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Genping Zhao
Feed . Journal article Hyperspectral and Full-Waveform LiDAR Improve Mapping of Tropical Dry Forest’s Successional Stages Genping Zhao Arturo Sanchez-Azofeifa Kati Laakso Chuanliang Sun Lunke Fei https://doi.org/10.3390/rs13193830 Published: 24 September 2021 in Remote Sensing . Reads? 0 Downloads? 0 Abstract Cite All recommendations Accurate estimation of the degree of regeneration in tropical dry forest (TDF) is critical for conservation policymaking and evaluation. Hyperspectral remote sensing and light detection and ranging (LiDAR) have been used to characterize the deterministic successional stages in a TDF. These successional stages, classified as early, intermediate, and late, are considered a proxy for mapping the age since the abandonment of a given forest area. Expanding on the need for more accurate successional forest mapping, our study considers the age attributes of a TDF study area as a continuous expression of relative attribute scores/levels that vary along the process of ecological succession. Specifically, two remote-sensing data sets: HyMap (hyperspectral) and LVIS (waveform LiDAR), were acquired at the Santa Rosa National Park Environmental Monitoring Super Site (SRNP-EMSS) in Costa Rica, were used to generate age-attribute metrics. These metrics were then used as entry-level variables on a randomized nonlinear archetypal analysis (RNAA) model to select the most informative metrics from both data sets. Next, a relative attribute learning (RAL) algorithm was adapted for both independent and fused metrics to comparatively learn the relative attribute levels of the forest ages of the study area. In this study, four HyMap indices and five LVIS metrics were found to have the potential to map the forest ages of the study area, and compared with these results, a significant improvement was found through the fusion of the metrics on the accuracy of the generated forest age maps. By linking the age group mapping and the relative attribute mapping results, a dynamic gradient of the age-attribute transition patterns emerged. ACS Style Genping Zhao; Arturo Sanchez-Azofeifa; Kati Laakso; Chuanliang Sun; Lunke Fei. Hyperspectral and Full-Waveform LiDAR Improve Mapping of Tropical Dry Forest’s Successional Stages. Remote Sensing 2021 , 13 , 3830 . AMA Style Genping Zhao, Arturo Sanchez-Azofeifa, Kati Laakso, Chuanliang Sun, Lunke Fei. Hyperspectral and Full-Waveform LiDAR Improve Mapping of Tropical Dry Forest’s Successional Stages. Remote Sensing. 2021; 13 (19):3830. Chicago/Turabian Style Genping Zhao; Arturo Sanchez-Azofeifa; Kati Laakso; Chuanliang Sun; Lunke Fei. 2021. "Hyperspectral and Full-Waveform LiDAR Improve Mapping of Tropical Dry Forest’s Successional Stages." Remote Sensing 13, no. 19: 3830. Journal article Binocular-Vision-Based Structure From Motion for 3-D Reconstruction of Plants Yeping Peng Mingbin Yang Genping Zhao Guangzhong Cao https://doi.org/10.1109/lgrs.2021.3105106 Published: 25 August 2021 in IEEE Geoscience and Remote Sensing Letters . Reads? 0 Downloads? 0 Abstract Cite All recommendations Monitoring plant growth is essential in modern agriculture to guarantee productivity. Since manual measurement of plant characteristics is laborious and expensive, automatic measures are desirable. This can be accomplished by methods such as vision-based structure from motion (SFM) to obtain the 3-D information of a plant. An SFM method based on binocular vision is here developed to acquire the physical parameters of plants. In this method, image sequences are captured by a binocular camera from multiple views of the target plant to improve the effectiveness and simplify the implementation. The spatial relationships between adjacent images are estimated through image feature extraction and matching. A disparity map is then built and the 3-D coordinate of