Identification of coastal reclamation from GF-1 imagery using ensemble classification strategy
WU Junchao1,2, LI Liwei2, HU Shengwu1
1. School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China;
2. Key Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
The coastal reclamation is an important way for people to access marine resources. Monitoring the coastal reclamation changes is an important task in coastal zone management and coastal zone evolution study. However, the coastal reclamation feature is complex, and it is difficult for remote sensing techniques to efficiently monitor reclamation. In this paper, the authors propose an ensemble classification algorithm for identifying four categories of reclamation using GF-1 imagery. The ensemble classification is constructed based on minimum distance algorithm and 10 features from manually extracted image objects. The 10 features include four mean features of each object in the four bands of GF-1 imagery respectively, mean value of the four mean features, object size, object perimeter, external rectangular area, ratio of object area, external rectangular area, ratio of object perimeter and object area. The proposed method was extensively tested by using two GF-1 images from 2013 and 2014. The results show that the highest accuracy of single feature model is up to 82.03%, and the accuracy of spectral features based ensemble model and that of the spatial features based ensemble model are 63.28% and 87.50% respectively, and the accuracy of full feature based ensemble model is 80.47%. This study provides a useful solution for monitoring the coastal reclamation.
吴军超, 李利伟, 胡圣武. 基于多分类器集成的GF-1影像围填海地物识别[J]. 国土资源遥感, 2017, 29(1): 143-148.
WU Junchao, LI Liwei, HU Shengwu. Identification of coastal reclamation from GF-1 imagery using ensemble classification strategy. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(1): 143-148.
[1] 张霞,庄智,张旭凯,等.秦皇岛市海岸线遥感提取及变化监测[J].遥感技术与应用,2014,29(4):625-630. Zhang X,Zhuang Z,Zhang X K,et al.Coastline extraction and change monitoring by remote sensing technology in Qinhuangdao City[J].Remote Sensing Technology and Application,2014,29(4):625-630.
[2] 粟云召,与君宝,韩广轩,等.基于遥感的黄河三角洲海岸线变化研究[J].研究报告,2012,36(4):99-106. Li Y Z,Yu J B,Han G X,et al.Coastline change detection of the Yellow River Delta by satellite remote sensing[J].Marine Sciences,2012,36(4):99-106.
[3] 高志强,刘向阳,宁吉才,等.基于遥感的近30a中国海岸线和围填海面积变化及成因分析[J].农业工程学报,2014,30(12):140-147. Gao Z Q,Liu X Y,Ning J C,et al.Analysis on changes in coastline and reclamation area and its causes based on 30-year satellite data in China[J].Transactions of the Chinese Society of Agricultural Engineering,2014,30(12):140-147.
[4] Alesheikh A A,Ghorbanali A,Nouri N.Coastline change detection using remote sensing[J].Technology Journal of Environmental Science & Technology,2007,4(1):61-66.
[5] 李秀梅,袁承志,李月洋.渤海湾海岸带遥感监测及时空变化[J].国土资源遥感,2013,25(2):156-163.doi:10.6046/gtzyyg.2013.02.26. Li X M,Yuan C Z,Li Y Y.Remote sensing monitoring and spatial-temporal variation of Bohai Bay coastal zone[J].Remote Sensing for Land and Resources,2013,25(2):156-163.doi:10.6046/gtzyyg.2013.02.26.
[6] 张旭凯,张霞,杨邦会,等.结合海岸类型和潮位校正的海岸线遥感提取[J].国土资源遥感,2013,25(4):91-97.doi:10.6046/gtzyyg.2013.04.15. Zhang X K,Zhang X,Yang B H,et al.Coastline extraction using remote sensing based on coastal type and tidal correction[J].Remote Sensing for Land and Resources,2013,25(4):91-97.doi:10.6046/gtzyyg.2013.04.15.
[7] 刘鹏程.基于水平集理论的海岸线轮廓特征提取[J].国土资源遥感,2015,27(2):75-79.doi:10.6046/gtzyyg.2015.02.12. Liu P C.Feature extraction of coastline contour based on level set theory[J].Remote Sensing for Land and Resources,2015,27(2):75-79.doi:10.6046/gtzyyg.2015.02.12.
[8] 徐进勇,张增祥,赵晓丽,等.围填海遥感监测方法研究[J].测绘通报,2014(5):60-62,75. Xu J Y,Zhang Z X,Zhao X L,et al.Remote sensing monitoring method of land reclamation[J].Bulletin of Surveying and Mapping,2014(5):60-62,75.
[9] 周小成,汪小钦,向天梁,等.基于ASTER影像的近海水产养殖信息自动提取方法[J].湿地科学,2006,4(1):64-68. Zhou X C,Wang X Q,Xiang T L,et al.Method of automatic extracting seaside aquaculture land based on ASTER remote sensing image[J].Wetland Science,2006,4(1):64-68.
[10] 鞠明明.基于面向对象图像分析的围填海工程遥感信息提取技术研究[D].南京:南京师范大学,2013. Ju M M.Remote-Sensing-Based Information Extraction of Reclamation Project Using Object-Oriented Image Analysis Technique[D].Nanjing:Nanjing Normal University,2013.
[11] 鞠明明,汪闽,张东,等.基于面向对象图像分析技术的围填海用海工程遥感监测[J].海洋通报,2013,32(6):678-684. Ju M M,Wang M,Zhang D,et al.Study on the remote-sensing-based monitoring of reclamation project area by using the object-oriented image analysis technique[J].Marine Science Bulletin,2013,32(6):678-684.
[12] 汪闽,万其明,张大骞,等.光谱、形状特征结合的多精度图像分割算法与应用[J].地球信息科学学报,2010,12(2):261-268. Wang M,Wan Q M,Zhang D Q,et al.Multi-resolution remotely sensed image segmentation in combination with spectral and shape features[J].Journal of Geo-Information Science,2010,12(2):261-268.
[13] Du P J,Xia J S,Zhang W,et al.Multiple classifier system for remote sensing image classification:A review[J].Sensors,2012,12(4):4764-4792.
[14] Briem G J,Benediktsson J A,Sveinsson J R.Multiple classifiers applied to multisource remote sensing data[J].IEEE Transactions on Geoscience and Remote Sensing,2002,40(10):2291-2299.
[15] 国家海洋局.HY/T123-2009海域使用分类[S].北京:中国标准出版社,2009. State Oceanic Administration Peoples Republic Of China.HY/T123-2009 Sea Area Use Classification[S].Beijing:China Standards Publishing House,2009.
2017, Vol. 29 
