1 September 2017 The extraction of coastal windbreak forest information based on UAV remote sensing images
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Abstract
Unmanned aerial vehicle(UAV) have been increasingly used for natural resource applications in recent years as a result of their greater availability, the miniaturization of sensors, and the ability to deploy UAV relatively quickly and repeatedly at low altitudes. UAV remote sensing offer rich contextual information, including spatial, spectral and contextual information. In order to extract the information from these UAV remote sensing images, we need to utilize the spatial and contextual information of an object and its surroundings. If pixel based approaches are applied to extract information from such remotely sensed data, only spectral information is used. Thereby, in Pixel based approaches, information extraction is based exclusively on the gray level thresholding methods. To extract the certain features only from UAV remote sensing images, this situation becomes worse. To overcome this situation an object-oriented approach is implemented. By object-oriented thought, the coastal windbreak forest information are extracted by the use of UAV remote sensing images. Firstly, the images are segmented. And then the spectral information and object geometry information of images objects are comprehensively applied to build the coastal windbreak forest extraction knowledge base. Thirdly, the results of coastal windbreak forest extraction are improved and completed. The results show that better accuracy of coastal windbreak forest extraction can be obtained by the proposed method, in contrast to the pixel-oriented method. In this study, the overall accuracy of classified image is 0.94 and Kappa accuracy is 0.92.
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Weitao Shang, Weitao Shang, Zhiqiang Gao, Zhiqiang Gao, Xiaopeng Jiang, Xiaopeng Jiang, Maosi Chen, Maosi Chen, } "The extraction of coastal windbreak forest information based on UAV remote sensing images", Proc. SPIE 10405, Remote Sensing and Modeling of Ecosystems for Sustainability XIV, 104050Q (1 September 2017); doi: 10.1117/12.2272373; https://doi.org/10.1117/12.2272373
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