each image pixel is obtained by applying stereo-vision. The connected coordinates then constitute the 3-D model of the plant. By doing so, plant structure parameters, such as height, canopy size, and trunk diameter, can be derived from the 3-D model. Experimental results show that the measured plant height, the canopy width, and the trunk diameter of the target plant are within an acceptable accuracy at the millimeter level, and the mean errors of the measured sizes are all less than 2%. This demonstrates the potential value of the proposed method for online growth monitoring of agricultural plants. ACS Style Yeping Peng; Mingbin Yang; Genping Zhao; Guangzhong Cao. Binocular-Vision-Based Structure From Motion for 3-D Reconstruction of Plants. IEEE Geoscience and Remote Sensing Letters 2021 , PP , 1 -5. AMA Style Yeping Peng, Mingbin Yang, Genping Zhao, Guangzhong Cao. Binocular-Vision-Based Structure From Motion for 3-D Reconstruction of Plants. IEEE Geoscience and Remote Sensing Letters. 2021; PP (99):1-5. Chicago/Turabian Style Yeping Peng; Mingbin Yang; Genping Zhao; Guangzhong Cao. 2021. "Binocular-Vision-Based Structure From Motion for 3-D Reconstruction of Plants." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5. Journal article Assessing the Operation Parameters of a Low-altitude UAV for the Collection of NDVI Values Over a Paddy Rice Field Rui Jiang Pei Wang Yan Xu [...] Zhiyan Zhou Xiwen Luo Yubin Lan Genping Zhao Arturo Sanchez-Azofeifa Kati Laakso show less https://doi.org/10.3390/rs12111850 Published: 08 June 2020 in Remote Sensing . Reads? 0 Downloads? 0 Abstract Cite All recommendations Unmanned aerial vehicle (UAV) remote sensing platforms allow for normalized difference vegetation index (NDVI) values to be mapped with a relatively high resolution, therefore enabling an unforeseeable ability to evaluate the influence of the operation parameters on the quality of the thus acquired data. In order to better understand the effects of these parameters, we made a comprehensive evaluation on the effects of the solar zenith angle (SZA), the time of day (TOD), the flight altitude (FA) and the growth level of paddy rice at a pixel-scale on UAV-acquired NDVI values. Our results show that: (1) there was an inverse relationship between the FA (≤100 m) and the mean NDVI values, (2) TOD and SZA had a greater impact on UAV–NDVIs than the FA and the growth level; (3) Better growth levels of rice—measured using the NDVI—could reduce the effects of the FA, TOD and SZA. We expect that our results could be used to better plan flight campaigns that aim to collect NDVI values over paddy rice fields. ACS Style Rui Jiang; Pei Wang; Yan Xu; Zhiyan Zhou; Xiwen Luo; Yubin Lan; Genping Zhao; Arturo Sanchez-Azofeifa; Kati Laakso. Assessing the Operation Parameters of a Low-altitude UAV for the Collection of NDVI Values Over a Paddy Rice Field. Remote Sensing 2020 , 12 , 1850 . AMA Style Rui Jiang, Pei Wang, Yan Xu, Zhiyan Zhou, Xiwen Luo, Yubin Lan, Genping Zhao, Arturo Sanchez-Azofeifa, Kati Laakso. Assessing the Operation Parameters of a Low-altitude UAV for the Collection of NDVI Values Over a Paddy Rice Field. Remote Sensing. 2020; 12 (11):1850. Chicago/Turabian Style Rui Jiang; Pei Wang; Yan Xu; Zhiyan Zhou; Xiwen Luo; Yubin Lan; Genping Zhao; Arturo Sanchez-Azofeifa; Kati Laakso. 2020. "Assessing the Operation Parameters of a Low-altitude UAV for the Collection of NDVI Values Over a Paddy Rice Field." Remote Sensing 12, no. 11: 1850. Journal article Computational ghost imaging with uncertain imaging distance Meiyun Chen Heng Wu Ruizhou Wang [...] Zhenya He Hai Li Jinqiang Gan Genping Zhao show less https://doi.org/10.1016/j.optcom.2019.04.022 Published: 09 April 2019 in Optics Communications . Reads? 0 Downloads? 