Abstract
Aiming at applying unmanned aerial vehicle (UAV) remote sensing technology in extracting individual standing tree information, a new automatic single‐tree information extraction method is proposed in this paper. The spectral information enhancement processing was performed on the original UAV image to highlight detailed local features; by importing DBI index, the optimal cluster number of the K‐means clustering was automatically determined, and image pixels were then marked; Gauss Markov random field (GMRF) model was employed to segment the image further; by mathematical morphology, operators to postprocess the segmentation results to obtain the individual standing tree crown information, and individual standing tree position was calculated through image geometric moment as the basis for its identification. The results show that with the proposed extraction method, the overall accuracy of standing tree identification for the Pinus sylvestris and Pinus tabulaeformis forest areas are 95.65% and 89.52%; the single‐tree crown extraction accuracy is 95.65% and 81.90%, respectively. This method exhibits good applicability while it does not require a large amount of manual intervention and prior knowledge, which significantly improves the automation of information extraction.
Highlights
The extraction of forestland information is one of the most important fields of remote sensing technology applications
The flow chart of the information extraction of individual standing trees based on the high spatial resolution unmanned aerial vehicle (UAV) remote sensing image is shown in Figure 3, the left columns display technical methods used, and the operational targets to be achieved are listed in the middle
It can be seen that the minimum DBI of the Pinus sylvestris area is 1.07897, and the number of classifications is 6, and it is taken as the optimal number of classifications; The minimum value of DBI is 1.02295 of the Pinus tabulaeformis forest, and the number of classifications is 4, which is regarded as the optimal number of classifications
Summary
The extraction of forestland information is one of the most important fields of remote sensing technology applications. The traditional machine vision features include hog, sift, surf, orb, LBP, and Haar. These features can only extract limited information, not enough to support the subsequent detection task. The standing tree information is traditionally obtained by manual field measurement, which is slow with high cost [5].The application of remote sensing technology and methods improves the efficiency of an individual [6, 7] standing tree information extraction and provides a broader range of data sources including high spatial resolution satellite image data [7, 8], airborne hyperspectral, multispectral image data, and airborne radar data (such as IKONOS, QuickBird, and WorldView series) [9]. Target detection technologies mainly include one-stage and two-stage technologies, including RCNN, fast RCNN, and fast RCNN in one stage, and Yolo and SSD in the other stage
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