Experimental results on PASCAL VOC dataset and COCO dataset confirm that the MSRNet has competitive accuracy compared with several other classical object detection algorithms, while providing a unified framework for training and reasoning. The MSRNet runs on GeForce RTX 2080Ti.<\/jats:p>","DOI":"10.1007\/s40747-022-00859-7","type":"journal-article","created":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T05:03:41Z","timestamp":1662440621000},"page":"1347-1362","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Multiple spatial residual network for object detection"],"prefix":"10.1007","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6281-9658","authenticated-orcid":false,"given":"Yongsheng","family":"Dong","sequence":"first","affiliation":[]},{"given":"Zhiqiang","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Fazhan","family":"Tao","sequence":"additional","affiliation":[]},{"given":"Zhumu","family":"Fu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,6]]},"reference":[{"issue":"7","key":"859_CR1","doi-asserted-by":"publisher","first-page":"4820","DOI":"10.1109\/TII.2021.3129629","volume":"18","author":"M Wieczorek","year":"2022","unstructured":"Wieczorek M, Si\u0142ka J, Wo\u017aniak M, Garg S, Hassan MM (2022) Lightweight convolutional neural network model for human face detection in risk situations. IEEE Trans Ind Inform 18(7):4820\u20134829. https:\/\/doi.org\/10.1109\/TII.2021.3129629","journal-title":"IEEE Trans Ind Inform"},{"issue":"1","key":"859_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-022-09293-8","volume":"12","author":"H Basak","year":"2022","unstructured":"Basak H, Kundu R, Singh PK, Ijaz MF, Wo\u017aniak M, Sarkar R (2022) A union of deep learning and swarm-based optimization for 3D human action recognition. Sci Rep 12(1):1\u201317","journal-title":"Sci Rep"},{"key":"859_CR3","doi-asserted-by":"publisher","unstructured":"Wo\u017aniak M, Si\u0142ka J, Wieczorek M (2021) Deep neural network correlation learning mechanism for CT brain tumor detection. Neural Comput Appl 1\u201316. https:\/\/doi.org\/10.1007\/s00521-021-05841-x","DOI":"10.1007\/s00521-021-05841-x"},{"key":"859_CR4","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3102268","author":"F Huo","year":"2021","unstructured":"Huo F, Zhu X, Zhang L, Liu Q, Shu Y (2021) Efficient context-guided stacked refinement network for rgb-t salient object detection. IEEE Trans Circuits Syst Video Technol. https:\/\/doi.org\/10.1109\/TCSVT.2021.3102268","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"5","key":"859_CR5","doi-asserted-by":"publisher","first-page":"1325","DOI":"10.1109\/TCSVT.2018.2841825","volume":"29","author":"I Kajo","year":"2019","unstructured":"Kajo I, Kamel N, Ruichek Y (2019) Incremental tensor-based completion method for detection of stationary foreground objects. IEEE Trans Circuits Syst Video Technol 29(5):1325\u20131338. https:\/\/doi.org\/10.1109\/TCSVT.2018.2841825","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"859_CR6","doi-asserted-by":"publisher","unstructured":"Hu H-N, Cai Q-Z, Wang D, Lin J, Sun M, Kraehenbuehl P, Darrell T, Yu F (2019) Joint monocular 3d vehicle detection and tracking. In: Proceedings of the 2019 IEEE international conference on computer vision (ICCV). Seoul, pp 5389\u20135398. https:\/\/doi.org\/10.1109\/ICCV.2019.00549","DOI":"10.1109\/ICCV.2019.00549"},{"issue":"2","key":"859_CR7","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1109\/TCSVT.2020.2980876","volume":"31","author":"X Chen","year":"2021","unstructured":"Chen X, Yu J, Kong S, Wu Z, Wen L (2021) Joint anchor-feature refinement for real-time accurate object detection in images and videos. IEEE Trans Circuits Syst Video Technol 31(2):594\u2013607. https:\/\/doi.org\/10.1109\/TCSVT.2020.2980876","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"859_CR8","unstructured":"Zou Z, Shi Z, Guo Y, Ye J (2019) Object detection in 20 years: a survey. arXiv preprint arXiv:1905.05055"},{"key":"859_CR9","doi-asserted-by":"publisher","first-page":"9165","DOI":"10.1109\/TIP.2020.3023774","volume":"29","author":"X Li","year":"2020","unstructured":"Li