{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T17:44:24Z","timestamp":1760118264297,"version":"build-2065373602"},"reference-count":46,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T00:00:00Z","timestamp":1734912000000},"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","doi-asserted-by":"publisher","award":["41971294","82471999","BNR2019TD01022"],"award-info":[{"award-number":["41971294","82471999","BNR2019TD01022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Beijing Institute of Technology Research Fund Program for Young Scholars, and the Cross-Media Intelligent Technology Project of BNRist","award":["41971294","82471999","BNR2019TD01022"],"award-info":[{"award-number":["41971294","82471999","BNR2019TD01022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As the performance of a convolutional neural network is logarithmically proportional to the amount of training data, data augmentation has attracted increasing attention in recent years. Although the current data augmentation methods are efficient because they force the network to learn multiple parts of a given training image through occlusion or re-editing, most of them can damage the internal structures of targets and ultimately affect the results of subsequent application tasks. To this end, region-focusing data augmentation via salient region activation and bitplane recombination for the target detection of optical satellite images is proposed in this paper to solve the problem of internal structure loss in data augmentation. More specifically, to boost the utilization of the positive regions and typical negative regions, a new surroundedness-based strategy for salient region activation is proposed, through which new samples with meaningful focusing regions can be generated. And to generate new samples of the focusing regions, a region-based strategy for bitplane recombination is also proposed, through which internal structures of the focusing regions can be reserved. Thus, a multiplied effect of data augmentation by the two strategies can be achieved. In addition, this is the first time that data augmentation has been examined from the perspective of meaningful focusing regions, rather than the whole sample image. Experiments on target detection with public datasets have demonstrated the effectiveness of this proposed method, especially for small targets.<\/jats:p>","DOI":"10.3390\/rs16244806","type":"journal-article","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T09:13:38Z","timestamp":1734945218000},"page":"4806","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Region-Focusing Data Augmentation via Salient Region Activation and Bitplane Recombination for Target Detection"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0747-4375","authenticated-orcid":false,"given":"Huan","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9721-5371","authenticated-orcid":false,"given":"Xiaolin","family":"Han","sequence":"additional","affiliation":[{"name":"School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8931-8407","authenticated-orcid":false,"given":"Weidong","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sun, C., Shrivastava, A., Singh, S., and Gupta, A. (2017, January 22\u201329). Revisiting Unreasonable Effectiveness of Data in Deep Learning Era. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.97"},{"key":"ref_2","first-page":"103950","article-title":"Landslide Extraction from Aerial Imagery Considering Context Association Characteristics","volume":"131","author":"Xie","year":"2024","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2346259","DOI":"10.1080\/17538947.2024.2346259","article-title":"A Cross-View Intelligent Person Search Method Based on Multi-Feature Constraints","volume":"17","author":"Zhu","year":"2024","journal-title":"Int. J. Digit. Earth"},{"key":"ref_4","first-page":"5519016","article-title":"Hyperspectral Image Super-Resolution with ConvLSTM Skip-Connections","volume":"62","author":"Xu","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"16342","DOI":"10.1109\/JSTARS.2024.3447788","article-title":"BEMRF-Net: Boundary Enhancement and Multiscale Refinement Fusion for Building Extraction from Remote Sensing Imagery","volume":"17","author":"Cao","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","unstructured":"Zhang, H., Han, X., Deng, J., and Sun, W. (2024). How to Evaluate and Remove the Weakened Bands in Hyperspectral Image Classification. IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","unstructured":"Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., and Yoo, Y. (November, January 27). Cutmix: Regularization Strategy to Train Strong Classifiers with Localizable Features. