{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:13:21Z","timestamp":1760242401371,"version":"build-2065373602"},"reference-count":52,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2017,6,12]],"date-time":"2017-06-12T00:00:00Z","timestamp":1497225600000},"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>Remote sensing technologies have been widely applied in urban environments\u2019 monitoring, synthesis and modeling. Incorporating spatial information in perceptually coherent regions, superpixel-based approaches can effectively eliminate the \u201csalt and pepper\u201d phenomenon which is common in pixel-wise approaches. Compared with fixed-size windows, superpixels have adaptive sizes and shapes for different spatial structures. Moreover, superpixel-based algorithms can significantly improve computational efficiency owing to the greatly reduced number of image primitives. Hence, the superpixel algorithm, as a preprocessing technique, is more and more popularly used in remote sensing and many other fields. In this paper, we propose a superpixel segmentation algorithm called Superpixel Segmentation with Local Competition (SSLC), which utilizes a local competition mechanism to construct energy terms and label pixels. The local competition mechanism leads to energy terms locality and relativity, and thus, the proposed algorithm is less sensitive to the diversity of image content and scene layout. Consequently, SSLC could achieve consistent performance in different image regions. In addition, the Probability Density Function (PDF), which is estimated by Kernel Density Estimation (KDE) with the Gaussian kernel, is introduced to describe the color distribution of superpixels as a more sophisticated and accurate measure. To reduce computational complexity, a boundary optimization framework is introduced to only handle boundary pixels instead of the whole image. We conduct experiments to benchmark the proposed algorithm with the other state-of-the-art ones on the Berkeley Segmentation Dataset (BSD) and remote sensing images. Results demonstrate that the SSLC algorithm yields the best overall performance, while the computation time-efficiency is still competitive.<\/jats:p>","DOI":"10.3390\/s17061364","type":"journal-article","created":{"date-parts":[[2017,6,12]],"date-time":"2017-06-12T10:27:59Z","timestamp":1497263279000},"page":"1364","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Local Competition-Based Superpixel Segmentation Algorithm in Remote Sensing"],"prefix":"10.3390","volume":"17","author":[{"given":"Jiayin","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenmin","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Cui","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Zhejiang University of Technology, Hangzhou 310023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoxing","family":"Wu","sequence":"additional","affiliation":[{"name":"Huawei Technologies Co., Ltd., Nanjing 210012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,6,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","article-title":"Classification of hyperspectral remote sensing images with support vector machines","volume":"42","author":"Melgani","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"894","DOI":"10.1109\/TGRS.2011.2162589","article-title":"An adaptive artificial immune network for supervised classification of multi-\/ hyperspectral remote sensing imagery","volume":"50","author":"Zhong","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7416","DOI":"10.1109\/TGRS.2016.2603190","article-title":"Probabilistic fusion of pixel-level and superpixel-level hyperspectral image classification","volume":"54","author":"Li","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2056","DOI":"10.1109\/JSTARS.2013.2264720","article-title":"A nonlocal weighted joint sparse representation classification method for hyperspectral imagery","volume":"7","author":"Zhang","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3973","DOI":"10.1109\/TGRS.2011.2129595","article-title":"Hyperspectral image classification using dictionary-based sparse representation","volume":"49","author":"Chen","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4186","DOI":"10.1109\/TGRS.2015.2392755","article-title":"Spectral-spatial classification of hyperspectral images with a superpixel-based discriminative sparse model","volume":"53","author":"Fang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ren, X., and Malik, J. (2003, January 13\u201316). Learning a classification model for segmentation. Proceedings of the IEEE International Conference on Computer Vision, Nice, France.","DOI":"10.1109\/ICCV.2003.1238308"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1109\/TGRS.2016.2613848","article-title":"Adaptive spectral-spatial compression of hyperspectral image with sparse representation","volume":"55","author":"Fu","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5338","DOI":"10.1109\/TGRS.2015.2421638","article-title":"Efficient superpixel-level multitask joint sparse representation for hyperspectral image classification","volume":"53","author":"Li","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, S., Li, S., Fu, W., and Fang, L. (2017). Multiscale superpixel-based sparse representation for hyperspectral image classification. Remote Sens., 9.","DOI":"10.3390\/rs9020139"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Duan, W., Li, S., and