X, Song D, Dong Y (2020) Hierarchical feature fusion network for salient object detection. IEEE Trans Image Process 29:9165\u20139175","journal-title":"IEEE Trans Image Process"},{"key":"859_CR10","doi-asserted-by":"publisher","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the 2021 IEEE international conference on computer vision (ICCV). pp 9992\u201310002. https:\/\/doi.org\/10.1109\/ICCV48922.2021.00986","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"859_CR11","first-page":"213","volume-title":"European conference on computer vision (ECCV)","author":"N Carion","year":"2020","unstructured":"Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. In: Vedaldi A, Bischof H, Brox T, Frahm J-M (eds) European conference on computer vision (ECCV). Springer, Cham, pp 213\u2013229"},{"key":"859_CR12","doi-asserted-by":"publisher","unstructured":"Chen P, Liu J, Zhuang B, Tan M, Shen C (2021) Aqd: towards accurate quantized object detection. In: IEEE conference on computer vision and pattern recognition (CVPR). pp 104\u2013113. https:\/\/doi.org\/10.1109\/CVPR46437.2021.00017","DOI":"10.1109\/CVPR46437.2021.00017"},{"key":"859_CR13","doi-asserted-by":"publisher","unstructured":"Wang J, Chen K, Yang S, Loy CC, Lin D (2019) Region proposal by guided anchoring. In: Proceedings of the 2019 IEEE conference on computer vision and pattern recognition (CVPR). Long Beach, pp 2960\u20132969. https:\/\/doi.org\/10.1109\/CVPR.2019.00308","DOI":"10.1109\/CVPR.2019.00308"},{"key":"859_CR14","doi-asserted-by":"publisher","unstructured":"Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the 2014 IEEE conference on computer vision and pattern recognition (CVPR). Columbus, pp 580\u2013587. https:\/\/doi.org\/10.1109\/CVPR.2014.81","DOI":"10.1109\/CVPR.2014.81"},{"issue":"1","key":"859_CR15","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1109\/TPAMI.2019.2929257","volume":"43","author":"Z Cao","year":"2021","unstructured":"Cao Z, Hidalgo G, Simon T, Wei S-E, Sheikh Y (2021) Openpose: realtime multi-person 2d pose estimation using part affinity fields. IEEE Trans Pattern Anal Mach Intell 43(1):172\u2013186. https:\/\/doi.org\/10.1109\/TPAMI.2019.2929257","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"859_CR16","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3102944","author":"Z Li","year":"2021","unstructured":"Li Z, Lang C, Liang L, Zhao J, Feng S, Hou Q, Feng J (2021) Dense attentive feature enhancement for salient object detection. IEEE Trans Circuits Syst Video Technol. https:\/\/doi.org\/10.1109\/TCSVT.2021.3102944","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"859_CR17","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1109\/TIP.2021.3130539","volume":"31","author":"Y Dong","year":"2022","unstructured":"Dong Y, Tan W, Tao D, Zheng L, Li X (2022) CartoonlossGAN: learning surface and coloring of images for cartoonization. IEEE Trans Image Process 31:485\u2013498","journal-title":"IEEE Trans Image Process"},{"key":"859_CR18","doi-asserted-by":"crossref","unstructured":"Law H, Deng J (2018) Cornernet: detecting objects as paired keypoints. In: European conference on computer vision (ECCV)","DOI":"10.1007\/978-3-030-01264-9_45"},{"key":"859_CR19","doi-asserted-by":"publisher","unstructured":"Duan K, Bai S, Xie L, Qi H, Huang Q, Tian Q (2019) Centernet: keypoint triplets for object detection. In: Proceedings of the 2019 IEEE international conference on computer vision (ICCV). Seoul, pp 6568\u20136577. https:\/\/doi.org\/10.1109\/ICCV.2019.00667","DOI":"10.1109\/ICCV.2019.00667"},{"issue":"6","key":"859_CR20","doi-asserted-by":"publisher","first-page":"1639","DOI":"10.1109\/TCSVT.2019.2906246","volume":"30","author":"K Duan","year":"2020","unstructured":"Duan K, Du D, Qi H, Huang Q (2020) Detecting small objects using a channel-aware deconvolutional network. IEEE Trans Circuits Syst Video Technol 30(6):1639\u20131652. https:\/\/doi.org\/10.1109\/TCSVT.2019.2906246","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"6","key":"859_CR21","doi-asserted-by":"publisher","first-page":"1758","DOI":"10.1109\/TCSVT.2019.2905881","volume":"30","author":"X