Browsing by Author "Tong, Fei"

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    Delineation of Individual Tree Crowns Using High Spatial Resolution Multispectral WorldView-3 Satellite Imagery
    (Institute of Electrical and Electronics Engineers, 2021) Tong, Fei; Tong, Hengjian; Mishra, Rakesh; Zhang, Yun
    Delineation of individual tree crowns can provide valuable information for sustainable forest management and environmental protection. However, it is hard to find a reliable tree crown delineation method that can continually generate expected results in high spatial resolution multispectral satellite images, because most of the existing methods need user-assigned parameters that greatly affect the quality of the delineation results. In this article, we propose a method based on the marker-controlled watershed segmentation to delineate individual tree crowns using high spatial resolution multispectral WorldView-3 satellite imagery. A gradient binarization process is proposed to accurately locate tree crown borders. The threshold for the binarization is determined by a supervised searching process. Markers used in marker-controlled watershed segmentation are spatial local maxima detected from the information provided by tree crown borders. Moreover, the definition of spatial local maxima from the literature is improved to eliminate false treetops. To validate the performance of the proposed delineation method, delineation results are compared with those obtained from the spectral angle segmentation (SAS) method that has been proposed in literature because the quality of delineation results generated by SAS does not rely on user-assigned parameters. The experiment results in two test images demonstrate that the proposed method outperforms SAS in terms of both delineation accuracy and visual quality of the delineation map. Moreover, it is proved that the modified spatial local maxima are more reliable for detecting treetops.
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    Forestry information extraction using high spatial resolution remote sensing imagery
    (University of New Brunswick, 2023-12) Tong, Fei; Zhang, Yun
    This PhD research focuses on the development of reliable and efficient methods for individual tree crown delineation and tree species classification, which provide essential information for modern forestry management and climate change monitoring. The primary objective of this research is to develop robust methods that can accurately delineate individual tree crowns and classify tree species using high spatial resolution remote sensing imagery. By achieving this objective, the research aims to enhance the reliability and efficiency of forestry management practices and contribute to the field of remote sensing applications in forestry. For tree crown delineation task, existing tree crown delineation methods are not suitable for large areas applications, because they need highly experienced experts to manually assign suitable parameters to control the delineation results, which is time-consuming, inaccurate, and not suitable for normal users. To address this issue, this dissertation presents a tree crown delineation method utilizing marker-controlled watershed segmentation specifically designed for high spatial resolution multispectral WorldView-3 satellite imagery. To reduce the difficulty of assigning parameters, the proposed method incorporates an automated supervised search process to determine the threshold. Moreover, an enhanced definition of spatial local maximum is employed to mitigate false treetops, thereby enhancing the accuracy of treetop detection. For tree species classification task, although deep learning methods based on convolutional neural networks (CNN) have achieved promising results, challenges remain in hyperparameter tuning and the requirement for large number of labeled training samples, limiting their applicability in real-world applications. This dissertation addresses these challenges by proposing three models based on the concept of deep forest to enhance the tree species classification from high spatial resolution hyperspectral imagery. All the three proposed models only require two hyperparameters that are easy to be determined by users. To optimize the classification accuracy, two different ways to combine both fixed-size patches and shape-adaptive superpixels are proposed to fully exploit spectral-spatial information within the high spatial resolution hyperspectral imagery. To reduce the demand for labeled training samples, the active learning (AL) is perfectly integrated into the multilayer cascaded random forests classification model.