0 Abstract Cite All recommendations Imaging distance plays an essential role in image formation in computational ghost imaging (CGI). Uncertain or unknown imaging distance often leads to the deterioration of the image quality or, even worse, to the failure of imaging. Here we present a CGI technique that provides high-quality image reconstruction in an uncertain distance environment. We propose a method to generate a stable speckle field, where the size of the field remains invariant and the field does not distort during light transmission, and find that the use this field removes the constraint imposed by the imaging distance. We experimentally and numerically verify the performance of the technique by applying it to a single-detector CGI system. The results show that the proposed technique provides excellent imaging performance, even when the imaging distance is uncertain, and can be extended to existing computational-based ghost-imaging schemes. e, ACS Style Meiyun Chen; Heng Wu; Ruizhou Wang; Zhenya He; Hai Li; Jinqiang Gan; Genping Zhao. Computational ghost imaging with uncertain imaging distance. Optics Communications 2019 , 445 , 106 -110. AMA Style Meiyun Chen, Heng Wu, Ruizhou Wang, Zhenya He, Hai Li, Jinqiang Gan, Genping Zhao. Computational ghost imaging with uncertain imaging distance. Optics Communications. 2019; 445 ():106-110. Chicago/Turabian Style Meiyun Chen; Heng Wu; Ruizhou Wang; Zhenya He; Hai Li; Jinqiang Gan; Genping Zhao. 2019. "Computational ghost imaging with uncertain imaging distance." Optics Communications 445, no. : 106-110. Journal article Structured Background Modeling for Hyperspectral Anomaly Detection Fei Li Lei Zhang Xiuwei Zhang [...] Yanjia Chen Dongmei Jiang Genping Zhao Yanning Zhang show less https://doi.org/10.3390/s18093137 Published: 17 September 2018 in Sensors . Reads? 0 Downloads? 0 Abstract Cite All recommendations Background modeling has been proven to be a promising method of hyperspectral anomaly detection. However, due to the cluttered imaging scene, modeling the background of an hyperspectral image (HSI) is often challenging. To mitigate this problem, we propose a novel structured background modeling-based hyperspectral anomaly detection method, which clearly improves the detection accuracy through exploiting the block-diagonal structure of the background. Specifically, to conveniently model the multi-mode characteristics of background, we divide the full-band patches in an HSI into different background clusters according to their spatial-spectral features. A spatial-spectral background dictionary is then learned for each cluster with a principal component analysis (PCA) learning scheme. When being represented onto those dictionaries, the background often exhibits a block-diagonal structure, while the anomalous target shows a sparse structure. In light of such an observation, we develop a low-rank representation based anomaly detection framework that can appropriately separate the sparse anomaly from the block-diagonal background. To optimize this framework effectively, we adopt the standard alternating direction method of multipliers (ADMM) algorithm. With extensive experiments on both synthetic and real-world datasets, the proposed method achieves an obvious improvement in detection accuracy, compared with several state-of-the-art hyperspectral anomaly detection methods. ACS Style Fei Li; Lei Zhang; Xiuwei Zhang; Yanjia Chen; Dongmei Jiang; Genping Zhao; Yanning Zhang. Structured Background Modeling for Hyperspectral Anomaly Detection. Sensors 2018 , 18 , 3137 . AMA Style Fei Li, Lei Zhang, Xiuwei Zhang, Yanjia Chen, Dongmei Jiang, Genping Zhao, Yanning Zhang. Structured Background Modeling for Hyperspectral Anomaly Detection. Sensors. 2018; 18 (9):3137. Chicago/Turabian Style Fei Li; Lei Zhang; Xiuwei Zhang; Yanjia Chen; Dongmei Jiang; Genping Zhao; Yanning Zhang. 2018. "Structured Background Modeling for Hyperspectral Anomaly Detection." Sensors 18, no. 9: 3137. .
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