Liang","year":"2020","unstructured":"Liang X, Zhang J, Zhuo L, Li Y, Tian Q (2020) Small object detection in unmanned aerial vehicle images using feature fusion and scaling-based single shot detector with spatial context analysis. IEEE Trans Circuits Syst Video Technol 30(6):1758\u20131770. https:\/\/doi.org\/10.1109\/TCSVT.2019.2905881","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"7","key":"859_CR22","doi-asserted-by":"publisher","first-page":"2067","DOI":"10.1109\/TCSVT.2019.2909982","volume":"30","author":"C Zhou","year":"2020","unstructured":"Zhou C, Yuan J (2020) Occlusion pattern discovery for object detection and occlusion reasoning. IEEE Trans Circuits Syst Video Technol 30(7):2067\u20132080. https:\/\/doi.org\/10.1109\/TCSVT.2019.2909982","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"859_CR23","doi-asserted-by":"publisher","unstructured":"Li Y, Chen Y, Wang N, Zhang Z-X (2019) Scale-aware trident networks for object detection. In: Proceedings of the 2019 IEEE international conference on computer vision (ICCV). Seoul, pp 6053\u20136062. https:\/\/doi.org\/10.1109\/ICCV.2019.00615","DOI":"10.1109\/ICCV.2019.00615"},{"key":"859_CR24","doi-asserted-by":"publisher","unstructured":"Lu X, Li B, Yue Y, Li Q, Yan J (2019) Grid r-cnn. In: Proceedings of the 2019 IEEE conference on computer vision and pattern recognition (CVPR). Long Beach, pp 7355\u20137364. https:\/\/doi.org\/10.1109\/CVPR.2019.00754","DOI":"10.1109\/CVPR.2019.00754"},{"issue":"2","key":"859_CR25","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham M, Gool LV, Williams CKI, Winn J, Zisserman A (2010) The pascal visual object classes (voc) challenge. Int J Comput Vis 88(2):303\u2013338","journal-title":"Int J Comput Vis"},{"key":"859_CR26","doi-asserted-by":"crossref","unstructured":"Girshick R (2015) Fast r-cnn. In: Proceedings of the 2015 IEEE international conference on computer vision (ICCV). Santiago, pp 1440\u20131448","DOI":"10.1109\/ICCV.2015.169"},{"issue":"6","key":"859_CR27","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren S, He K, Girshick R, Sun J (2017) Faster r-cnn: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell 39(6):1137\u20131149. https:\/\/doi.org\/10.1109\/TPAMI.2016.2577031","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"9","key":"859_CR28","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","volume":"37","author":"K He","year":"2015","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37(9):1904\u20131916. https:\/\/doi.org\/10.1109\/TPAMI.2015.2389824","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"859_CR29","doi-asserted-by":"publisher","unstructured":"Lin T-Y, Doll\u00e1r P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the 2017 IEEE conference on computer vision and pattern recognition (CVPR). Hawaii, pp 936\u2013944. https:\/\/doi.org\/10.1109\/CVPR.2017.106","DOI":"10.1109\/CVPR.2017.106"},{"key":"859_CR30","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Dollar P, Girshick R (2017) Mask r-cnn. In: IEEE international conference on computer vision (ICCV)","DOI":"10.1109\/ICCV.2017.322"},{"key":"859_CR31","doi-asserted-by":"crossref","unstructured":"Qiao S, Chen L-C, Yuille A (2021) Detectors: detecting objects with recursive feature pyramid and switchable atrous convolution. In: Proceedings of the 2021 IEEE conference on computer vision and pattern recognition (CVPR). pp 10213\u201310224","DOI":"10.1109\/CVPR46437.2021.01008"},{"key":"859_CR32","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) SSD: single shot multibox detector. In: Proceedings of the 2016 European conference on computer vision (ECCV). Amsterdam, pp 21\u201337","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"859_CR33","doi-asserted-by":"publisher","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: Unified, real-time object detection. In: IEEE conference on computer vision and pattern recognition (CVPR). pp 779\u2013788. https:\/\/doi.org\/10.1109\/CVPR.2016.91","DOI":"10.1109\/CVPR.2016.91"},{"key":"859_CR34","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: Proceedings of the 3rd International Conference on Learning