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Xia, G.S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M., Pelillo, M., and Zhang, L. (2018, January 18\u201323). DOTA: A Large-scale Dataset for Object Detection in Aerial Images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00418"},{"key":"ref_9","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 Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3713","DOI":"10.1109\/TIP.2022.3175429","article-title":"Data Augmentation using Bitplane Information Recombination Model","volume":"31","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_11","first-page":"5618319","article-title":"Metalantis: A Comprehensive Underwater Image Enhancement Framework","volume":"62","author":"Wang","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","unstructured":"Liang, J., Liang, S., Liu, A., Ma, K., Li, J., and Cao, X. (November, January 29). Exploring Inconsistent Knowledge Distillation for Object Detection with Data Augmentation. Proceedings of the 31st ACM International Conference on Multimedia, Ottawa, ON, Canada."},{"key":"ref_13","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., and Lopez-Paz, D. (2018). mixup: Beyond Empirical Risk Minimization. Int. Conf. Learn. Represent."},{"key":"ref_14","unstructured":"Verma, V., Lamb, A., Beckham, C., Najafi, A., Mitliagkas, I., Lopez-Paz, D., and Bengio, Y. (2019, January 9\u201315). Manifold mixup: Better Representations by Interpolating Hidden States. Proceedings of the International Conference on Machine Learning, PMLR, Long Beach, CA, USA."},{"key":"ref_15","unstructured":"Mariani, G., Scheidegger, F., Istrate, R., Bekas, C., and Malossi, C. (2018, January 18\u201322). Bagan: Data Augmentation with Balancing GAN. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA."},{"key":"ref_16","first-page":"7559","article-title":"Differentiable Augmentation for Data-efficient GAN Training","volume":"33","author":"Zhao","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_17","first-page":"21655","article-title":"Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data","volume":"34","author":"Jiang","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_18","unstructured":"DeVries, T., and Taylor, G.W. (2017, January 21\u201326). Improved Regularization of Convolutional Neural Networks with Cutout. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA."},{"key":"ref_19","first-page":"13001","article-title":"Random Erasing Data Augmentation","volume":"34","author":"Zhong","year":"2020","journal-title":"AAAI Conf. Artif. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kumar Singh, K., and Jae Lee, Y. (2017, January 22\u201329). Hide-and-seek: Forcing a Network to Be Meticulous for Weakly-supervised Object and Action Localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.381"},{"key":"ref_21","unstructured":"Chen, P., Liu, S., Zhao, H., Wang, X., and Jia, J. (2020, January 14\u201319). Gridmask Data Augmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA."},{"key":"ref_22","unstructured":"Takahashi, R., Matsubara, T., and Uehara, K. (2018, January 14\u201316). Ricap: Random Image Cropping and Patching Data Augmentation for Deep CNNs. Proceedings of the Asian Conference on Machine Learning, PMLR, Beijing, China."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Droste, R., Jiao, J., and Noble, J. (2020, January 23\u201328). Unified image and video saliency modeling. Proceedings of the European Computer Vision Conference, Glasgow, UK.","DOI":"10.1007\/978-3-030-58558-7_25"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3264","DOI":"10.1109\/TIP.2018.2817047","article-title":"A Deep Spatial Contextual Longterm Recurrent Convolutional Network for Saliency Detection","volume":"27","author":"Liu","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_25","unstructured":"Djilali, Y.A.D., McGuinness, K., and O\u2019Connor, N. (2024, January 3\u20138). Learning Saliency from Fixations. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA."},{"key":"ref_26","unstructured":"Hosseini, A., Kazerouni, A., Akhavan, S., Brudno, M., and Taati, B. (2024). SUM: Saliency Unification through Mamba for Visual Attention Modeling. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"94","DOI":"10.2478\/msr-2014-0013","article-title":"Intelligent Segmentation of Medical Images using Fuzzy Bitplane Thresholding","volume":"14","author":"Khan","year":"2014","journal-title":"Meas. Sci. Rev."