农业工程学报
農業工程學報
농업공정학보
2013年
18期
136-145
,共10页
王利民%刘佳%杨玲波%陈仲新%王小龙%欧阳斌
王利民%劉佳%楊玲波%陳仲新%王小龍%歐暘斌
왕이민%류가%양령파%진중신%왕소룡%구양빈
遥感%影像处理%农业%监测%无人机
遙感%影像處理%農業%鑑測%無人機
요감%영상처리%농업%감측%무인궤
remote sensing%image processing%agriculture%monitoring%unmanned aerial vehicle (UAV)
该文以中国农业科学院（万庄）农业高新技术产业园及周边地区4.2×3.1 km的范围为研究区域，利用无人机搭载RICOH GXR A12型相机进行了航拍试验，主要测试了定位定向系统（positioning and orientation system， POS）数据辅助下光束法区域网平差方法平面定位及面积测量精度，以及无人机影像的作物面积识别精度。结果表明，在无控制点约束条件下，直接采用POS数据进行光束法区域网平差后，以中误差表示的平面定位精度为X轴方向（东西方向）中误差为2.29 m，Y轴方向（南北方向）中误差为2.78 m，整体平面中误差3.61 m；采用3阶一般多项式模型进行几何精校正，X轴方向中误差为1.59 m，Y轴方向中误差为1.8965 m，整体平面中误差为2.32 m，符合《数字航空摄影测量空中三角测量规范》中对1∶10000平地的平面精度要求，能够满足农作物面积遥感监测中作物面积调查定位精度的要求；采用监督分类和面向对象分类2种方法，对面积评价区域种植的春玉米、夏玉米、苜蓿和裸土4种地物类型进行分类，以差分GPS调查结果为评价标准，4种作物总体识别精度分别达到了88.2%（监督分类）和92.0%（面向对象分类），单独分类精度分别为88.9%、86.7%、93.0%、86.6%和90.35%、92.61%、94.93%、93.30%。研究结果说明了无人机遥感影像获取小范围、样方式分布的作物影像方面具有广泛的应用前景，推广后能够满足全国农作物地面样方对高空间分辨率影像的需求，可以部分替代现有人工GPS测量的作业方式。
該文以中國農業科學院（萬莊）農業高新技術產業園及週邊地區4.2×3.1 km的範圍為研究區域，利用無人機搭載RICOH GXR A12型相機進行瞭航拍試驗，主要測試瞭定位定嚮繫統（positioning and orientation system， POS）數據輔助下光束法區域網平差方法平麵定位及麵積測量精度，以及無人機影像的作物麵積識彆精度。結果錶明，在無控製點約束條件下，直接採用POS數據進行光束法區域網平差後，以中誤差錶示的平麵定位精度為X軸方嚮（東西方嚮）中誤差為2.29 m，Y軸方嚮（南北方嚮）中誤差為2.78 m，整體平麵中誤差3.61 m；採用3階一般多項式模型進行幾何精校正，X軸方嚮中誤差為1.59 m，Y軸方嚮中誤差為1.8965 m，整體平麵中誤差為2.32 m，符閤《數字航空攝影測量空中三角測量規範》中對1∶10000平地的平麵精度要求，能夠滿足農作物麵積遙感鑑測中作物麵積調查定位精度的要求；採用鑑督分類和麵嚮對象分類2種方法，對麵積評價區域種植的春玉米、夏玉米、苜蓿和裸土4種地物類型進行分類，以差分GPS調查結果為評價標準，4種作物總體識彆精度分彆達到瞭88.2%（鑑督分類）和92.0%（麵嚮對象分類），單獨分類精度分彆為88.9%、86.7%、93.0%、86.6%和90.35%、92.61%、94.93%、93.30%。研究結果說明瞭無人機遙感影像穫取小範圍、樣方式分佈的作物影像方麵具有廣汎的應用前景，推廣後能夠滿足全國農作物地麵樣方對高空間分辨率影像的需求，可以部分替代現有人工GPS測量的作業方式。
해문이중국농업과학원（만장）농업고신기술산업완급주변지구4.2×3.1 km적범위위연구구역，이용무인궤탑재RICOH GXR A12형상궤진행료항박시험，주요측시료정위정향계통（positioning and orientation system， POS）수거보조하광속법구역망평차방법평면정위급면적측량정도，이급무인궤영상적작물면적식별정도。결과표명，재무공제점약속조건하，직접채용POS수거진행광속법구역망평차후，이중오차표시적평면정위정도위X축방향（동서방향）중오차위2.29 m，Y축방향（남북방향）중오차위2.78 m，정체평면중오차3.61 m；채용3계일반다항식모형진행궤하정교정，X축방향중오차위1.59 m，Y축방향중오차위1.8965 m，정체평면중오차위2.32 m，부합《수자항공섭영측량공중삼각측량규범》중대1∶10000평지적평면정도요구，능구만족농작물면적요감감측중작물면적조사정위정도적요구；채용감독분류화면향대상분류2충방법，대면적평개구역충식적춘옥미、하옥미、목숙화라토4충지물류형진행분류，이차분GPS조사결과위평개표준，4충작물총체식별정도분별체도료88.2%（감독분류）화92.0%（면향대상분류），단독분류정도분별위88.9%、86.7%、93.0%、86.6%화90.35%、92.61%、94.93%、93.30%。연구결과설명료무인궤요감영상획취소범위、양방식분포적작물영상방면구유엄범적응용전경，추엄후능구만족전국농작물지면양방대고공간분변솔영상적수구，가이부분체대현유인공GPS측량적작업방식。
By taking Agricultural High-tech Industrial Park of Chinese Academy of Agricultural Sciences (Wan Zhuang) and its peripheral regions with a total area of 4.2 × 3.1 km as the study area, this paper carried out an aerial photogrammetry experiment by using the RICOH GXR A12 camera carried on an unmanned aerial vehicle (UAV), and the experiment mainly tested the precisions of planar positioning under a POS (positioning and orientation system) supported bundle block adjustment method and of area measurement, as well as the precision of the crop area identification