Fang, L. (2014, January 17\u201319). Spectral-spatial hyperspectral image classification using superpixel and extreme learning machines. Proceedings of the Chinese Conference on Pattern Recognition, Changsha, China.","DOI":"10.1007\/978-3-662-45646-0_17"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2367","DOI":"10.1016\/j.patcog.2010.01.016","article-title":"Segmentation and classification of hyperspectral images using watershed transformation","volume":"43","author":"Tarabalka","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5861","DOI":"10.1109\/TGRS.2015.2423688","article-title":"Superpixel-based graphical model for remote sensing image mapping","volume":"53","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"907","DOI":"10.1109\/TGRS.2012.2203358","article-title":"Superpixel-based classification with an adaptive number of classes for polarimetric sar images","volume":"51","author":"Liu","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4905","DOI":"10.1080\/01431161.2016.1225175","article-title":"Multi-scale superpixel spectral-spatial classification of hyperspectral images","volume":"37","author":"Li","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Fu, W., Li, S., and Fang, L. (2015, January 26\u201331). Spectral-spatial hyperspectral image classification via superpixel merging and sparse representation. Proceedings of the Geoscience and Remote Sensing Symposium (IGARSS 2015), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326948"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, Y., Condessa, F., Bioucas-Dias, J., Li, J., and Plaza, A. (2016, January 10\u201315). Convex formulation for hyperspectral image classification with superpixels. Proceedings of the Geoscience and Remote Sensing Symposium (IGARSS 2016), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729852"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xu, Q., Chen, Q., Yang, S., and Liu, X. (2016). Superpixel-based classification using k distribution and spatial context for polarimetric sar images. Remote Sens., 8.","DOI":"10.3390\/rs8080619"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.ins.2017.02.044","article-title":"Hyperspectral image denoising with superpixel segmentation and low-rank representation","volume":"397","author":"Fan","year":"2017","journal-title":"Inf. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Caliskan, A., Bati, E., Koza, A., and Alatan, A.A. (2016, January 10\u201315). Superpixel based hyperspectral target detection. Proceedings of the Geoscience and Remote Sensing Symposium (IGARSS 2016), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7730828"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1109\/LGRS.2016.2540809","article-title":"Superpixel-based cfar target detection for high-resolution sar images","volume":"13","author":"Yu","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liang, Y., Markopoulos, P.P., and Saber, E.S. (2016, January 10\u201315). Subpixel target detection in hyperspectral images from superpixel background statistics. Proceedings of the Geoscience and Remote Sensing Symposium (IGARSS 2016), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7730830"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1109\/34.87344","article-title":"Watersheds in digital spaces: An efficient algorithm based on immersion simulations","volume":"13","author":"Vincent","year":"1991","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1109\/34.868688","article-title":"Normalized cuts and image segmentation","volume":"22","author":"Shi","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, M., Tuzel, O., Ramalingam, S., and Chellappa, R. (2011, January 20\u201325). Entropy rate superpixel segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2011), Colorado, CO, USA.","DOI":"10.1109\/CVPR.2011.5995323"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"SLIC superpixels compared to state-of-the-art superpixel methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Schick, A., Bauml, M., and Stiefelhagen, R. (2012, January 16\u201321). Improving foreground segmentations with probabilistic superpixel markov random fields. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Providence, RI, USA.","DOI":"10.1109\/CVPRW.2012.6238923"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"26654","DOI":"10.3390\/s151026654","article-title":"A biologically-inspired framework for contour detection using superpixel-based candidates and hierarchical visual cues","volume":"15","author":"Sun","year":"2015","journal-title":"Sensors"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"29594","DOI":"10.3390\/s151129594","article-title":"Vision sensor-based road detection for field robot navigation","volume":"15","author":"Lu","year":"2015","journal-title":"Sensors"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Alvarez, J.M., Gevers, T., Lecun, Y., and Lopez, A.M. (2012, January 7\u201313). Road scene segmentation from a single image. Proceedings of the European Conference on Computer Vision, Firenze, Italy.","DOI":"10.1007\/978-3-642-33786-4_28"},{"key":"ref_31","unstructured":"Lu, H., Jiang, L., and Zell, A. (October, January 28). Long range traversable region detection based on superpixels clustering for mobile robots. Proceedings of the International Conference on Intelligent Robots and Systems, Hamburg, Germany."