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1139","DOI":"10.1109\/JBHI.2015.2437396","article-title":"Local Bit-plane Decoded Pattern: A Novel Feature Descriptor for Biomedical Image Retrieval","volume":"20","author":"Dubey","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1002\/ima.22273","article-title":"3D Brain Magnetic Resonance Imaging Segmentation by Using Bitplane and Adaptive Fast Marching","volume":"28","author":"Tuan","year":"2018","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_30","unstructured":"Vladimir, V. (1998). Statistical Learning Theory, Wiley."},{"key":"ref_31","unstructured":"He, Z., Xie, L., Chen, X., Zhang, Y., Wang, Y., and Tian, Q. (2019, January 15\u201320). Data Augmentation Revisited: Rethinking the Distribution Gap between Clean and Augmented Data. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1109\/TPAMI.2015.2473844","article-title":"Exploiting Surroundedness for Saliency Detection: A Boolean Map Approach","volume":"38","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_33","unstructured":"Koller, D., and Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques, MIT Press."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"324","DOI":"10.5220\/0006120603240331","article-title":"A High Resolution Optical Satellite Image Dataset for Ship Recognition and Some New Baselines","volume":"2","author":"Liu","year":"2017","journal-title":"Int. Conf. Pattern Recognit. Appl. Methods"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ding, J., Xue, N., Long, Y., Xia, G.S., and Lu, Q. (2019, January 15\u201320). Learning RoI Transformer for Oriented Object Detection in Aerial Images. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00296"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3111","DOI":"10.1109\/TMM.2018.2818020","article-title":"Arbitrary-oriented Scene Text Detection via Rotation Proposals","volume":"20","author":"Ma","year":"2018","journal-title":"IEEE Trans. Multimed."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y. (2017, January 22\u201329). Deformable Convolutional Networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Azimi, S.M., Vig, E., Bahmanyar, R., K\u00f6rner, M., and Reinartz, P. (2018). Towards Multi-class Object Detection in Unconstrained Remote Sensing Imagery. Asian Conference on Computer Vision, Springer International Publishing.","DOI":"10.1007\/978-3-030-20893-6_10"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.isprsjprs.2020.01.025","article-title":"Rotation-aware and Multi-scale Convolutional Neural Network for Object Detection in Remote Sensing Images","volume":"161","author":"Fu","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"50839","DOI":"10.1109\/ACCESS.2018.2869884","article-title":"Position Detection and Direction Prediction for Arbitrary-oriented Ships via Multiscale Rotation Region Convolutional Neural Network","volume":"6","author":"Yang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"7778","DOI":"10.1109\/TPAMI.2021.3117983","article-title":"Object Detection in Aerial Images: A Large-scale Benchmark and Challenges","volume":"44","author":"Ding","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhang, H., Leng, W., Han, X., and Sun, W. (2023). MOON: A Subspace-Based Multi-Branch Network for Object Detection in Remotely Sensed Images. Remote Sens., 15.","DOI":"10.3390\/rs15174201"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Yi, J., Wu, P., Liu, B., Huang, Q., Qu, H., and Metaxas, D. (2021, January 5\u20139). Oriented Object Detection in Aerial Images with Box Boundary-aware Vectors. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Virtual.","DOI":"10.1109\/WACV48630.2021.00220"},{"key":"ref_44","unstructured":"Zhang, H., Xu, Z., Han, X., and Sun, W. (2021, January 22\u201325). Refining FFT-based Heatmap for the Detection of Cluster Distributed Targets in Satellite Images. Proceedings of the British Machine Vision Conference, Online."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Han, J., Ding, J., Xue, N., and Xia, G.S. (2021, January 19\u201325). Redet: A Rotation-equivariant Detector for Aerial Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPR46437.2021.00281"},{"key":"ref_46","first-page":"3163","article-title":"R3det: Refined Single-stage Detector with Feature Refinement for Rotating Object","volume":"35","author":"Yang","year":"2021","journal-title":"Proc. AAAI Conf. Artif. Intell."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/24\/4806\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:58:39Z","timestamp":1760115519000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/24\/4806"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,23]]},"references-count":46,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["rs16244806"],"URL":"https:\/\/doi.org\/10.3390\/rs16244806","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,12,23]]}}}