1. Institute of Surveying and Mapping, Guilin University of Technology, Guilin 541006, China 2. Institute of Civil and Architectural Engineering, Guilin University of Technology, Guilin 541006, China 3. Institute of Information Science and Engineering, Guilin University of Technology, Guilin 541006, China
In order to explore the recognition accuracy of remote sensing technology of low-altitude UAV for surface features in agricultural areas with different forms under karst landform conditions, the authors chose three agricultural areas (each having an area size of 200 m×200 m) in Guilin City as the research object. Supported by UAV aerial images and ground survey data, the image analysis technology based on pixel and object-oriented was combined with support vector machine (SVM) algorithm, respectively, to build the remote sensing recognition model of agricultural areas under different geomorphological conditions, and the precision was comparatively studied and analyzed. The results show that the object-oriented SVM classification results retain the rough outline of the original ground features, and the plot is relatively complete, and hence this means is more suitable for the recognition of ground features in agricultural areas under karst landform conditions. Compared with the pixel based SVM classification method, the overall accuracy is higher by 6.54% , and the Kappa coefficient is higher by 0.135 . The SVM classification method based on pixel is suitable for feature recognition in agricultural areas with regular feature distribution. Compared with the object-oriented SVM classification method, the overall accuracy is higher by 2.92% and the Kappa coefficient is higher by 0.026 .
娄佩卿,陈晓雨,王疏桐,付波霖,黄永怡,唐廷元,凌铭. 基于无人机影像的喀斯特农耕区地物识别——以桂林市为例[J]. 国土资源遥感, 2020, 32(1): 216-223.
Peiqing LOU,Xiaoyu CHEN,Shutong WANG,Bolin FU,Yongyi HUANG,Tingyuan TANG,Ming LING. Object recognition of karst farming area based on UAV image: A case study of Guilin. Remote Sensing for Land & Resources, 2020, 32(1): 216-223.
Wang Y, Chen H T, Li H C . 3D path planning approach based on gravitational search algorithm for sprayer UAV[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018,49(2):28-33.
Gonzalez-Jorge H, Puente I, Roca D , et al. UAV photogrammetry application to the monitoring of rubble mound breakwaters[J]. Journal of Performance of Constructed Facilities, 2016,30(1):1-8.
Peppa M V, Mills J P, Moore P , et al. Automated co-registration and calibration in SFM photogrammetry for landslide change detection[J]. Earth Surface Processes and Landforms, 2019,44(1):287-303.
Fu B L, Li Y, Wang Y , et al. Evaluation of riparian condition of Songhua River by integration of remote sensing and field measurements[J]. Scientific Reports, 2017, 7(1):2565,1-16.
Fu B L, Wang Y, Campbell A , et al. Comparison of object-based and pixel-based random forest algorithm for wetland vegetation mapping using high spatial resolution GF-1 and SAR data[J]. Ecological Indicators, 2017,73(2):105-117.
Vapnik V . The Nature of Statistical Learning Theory[M]. Technometrics:Springer, 1995.
Cortes C, Vapnik V . Support-vector networks[J]. Machine Learning, 1995,20(3):273-297.
Jabari S, Fathollahi F, Roshan A , et al. Improving UAV imaging quality by optical sensor fusion:An initial study[J]. International Journal of Remote Sensing, 2017,38(17):4931-4953.
Akar A, Gökalp E, Akar Ö , et al. Improving classification accuracy of spectrally similar land covers in the rangeland and plateau areas with a combination of WorldView-2 and UAV images[J]. Geocarto International, 2017,32(9):990-1003.
Hao M, Deng K Z, Zhang H . Improved active contour model to extract buildings based on remotely sensed data[J]. Journal of China University of Mining and Technology, 2012,41(5):833-838.
Satoru K, Abdallah Z, Farid M , et al. Spatial and structured SVM for multilabel image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018,56(10):5948-5960.
Zhao C, Liu W, Xu Y , et al. A spectral-spatial SVM-based multi-layer learning algorithm for hyperspectral image classification[J]. Remote Sensing Letters, 2018,9(3):218-227.
Majdar R S, Ghassemian H . A probabilistic SVM approach for hyperspectral image classification using spectral and texture features[J]. International Journal of Remote Sensing, 2017,38(15):4265-4284.
Maulik U, Chakraborty D . A self-trained ensemble with semisupervised SVM:An application to pixel classification of remote sensing imagery[J]. Pattern Recognition, 2011,44(3):615-623.
Maulik U, Chakraborty D . A novel semisupervised SVM for pixel classification of remote sensing imagery[J]. International Journal of Machine Learning and Cybernetics, 2012,3(3):247-258.
Yang H Y, Zhang X J, Wang X Y . LS-SVM-based image segmentation using pixel color-texture descriptors[J]. Pattern Analysis and Applications, 2014,17(2):341-359.
Mugiraneza T, Ban Y, Haas J . Urban land cover dynamics and their impact on ecosystem services in Kigali,Rwanda using multi-temporal Landsat data[J]. Remote Sensing Applications:Society and Environment, 2018,13(1):234-246.
Li H T, Gu H Y, Han Y S , et al. Object-oriented classification of high-resolution remote sensing imagery based on an improved colour structure code and a support vector machine[J]. International Journal of Remote Sensing, 2010,31(6):1453-1470.
Malik R, Kheddam R, Belhadjaissa A . Object-oriented SVM classifier for ALSAT-2A high spatial resolution imagery:A case study of algiers urban area[C]// International Conference on Image Processing Theory.Orleans:IEEE, 2016: 35-40.
Pei H, Sun T J, Wang X Y . Object-oriented land use/cover classification based on texture features of Landsat8 OLI image[J]. Transactions of the Chinese Society of Agricultural Engineering, 2008,34(2):248-255.
Benza M, Weeks J R, Stow D A , et al. A pattern-based definition of urban context using remote sensing and GIS[J]. Remote Sensing of Environment, 2016,183:250-264.
Park S, Lee H S, Kim J . Seed growing for interactive image segmentation using SVM classification with geodesic distance[J]. Electronics Letters, 2017,53(1):22-24.
Mesas-Carrascosa F J, Rumbao I C, Torres-Sánchez J , et al. Accurate ortho-mosaicked six-band multispectral UAV images as affected by mission planning for precision agriculture proposes[J]. International Journal of Remote Sensing, 2017,38(8-10):2161-2176.
Diaz-Varela R A, Zarco-Tejada P J, Angileri V , et al. Automatic identification of agricultural terraces through object-oriented analysis of very high resolution DSMs and multispectral imagery obtained from an unmanned aerial vehicle[J]. Journal of Environmental Management, 2014,134(1):117-126.
Bazi Y, Melgani F . Convolutional SVM networks for object detection in UAV imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018,56(6):3107-3118.
Brovkina O, Cienciala E, Surovy P , et al. Unmanned aerial vehicles (UAV) for assessment of qualitative classification of Norway spruce in temperate forest stands[J]. Geo-Spatial Information Science, 2018,21(1):12-20.
Takahashi A, Doria N A D, Bedregal B R C , An introduction interval kernel-based methods applied on support vector machines[C]// Eighth International Conference on Natural Computation.Chongqing:IEEE, 2012.
Diao S J, Liu C L, Zhang T , et al. Extraction of remote sensing information for lake salinity level based on SVM:A case from Badain Jaran desert in Inner Mongolia[J]. Remote Sensing for Land and Resources, 2016,28(4):114-118.doi: 10.6046/gtzyyg.2016.04.18.