UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis

Remote Sensing - Tập 7 Số 1 - Trang 1074-1094
Quanlong Feng1, Jiantao Liu1, Jianhua Gong1
1State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences, No.20, Datun Road, Chaoyang District, Beijing 100101, China

Tóm tắt

Unmanned aerial vehicle (UAV) remote sensing has great potential for vegetation mapping in complex urban landscapes due to the ultra-high resolution imagery acquired at low altitudes. Because of payload capacity restrictions, off-the-shelf digital cameras are widely used on medium and small sized UAVs. The limitation of low spectral resolution in digital cameras for vegetation mapping can be reduced by incorporating texture features and robust classifiers. Random Forest has been widely used in satellite remote sensing applications, but its usage in UAV image classification has not been well documented. The objectives of this paper were to propose a hybrid method using Random Forest and texture analysis to accurately differentiate land covers of urban vegetated areas, and analyze how classification accuracy changes with texture window size. Six least correlated second-order texture measures were calculated at nine different window sizes and added to original Red-Green-Blue (RGB) images as ancillary data. A Random Forest classifier consisting of 200 decision trees was used for classification in the spectral-textural feature space. Results indicated the following: (1) Random Forest outperformed traditional Maximum Likelihood classifier and showed similar performance to object-based image analysis in urban vegetation classification; (2) the inclusion of texture features improved classification accuracy significantly; (3) classification accuracy followed an inverted U relationship with texture window size. The results demonstrate that UAV provides an efficient and ideal platform for urban vegetation mapping. The hybrid method proposed in this paper shows good performance in differentiating urban vegetation mapping. The drawbacks of off-the-shelf digital cameras can be reduced by adopting Random Forest and texture analysis at the same time.

Từ khóa


Tài liệu tham khảo

Nichol, 2005, Urban vegetation monitoring in Hong Kong using high resolution multispectral images, Int. J. Remote Sens., 26, 903, 10.1080/01431160412331291198

Small, 2001, Estimation of urban vegetation abundance by spectral mixture analysis, Int. J. Remote Sens., 22, 1305, 10.1080/01431160151144369

Zhang, 2010, Object-oriented method for urban vegetation mapping using IKONOS imagery, Int. J. Remote Sens., 31, 177, 10.1080/01431160902882603

Tigges, 2013, Urban vegetation classification: Benefits of multitemporal RapidEye satellite data, Remote Sens. Environ., 136, 66, 10.1016/j.rse.2013.05.001

Alonzo, 2014, Urban tree species mapping using hyperspectral and LiDAR data fusion, Remote Sens. Environ., 148, 70, 10.1016/j.rse.2014.03.018

Tooke, 2009, Extracting urban vegetation characteristics using spectral mixture analysis and decision tree classifications, Remote Sens. Environ., 113, 398, 10.1016/j.rse.2008.10.005

Li, 2013, Object-based urban vegetation mapping with high-resolution aerial photography as a single data source, Int. J. Remote Sens., 34, 771, 10.1080/01431161.2012.714508

Johansen, 2007, Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification, Remote Sens. Environ., 110, 29, 10.1016/j.rse.2007.02.014

Hollaus, 2012, Urban vegetation detection using radiometrically calibrated small-footprint full-waveform airborne LiDAR data, ISPRS J. Photogramm. Remote Sens., 67, 134, 10.1016/j.isprsjprs.2011.12.003

Powell, 2007, Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil, Remote Sens. Environ., 106, 253, 10.1016/j.rse.2006.09.005

Rosa, 2013, Land cover and impervious surface extraction using parametric and non-parametric algorithms from the open-source software R: An application to sustainable urban planning in Sicily, GISci. Remote Sens., 50, 231, 10.1080/15481603.2013.795307

Laliberte, 2009, Texture and scale in object-based analysis of subdecimeter resolution Unmanned Aerial Vehicle (UAV) imagery, IEEE Trans. Geosci. Remote Sens., 47, 761, 10.1109/TGRS.2008.2009355

Szantoi, 2013, Analyzing fine-scale wetland composition using high resolution imagery and texture features, Int. J. Appl. Earth Obs., 23, 204

Aguera, 2008, Using texture analysis to improve per-pixel classification of very high resolution images for mapping plastic greenhouses, ISPRS J. Photogramm. Remote Sens., 63, 635, 10.1016/j.isprsjprs.2008.03.003

Haralick, 1973, Textural features for image classification, IEEE Trans. Syst. Man Cybern., 3, 610, 10.1109/TSMC.1973.4309314