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1007\/s11263-012-0574-z","article-title":"SuperParsing: Scalable nonparametric image parsing with superpixels","volume":"101","author":"Tighe","year":"2013","journal-title":"Int. J. Comput. Vis."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1109\/TCSVT.2013.2273631","article-title":"Robust superpixel tracking via depth fusion","volume":"24","author":"Yuan","year":"2014","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_34","unstructured":"Fulkerson, B., Vedaldi, A., and Soatto, S. (October, January 29). Class segmentation and object localization with superpixel neighborhoods. Proceedings of the IEEE International Conference on Computer Vision, Kyoto, Japan."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yao, J., Boben, M., Fidler, S., and Urtasun, R. (2015, January 7\u201312). Real-time coarse-to-fine topologically preserving segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298913"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Moore, A.P., Prince, S.J.D., and Warrell, J. (2010, January 13\u201318). \u201cLattice Cut\u201d\u2014Constructing superpixels using layer constraints. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539890"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1007\/s11263-014-0744-2","article-title":"SEEDS: Superpixels extracted via energy-driven sampling","volume":"111","author":"Bergh","year":"2013","journal-title":"Int. J. Comput. Vis."},{"key":"ref_38","unstructured":"Martin, D., Fowlkes, C., Tal, D., and Malik, J. (2001, January 7\u201314). A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. Proceedings of the IEEE International Conference on Computer Vision, Vancouver, BC, Canada."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1109\/34.1000236","article-title":"Mean shift: A robust approach toward feature space analysis","volume":"24","author":"Comaniciu","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1023\/B:VISI.0000022288.19776.77","article-title":"Efficient graph-based image segmentation","volume":"59","author":"Felzenszwalb","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Vedaldi, A., and Soatto, S. (2008, January 12\u201318). Quick shift and kernel methods for mode seeking. Proceedings of the European Conference on Computer Vision, Marseille, France.","DOI":"10.1007\/978-3-540-88693-8_52"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2290","DOI":"10.1109\/TPAMI.2009.96","article-title":"Turbopixels: Fast superpixels using geometric flows","volume":"31","author":"Levinshtein","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_43","unstructured":"Zeng, G., Wang, P., Wang, J., Gan, R., and Zha, H. (2011, January 6\u201313). Structure-sensitive superpixels via geodesic distance. Proceedings of the International Conference on Computer Vision, Barcelona, Spain."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Moore, A.P., Prince, S.J.D., Warrell, J., Mohammed, U., and Jones, G. (2008, January 24\u201326). Superpixel lattices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA.","DOI":"10.1109\/CVPR.2008.4587471"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Tang, D., Fu, H., and Cao, X. (2012, January 9\u201313). Topology preserved regular superpixel. Proceedings of the IEEE International Conference on Multimedia and Expo, Melbourne, Australia.","DOI":"10.1109\/ICME.2012.184"},{"key":"ref_46","unstructured":"Li, Z., and Chen, J. (2015, January 7\u201312). Superpixel segmentation using linear spectral clustering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA."},{"key":"ref_47","unstructured":"Liu, Y., Yu, C., Yu, M., and He, Y. (July, January 26). Manifold slic: A fast method to compute content-sensitive superpixels. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Wang, H., Peng, X., Xiao, X., and Liu, Y. (2017). BSLIC: Slic superpixels based on boundary term. Symmetry, 9.","DOI":"10.3390\/sym9030031"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Yang, C., Duraiswami, R., Gumerov, N.A., and Davis, L. (2003, January 13\u201316). Improved fast gauss transform and efficient kernel density estimation. Proceedings of the IEEE International Conference on Computer Vision, Nice, France.","DOI":"10.1109\/ICCV.2003.1238383"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1561\/0600000029","article-title":"Geodesic methods in computer vision and graphics","volume":"5","author":"Peyr","year":"2010","journal-title":"Found. Trends Comput. Graph. Vis."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1006\/jcph.1998.6090","article-title":"The fast construction of extension velocities in level set methods","volume":"148","author":"Adalsteinsson","year":"1999","journal-title":"J. Comput. Phys."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.irbm.2013.12.007","article-title":"Eikonal based region growing for superpixels generation: Application to semi-supervised real time organ segmentation in ct images","volume":"35","author":"Buyssens","year":"2014","journal-title":"IRBM"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/6\/1364\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:38:48Z","timestamp":1760207928000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/6\/1364"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,6,12]]},"references-count":52,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2017,6]]}},"alternative-id":["s17061364"],"URL":"https:\/\/doi.org\/10.3390\/s17061364","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2017,6,12]]}}}