of an UAV orthophoto map obtained from an aerial triangulation correction. We use an unmanned aerial vehicle (UAV) to obtain 690 images which covered the whole study area. After a series of processes such as image screen, POS-supported aerial triangulation correction, digital elevation model making, image fusion, and digital differential rectification, we have obtained the ortho-photo map of the whole study area. Since the deployment of high precision ground control point wastes time and energy, POS-supported aerial triangulation employs a non-control point model. Therefore, its absolute positioning precision may be affected by the error of the GPS carried on an UAV. In order to eliminate this error, the project team used a high precision wordview image to rectify the ortho-photo map. In this way, we could improve the image positioning precision, and meanwhile unify the study sample areas with the overall larger scope image coordinate system, so as to provide high precision samples for large-scale agriculture remote sensing statistics and monitoring. The result shows that, under the condition of no control point and after direct POS data bundle block adjustment, the mean square error of plane positioning precision of the X axis direction is 2.29 m, Y direction is 2.78 m, and overall plane error is 3.61 m. If a three order general polynomial model is adopted to conduct a geometric precision correction, then the mean square error of the X axis direction is 1.59 m, the Y direction is 1.8965 m, and the mean square error of the overall plane is 2.32 m. The above figures conform to the 1:10 000 ground plane precision requirements specified in the‘Standard for Aerotriangulation of Digital Aerophotogrammetry’ and can meet the positioning precision requirements of a crop area survey in remote sensing monitoring. After obtaining the ortho-photo map, the four ground objects in the area evaluation areas of spring corn, summer corn, alfalfa, and bare soil were classified by employing two methods of supervised classification and object-oriented classification. By taking the differential GPS survey results as the evaluation criteria, the overall precisions of the four crops reached 88.2% (supervised classification) and 92.0% (object-oriented classification) respectively. The separate classification precisions of the two classification methods of the four ground objects were 88.9%, 86.7%, 93.0%, 86.6%, and 90.35%, as well as 90.35%, 92.61%, 94.93%, and 93.30%respectively. The result showed that remote sensing images of unmanned aerial vehicle (UAV), by acquiring small scale and quadrat sampled crop images, have a prospect of wide application. After promotion, it can meet the demands of nationwide crop ground sampling on high spatial resolution images, and can partially replace the operation model of GPS measurement.