Representations. Hilton San Diego Resort & Spa, Chile, pp 1\u201314"},{"key":"859_CR35","doi-asserted-by":"publisher","unstructured":"Redmon J, Farhadi A (2017) Yolo9000: better, faster, stronger. In: IEEE conference on computer vision and pattern recognition (CVPR). pp 6517\u20136525. https:\/\/doi.org\/10.1109\/CVPR.2017.690","DOI":"10.1109\/CVPR.2017.690"},{"key":"859_CR36","unstructured":"Redmon J, Farhadi A (2018) Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767"},{"key":"859_CR37","unstructured":"Ge Z, Liu S, Wang F, Li Z, Sun J (2021) Yolox: exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430"},{"key":"859_CR38","doi-asserted-by":"publisher","unstructured":"Tan M, Pang R, Le QV (2020) Efficientdet: scalable and efficient object detection. In: IEEE conference on computer vision and pattern recognition (CVPR). pp. 10778\u201310787. https:\/\/doi.org\/10.1109\/CVPR42600.2020.01079","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"859_CR39","doi-asserted-by":"publisher","unstructured":"Lin T-Y, Goyal P, Girshick R, He K, Doll\u00e1r P (2017) Focal loss for dense object detection. In: Proceedings of the 2017 IEEE international conference on computer vision (ICCV). Venice, pp 2999\u20133007. https:\/\/doi.org\/10.1109\/ICCV.2017.324","DOI":"10.1109\/ICCV.2017.324"},{"key":"859_CR40","unstructured":"Zhou X, Wang D, Kr\u00e4henb\u00fchl P (2019) Objects as points. arXiv preprint arXiv:1904.07850"},{"key":"859_CR41","doi-asserted-by":"publisher","unstructured":"Neubeck A, Van\u00a0Gool L (2006) Efficient non-maximum suppression. In: Proceedings of the 18th International conference on pattern recognition, vol 3. Hong Kong, pp 850\u2013855. https:\/\/doi.org\/10.1109\/ICPR.2006.479","DOI":"10.1109\/ICPR.2006.479"},{"key":"859_CR42","doi-asserted-by":"publisher","first-page":"104471","DOI":"10.1016\/j.imavis.2022.104471","volume":"123","author":"K Tong","year":"2022","unstructured":"Tong K, Wu Y (2022) Deep learning-based detection from the perspective of small or tiny objects: a survey. Image Vis Comput 123:104471. https:\/\/doi.org\/10.1016\/j.imavis.2022.104471","journal-title":"Image Vis Comput"},{"key":"859_CR43","doi-asserted-by":"publisher","unstructured":"Li J, Liang X, Wei Y, Xu T, Feng J, Yan S (2017) Perceptual generative adversarial networks for small object detection. In: IEEE conference on computer vision and pattern recognition (CVPR). pp 1951\u20131959. https:\/\/doi.org\/10.1109\/CVPR.2017.211","DOI":"10.1109\/CVPR.2017.211"},{"key":"859_CR44","doi-asserted-by":"publisher","unstructured":"Liang X, Zhang J, Zhuo L, Li Y, Tian Q (2020) Small object detection in unmanned aerial vehicle images using feature fusion and scaling-based single shot detector with spatial context analysis. IEEE Trans Circuits Syst Video Technol 30(6):1758\u20131770. https:\/\/doi.org\/10.1109\/TCSVT.2019.2905881","DOI":"10.1109\/TCSVT.2019.2905881"},{"key":"859_CR45","doi-asserted-by":"crossref","unstructured":"Yang S, Tian L, Zhou B, Chen D, Zhang D, Xu Z, Guo W, Liu J (2020) Inception parallel attention network for small object detection in remote sensing images. In: Chinese conference on pattern recognition and computer vision (PRCV). pp 469\u2013480","DOI":"10.1007\/978-3-030-60633-6_39"},{"key":"859_CR46","doi-asserted-by":"publisher","unstructured":"Kong T, Yao A, Chen Y, Sun F (2016) Hypernet: towards accurate region proposal generation and joint object detection. In: IEEE conference on computer vision and pattern recognition (CVPR). pp 845\u2013853. https:\/\/doi.org\/10.1109\/CVPR.2016.98","DOI":"10.1109\/CVPR.2016.98"},{"issue":"11","key":"859_CR47","doi-asserted-by":"publisher","first-page":"6699","DOI":"10.1109\/TGRS.2018.2841808","volume":"56","author":"L Mou","year":"2018","unstructured":"Mou L, Zhu XX (2018) Vehicle instance segmentation from aerial image and video using a multitask learning residual fully convolutional network. IEEE Trans Geosci Remote Sens 56(11):6699\u20136711. https:\/\/doi.org\/10.1109\/TGRS.2018.2841808","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"859_CR48","doi-asserted-by":"publisher","unstructured":"Wang A, Sun Y, Kortylewski