{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T17:09:41Z","timestamp":1763226581070,"version":"build-2065373602"},"reference-count":53,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,5,18]],"date-time":"2018-05-18T00:00:00Z","timestamp":1526601600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and development program of China","doi-asserted-by":"publisher","award":["2016YFC0802500"],"award-info":[{"award-number":["2016YFC0802500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hainan Province Natural Science Foundation","award":["2017CXTD015"],"award-info":[{"award-number":["2017CXTD015"]}]},{"name":"China National Key S &amp; T project of High resolution earth observation system","award":["30-Y20A07-9003-17\/18"],"award-info":[{"award-number":["30-Y20A07-9003-17\/18"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41471310"],"award-info":[{"award-number":["41471310"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Image segmentation is an important process and a prerequisite for object-based image analysis, but segmenting an image into meaningful geo-objects is a challenging problem. Recently, some scholars have focused on hybrid methods that employ initial segmentation and subsequent region merging since hybrid methods consider both boundary and spatial information. However, the existing merging criteria (MC) only consider the heterogeneity between adjacent segments to calculate the merging cost of adjacent segments, thus limiting the goodness-of-fit between segments and geo-objects because the homogeneity within segments and the heterogeneity between segments should be treated equally. To overcome this limitation, in this paper a hybrid remote-sensing image segmentation method is employed that considers the objective heterogeneity and relative homogeneity (OHRH) for MC during region merging. In this paper, the OHRH method is implemented in five different study areas and then compared to our region merging method using the objective heterogeneity (OH) method, as well as the full lambda-schedule algorithm (FLSA). The unsupervised evaluation indicated that the OHRH method was more accurate than the OH and FLSA methods, and the visual results showed that the OHRH method could distinguish both small and large geo-objects. The segments showed greater size changes than those of the other methods, demonstrating the superiority of considering within- and between-segment heterogeneity in the OHRH method.<\/jats:p>","DOI":"10.3390\/rs10050781","type":"journal-article","created":{"date-parts":[[2018,5,21]],"date-time":"2018-05-21T04:07:30Z","timestamp":1526875650000},"page":"781","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Region Merging Considering Within- and Between-Segment Heterogeneity: An Improved Hybrid Remote-Sensing Image Segmentation Method"],"prefix":"10.3390","volume":"10","author":[{"given":"Yongji","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Qingyan","family":"Meng","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Sanya Institute of Remote Sensing, Sanya 572029, China"}]},{"given":"Qingwen","family":"Qi","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Jian","family":"Yang","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Sanya Institute of Remote Sensing, Sanya 572029, China"}]},{"given":"Ying","family":"Liu","sequence":"additional","affiliation":[{"name":"Sanya Institute of Remote Sensing, Sanya 572029, China"},{"name":"College of Geography, South China Normal University, Guangzhou 510631, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object based image analysis for remote sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. 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