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吴金胜,刘红利,张锦水.无人机遥感影像面向对象分类方法估算市域水稻面积[J].农业工程学报,2018,34(1):70-77.DOI:10.11975/j.issn.1002-6819.2018.01.10
无人机遥感影像面向对象分类方法估算市域水稻面积
投稿时间:2017-06-08  修订日期:2017-11-12
中文关键词:  无人机  抽样  遥感  单季晚稻  面向对象分类  目视解译  面积推断
基金项目:国家自然科学基金(41301444)
作者单位
吴金胜 1.北京师范大学地表过程与资源生态国家重点实验室北京 100875; 2. 北京师范大学地理科学学部遥感科学与工程研究院北京 100875; 3. 国家统计局山东调查总队济南 250001; 
刘红利 1.北京师范大学地表过程与资源生态国家重点实验室北京 100875; 2. 北京师范大学地理科学学部遥感科学与工程研究院北京 100875; 
张锦水 1.北京师范大学地表过程与资源生态国家重点实验室北京 100875; 2. 北京师范大学地理科学学部遥感科学与工程研究院北京 100875; 
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中文摘要:针对如何高效地从无人机遥感影像中提取农作物样方数据,用于农作物面积遥感估算,该文以浙江省平湖市为例,利用面向对象分类方法对无人机影像进行水稻自动化识别,作为样方数据与卫星遥感全覆盖空间分布分类结果结合,采用分层联合比估计进行2014年单季晚稻面积估算。然后,与人工目视解译识别方法获取的水稻样方数据推断的区域水稻面积估算的结果进行精度、效率对比分析。研究结果表明:1)利用面向对象分类方法对无人机影像进行分类,总体分类精度达到93%以上,满足构建样本的要求;2)通过区域作物估算对比分析发现,面向对象分类方法对无人机影像进行水稻识别,构建平湖市单季晚稻的样方数据,能够替代人工目视解译样方准确推断区域作物种植面积,有效地提高了无人机影像在遥感面积估算中的应用效率。
Wu Jinsheng,Liu Hongli,Zhang Jinshui.Paddy planting acreage estimation in city level based on UAV images and object-oriented classification method[J].Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE),2018,34(1):70-77.DOI:10.11975/j.issn.1002-6819.2018.01.10
Paddy planting acreage estimation in city level based on UAV images and object-oriented classification method
Author NameAffiliation
Wu Jinsheng 1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China; 2. Department of Geography, Institute of Remote Sensing Science and Engineering, Beijing Normal University, Beijing 100875, China; 3. Shandong Investigation Corps of National Bureau of Statistics of the People's Republic of China, Jinan 250001, China; 
Liu Hongli 1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China; 2. Department of Geography, Institute of Remote Sensing Science and Engineering, Beijing Normal University, Beijing 100875, China; 
Zhang Jinshui 1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China; 2. Department of Geography, Institute of Remote Sensing Science and Engineering, Beijing Normal University, Beijing 100875, China; 
Key words:unmanned aerial vehicles  sampling  remote sensing  single late rice  object-oriented classification  visual interpretation  area estimation
Abstract: Unmanned aerial vehicle (UAV) is a highly efficient technology to detect land surface, which is capable of acquiring centimeter-level spatial resolution data and acquiring ground survey samples information timely and accurately. This is the basis for large-scale crop acreage estimation. However, the usual procedure of UAV image for sampling plot identification is still hand-generated, which leads to time-consuming and high cost expense. Therefore, it is urgent to develop an efficient approach to extract crop acreage in sampling plot from UAV orthographic image to support the regional crop acreage estimation. In this paper, we introduced an object-oriented automatic classification method, instead of the visually digitized method, to identify UAV quadrat paddy, and the combination of the quadrat paddy data and the paddy classification result from satellite remote sensing was exploited to estimate the paddy planting acreage. The comprehensive comparison between the methods of manual visual interpretation and object-oriented classification was carried out. This experiment was conducted in Pinghu City, Zhejiang Province. The integrated satellite imageries of Chinese GF-1 WFV sensor and American Landsat8 OLI were acquired. According to paddy phenology calendar of the study area, there were 2 key phases for the paddy identification. In middle June, paddy was in the seedling growing season, which represented the water spectrum. However, in later July, the paddy rice field showed the vegetation spectral information in satellite imagery, which was at tillering stage. The information sources of GF-1 WFV and Landsat8 OLI acquired at the respective phenological stages were sufficient to map paddy rice distribution. First, the data were preprocessed for geometric correction, and atmospheric correction was applied for both satellite images. Support vector machine (SVM) was used to identify the vegetation and water components from GF-1 data and Landsat8 data, respectively. Then, a logical "and" operation was conducted between vegetation and water to generate the paddy rice spatial distribution. UAV images were obtained from T10 Bumblebee platform, and a total of 7 sampling belts were acquired. Amount of UAV photos was firstly tiled by Pix4D mapper to generate UAV images with the resolution of 0.08 m. Then, UAV images were segmented by multi-scale algorithm in eCognition Developer, the segmentation scale was set to 50, 100, 150, 200 and 250 and the spectral standard deviation of image objects and its rate were calculated at each scale to determine the optimal segmentation scale. After various tests, the choice of 200 was decided as the optimal segmentation scale to describe the field boundary clearly and correctly. Then, different features were constructed and nearest neighbor classification method was adopted to extract the paddy planting distribution in sampling belts. A framework of 300 m × 300 m square grids was built covering the extent of paddy as the primary sample unit, and then the sampling ratios were calculated using acreage index to allocate the samples in each stratum. Then, the regional paddy rice acreage was estimated by combining extracted paddy rice acreage based on the satellite remote sensing and UAV sampling belt. The results showed that the overall classification accuracy of paddy from UAV image was more than 93% for the object-oriented automatic classification method, which met the basic requirements of building the samples. Most of all, the difference of CVs (coefficient of variations) of the acreage estimation aided by automatic classification quadrat data and manual visual interpretation quadrat data was 0.0008, which stated the object-oriented automatic classification could achieve the same estimation performance as artificial visual interpretation method. That implies it is feasible to apply the object-oriented automatic classification in place of the visual digitization method to extract the quadrat data to support the regional paddy acreage estimation. This achievement can be applied and tested extensively in large-scale areas and with different crops.
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