Cleve, 2008, Classification of the wildland–urban interface: A comparison of pixel- and object-based classifications using high-resolution aerial photography, Comput. Environ. Urban Syst., 32, 317, 10.1016/j.compenvurbsys.2007.10.001

Yu, 2006, Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery, Photogramm. Eng. Remote Sens., 72, 799, 10.14358/PERS.72.7.799

Breiman, 2001, Random forests, Mach. Learn., 45, 5, 10.1023/A:1010933404324

Laliberte, 2011, Multispectral remote sensing from unmanned aircraft: Image processing workflows and applications for rangeland environments, Remote Sens., 3, 2529, 10.3390/rs3112529

Qin, 2014, An object-based hierarchical method for change detection using unmanned aerial vehicle images, Remote Sens., 6, 7911, 10.3390/rs6097911

Honkavaara, 2013, Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight UAV spectral camera for precision agriculture, Remote Sens., 5, 5006, 10.3390/rs5105006

Wallace, 2012, Development of a UAV-LiDAR system with application to forest inventory, Remote Sens., 4, 1519, 10.3390/rs4061519

Hunt, 2010, Acquisition of NIR-Green-Blue digital photographs from unmanned aircraft for crop monitoring, Remote Sens., 2, 290, 10.3390/rs2010290

Rango, 2009, Unmanned aerial vehicle-based remote sensing for rangeland assessment, monitoring, and management, J. Appl. Remote Sens., 3, 1

Gong, 2012, Impacts of the Wenchuan Earthquake on the Chaping River upstream channel change, Int. J. Remote Sens., 33, 3907, 10.1080/01431161.2011.636767

Colomina, 2014, Unmanned aerial systems for photogrammetry and remote sensing: A review, ISPRS J. Photogramm. Remote Sens., 92, 79, 10.1016/j.isprsjprs.2014.02.013

Pix4D. Available online:http://pix4d.com.

Anys, 1995, Evaluation of textural and multipolarization radar features for crop classification, IEEE Trans. Geosci. Remote Sens., 33, 1170, 10.1109/36.469481

Lu, 2010, Land cover classification in a complex urban-rural landscape with QuickBird imagery, Photogramm. Eng. Remote Sens., 76, 1159, 10.14358/PERS.76.10.1159

Lu, 2007, A survey of image classification methods and techniques for improving classification performance, Int. J. Remote Sens., 28, 823, 10.1080/01431160600746456

Chen, 2002, The effect of training strategies on supervised classification at different spatial resolutions, Photogramm. Eng. Remote Sens., 68, 1155

Atkinson, 2012, Random forest classification of mediterranean land cover using multi-seasonal imagery and multi-seasonal texture, Remote Sens. Environ., 121, 93, 10.1016/j.rse.2011.12.003

Ghosh, 2014, Random forest classification of urban landscape using Landsat archive and ancillary data: Combining seasonal maps with decision level fusion, Appl. Geol., 48, 31

Puissant, 2014, Object-oriented mapping of urban trees using random forest classifiers, Int. J. Appl. Earth Obs., 26, 235

Mishra, 2014, Mapping vegetation morphology types in a dry savanna ecosystem: Integrating hierarchical object-based image analysis with random forest, Int. J. Remote Sens., 35, 1175, 10.1080/01431161.2013.876120

Hayes, 2014, High-resolution land cover classification using random forest, Remote Sens. Lett., 5, 112, 10.1080/2150704X.2014.882526

Ghimire, 2012, An assessment of the effectiveness of a random forest classifier for land-cover classification, ISPRS J. Photogramm. Remote Sens., 67, 93, 10.1016/j.isprsjprs.2011.11.002

Xu, 2014, A comparative study of different classification techniques for marine oil spill identification using RADARSAT-1 imagery, Remote Sens. Environ., 141, 14, 10.1016/j.rse.2013.10.012

Immitzer, 2012, Tree species classification with random forest using very high spatial resolution 8-band WorldView-2 satellite data, Remote Sens., 4, 2661, 10.3390/rs4092661

Paola, 1995, A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification, IEEE Trans. Geosci. Remote Sens., 33, 981, 10.1109/36.406684

Amini, 2010, A method for generating floodplain maps using IKONOS images and DEMs, Int. J. Remote Sens., 31, 2441, 10.1080/01431160902929230

EXELIS. Available online:http://www.exelisvis.com/ProductsServices/ENVIProducts.aspx.

Feature Extraction with Example-Based Classification Tutorial. Available online:http://www.exelisvis.com/docs/FXExampleBasedTutorial.html.

Thông tin
Thông tin xuất bản

Remote Sensing

Tập 7 Số 1

1074-1094

Thông tin tác giả