A, Yuille A (2020) Robust object detection under occlusion with context-aware compositionalnets. In: Proceedings of the 2020 IEEE conference on computer vision and pattern recognition (CVPR).Seattle, pp 12642\u201312651. https:\/\/doi.org\/10.1109\/CVPR42600.2020.01266","DOI":"10.1109\/CVPR42600.2020.01266"},{"issue":"5","key":"859_CR49","doi-asserted-by":"publisher","first-page":"1181","DOI":"10.1109\/TIFS.2018.2871749","volume":"14","author":"M Boroumand","year":"2019","unstructured":"Boroumand M, Chen M, Fridrich J (2019) Deep residual network for steganalysis of digital images. IEEE Trans Inf Forensics Secur 14(5):1181\u20131193. https:\/\/doi.org\/10.1109\/TIFS.2018.2871749","journal-title":"IEEE Trans Inf Forensics Secur"},{"issue":"2","key":"859_CR50","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1109\/TPAMI.2018.2799847","volume":"41","author":"O Costilla-Reyes","year":"2019","unstructured":"Costilla-Reyes O, Vera-Rodriguez R, Scully P, Ozanyan KB (2019) Analysis of spatio-temporal representations for robust footstep recognition with deep residual neural networks. IEEE Trans Pattern Anal Mach Intell 41(2):285\u2013296","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"859_CR51","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1109\/TGRS.2018.2860125","volume":"57","author":"ME Paoletti","year":"2019","unstructured":"Paoletti ME, Haut JM, Fernandez-Beltran R, Plaza J, Plaza AJ, Pla F (2019) Deep pyramidal residual networks for spectral-spatial hyperspectral image classification. IEEE Trans Geosci Remote Sens 57(2):740\u2013754. https:\/\/doi.org\/10.1109\/TGRS.2018.2860125","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"859_CR52","doi-asserted-by":"publisher","unstructured":"Zhu X, Hu H, Lin S, Dai J (2019) Deformable convnets v2: more deformable, better results. In: IEEE conference on computer vision and pattern recognition (CVPR). pp 9300\u20139308. https:\/\/doi.org\/10.1109\/CVPR.2019.00953","DOI":"10.1109\/CVPR.2019.00953"},{"key":"859_CR53","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L et al (2019) Pytorch: an imperative style, high-performance deep learning library. Adv Neural Inf Process Syst 32:8024-8035"},{"key":"859_CR54","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980"},{"key":"859_CR55","unstructured":"Fu C-Y, Liu W, Ranga A, Tyagi A, Berg AC (2017) DSSD: Deconvolutional single shot detector. arXiv preprint arXiv:1701.06659"},{"issue":"12","key":"859_CR56","doi-asserted-by":"publisher","first-page":"3583","DOI":"10.1109\/TCSVT.2018.2883825","volume":"29","author":"Y Dong","year":"2019","unstructured":"Dong Y, Wu H, Li X, Zhou C, Wu Q (2019) Multiscale symmetric dense micro-block difference for texture classification. IEEE Trans Circuits Syst Video Technol 29(12):3583\u20133594","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"5","key":"859_CR57","doi-asserted-by":"publisher","first-page":"1684","DOI":"10.1109\/TCSVT.2020.3014526","volume":"31","author":"Y Dong","year":"2021","unstructured":"Dong Y, Jin M, Li X, Ma J, Liu Z, Wang L, Zheng L (2021) Compact interchannel sampling difference descriptor for color texture classification. IEEE Trans Circuits Syst Video Technol 31(5):1684\u20131696","journal-title":"IEEE Trans Circuits Syst Video Technol"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-022-00859-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-022-00859-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-022-00859-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,18]],"date-time":"2023-04-18T09:24:25Z","timestamp":1681809865000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-022-00859-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,6]]},"references-count":57,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,4]]}},"alternative-id":["859"],"URL":"https:\/\/doi.org\/10.1007\/s40747-022-00859-7","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,6]]},"assertion":[{"value":"22 April 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 August 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 September 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}