{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T17:35:39Z","timestamp":1770485739229,"version":"3.49.0"},"reference-count":24,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2013,7,5]],"date-time":"2013-07-05T00:00:00Z","timestamp":1372982400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>An approach based on the improved quadtree structure and region adjacency graph for the segmentation of a high-resolution remote sensing image is proposed in this paper. In order to obtain the initial segmentation results of the image, the image is first iteratively split into quarter sections and the quadtree structure is constructed. In this process, an improved fast calculation method for standard deviation of image is proposed, which significantly increases the speed of quadtree segmentation with standard deviation criterion. A spatial indexing structure was built using improved Morton encoding based on this structure, which provides the merging process with data structure for neighborhood queries. Then, in order to obtain the final segmentation result, we constructed a feature vector using both spectral and texture factors, and proposed an algorithm for region merging based on the region adjacency graph technique. Finally, to validate the method, experiments were performed on GeoEye-1 and IKONOS color images, and the segmentation results were compared with two typical algorithms: multi-resolution segmentation and Mean-Shift segmentation. The experimental results showed that: (1) Compared with multi-resolution and Mean-Shift segmentation, our method increased efficiency by 3\u20135 times and 10 times, respectively; (2) Compared with the typical algorithms, the new method significantly improved the accuracy of segmentation.<\/jats:p>","DOI":"10.3390\/rs5073259","type":"journal-article","created":{"date-parts":[[2013,7,5]],"date-time":"2013-07-05T12:28:23Z","timestamp":1373027303000},"page":"3259-3279","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Segmentation for High-Resolution Optical Remote Sensing Imagery Using Improved Quadtree and Region Adjacency Graph Technique"],"prefix":"10.3390","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8443-4604","authenticated-orcid":false,"given":"Gang","family":"Fu","sequence":"first","affiliation":[{"name":"Institute of Geomatics, Department of Civil Engineering, Tsinghua University, Chengfu Road, Beijing 100084, China"}]},{"given":"Hongrui","family":"Zhao","sequence":"additional","affiliation":[{"name":"Institute of Geomatics, Department of Civil Engineering, Tsinghua University, Chengfu Road, Beijing 100084, China"}]},{"given":"Cong","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Geomatics, Department of Civil Engineering, Tsinghua University, Chengfu Road, Beijing 100084, China"}]},{"given":"Limei","family":"Shi","sequence":"additional","affiliation":[{"name":"Institute of Geomatics, Department of Civil Engineering, Tsinghua University, Chengfu Road, Beijing 100084, China"}]}],"member":"1968","published-online":{"date-parts":[[2013,7,5]]},"reference":[{"key":"ref_1","unstructured":"Shapiro, L.G., and Stockman, G.C. (2011). Computer Vision, Prentice-Hall."},{"key":"ref_2","unstructured":"Willhauck, G., Schneider, T., de Kok, R., and Ammer, U (2000, January 16\u201323). Comparison of Object Oriented Classification Techniques and Standard Image Analysis for the Use of Change Detection between SPOT Multispectral Satellite Images and Aerial Photos. Amsterdam, The Netherlands."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1080\/01431160210144499","article-title":"A method for object-oriented land cover classification combining Landsat TM data and aerial photographs","volume":"24","author":"Geneletti","year":"2003","journal-title":"Int. J. Remote Sens"},{"key":"ref_4","first-page":"312","article-title":"Edge-guided segmentation method for multiscale and high resolution remote sensing image","volume":"29","author":"Tan","year":"2010","journal-title":"J. Infrared Millim. Waves"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1109\/34.400568","article-title":"Mean shift, mode seeking, and clustering","volume":"17","author":"Cheng","year":"1995","journal-title":"IEEE Trans. Pat. Anal. Mach. Intell"},{"key":"ref_6","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. Pat. Anal. Mach. Intell"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Georgescu, B., Shimshoni, I., and Meer, P (2003, January 13\u201316). Mean Shift based Clustering in High Dimensions: A Texture Classification Example. Nice, France.","DOI":"10.1109\/ICCV.2003.1238382"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2065","DOI":"10.1111\/j.1467-8659.2010.01793.x","article-title":"Efficient mean-shift clustering using gaussian KD-tree","volume":"29","author":"Xiao","year":"2010","journal-title":"Comput. Graph. Forum"},{"key":"ref_9","first-page":"177","article-title":"Fast segmentation algorithm of high resolution remote sensing image based on multiscale mean shift","volume":"31","author":"Wang","year":"2011","journal-title":"Spectrosc. Spectr. Anal"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"187","DOI":"10.3233\/FI-2000-411207","article-title":"The watershed transform: Definitions, algorithms and parallelization strategies","volume":"41","author":"Roerdink","year":"2000","journal-title":"Fundam. Informa"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1006\/cviu.1999.0822","article-title":"Watershed-based segmentation and region merging","volume":"77","author":"Bleau","year":"2000","journal-title":"Comput. Vis. Image Underst"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1080\/18756891.2012.670521","article-title":"Region-based image segmentation by watershed partition and DCT energy compaction","volume":"5","author":"Pun","year":"2012","journal-title":"Int. J. Comput. Intell. Syst"},{"key":"ref_13","unstructured":"Baatz, M., and Sch\u00e4pe, A (2000, January 5\u20137). Multiresolution Segmentation: An Optimization Approach for High Quality Multi-scale Image Segmentation. Heidelberg, Germany."},{"key":"ref_14","unstructured":"Definients Image (2004). eCognition User\u2019s Guide 4, Definients Image."},{"key":"ref_15","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. Pat. Anal. Mach. Intell"},{"key":"ref_16","unstructured":"(2008). ENVI Feature Extraction Module User\u2019s Guide, ITT Corporation. [2008th ed.]."},{"key":"ref_17","unstructured":"Robinson, D.J., Redding, N.J., and Crisp, D.J. (2002). Implementation of a Fast Algorithm for Segmenting SAR Imagery, DSTO Electronics and Surveillance Research Laboratory."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/BF00288933","article-title":"Quad trees: A data structure for retrieval on composite keys","volume":"4","author":"Finkel","year":"1974","journal-title":"Acta Inform"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1109\/83.941855","article-title":"Multiscale image segmentation using wavelet-domain hidden Markov models","volume":"10","author":"Choi","year":"2001","journal-title":"IEEE Trans. Image Process"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1109\/34.49050","article-title":"Integrating region growing and edge detection","volume":"12","author":"Pavlidis","year":"1990","journal-title":"IEEE Trans. Pat. Anal. Mach. Intell"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kelkar, D., and Gupta, S (2008, January 16\u201318). Improved Quadtree Method for Split Merge Image Segmentation. Nagpur, India.","DOI":"10.1109\/ICETET.2008.145"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1023\/B:VISI.0000013087.49260.fb","article-title":"Robust real-time face detection","volume":"57","author":"Viola","year":"2004","journal-title":"Int. J. Comput. Vis"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/0734-189X(83)90017-8","article-title":"A data structure and algorithm based on a linear key for a rectangle retrieval problem","volume":"24","author":"Abel","year":"1983","journal-title":"Comput. Vis. Graph. Image Process"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1109\/TSMC.1978.4309999","article-title":"Texture features corresponding to visual perception","volume":"8","author":"Tamura","year":"1978","journal-title":"IEEE Trans. Syst. 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