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Deriving a flow coherent surface for runoff simulation in urban areas using light detection and ranging data and multispectral imagery

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JOURNAL OF APPLIED REMOTE SENSING 2014年第1期8卷

作者： de Almeida Pereira, Gabriel Henrique Silva Centeno, Jorge Antonio Univ Fed Parana BR-80060000 Curitiba Parana Brazil

This work addresses the topic of flow direction and flow accumulation simulations in urban areas over digital surface models derived from light detection and ranging (LiDAR) data and multispectral high-resolution imagery. LiDAR data are very dense point clouds that include many objects that, in a 2 1/2-dimensional model, may become false obstacles for runoff, such as power lines or treetops. The presence of such obstacles is a problem for the flow paths simulation, especially in urban areas. We describe a methodology to produce a surface model more suitable for runoff modeling, by filtering objects that are above the surface and should not influence the flow paths. In a first step, thin obstacles are suppressed by applying mathematical morphology to a raster surface model. In a second step, satellite multispectral data and LiDAR data are classified using a support vector machine to identify trees, which are also removed from the digital model, and produce a more coherent surface model for runoff simulation. To simulate and evaluate the results, the flow-routing algorithm Dinfinity was used. The results show that the filtering is necessary to achieve a better characterization of runoff paths and allows identifying places where runoff may accumulate, causing floods or other problems. (C) 2014 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： light detection and ranging data complex urban environment mathematical morphology support vector machine Dinfinity algorithm runoff simulation

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Wind Shear Prediction from light detection and ranging data Using Machine Learning Methods

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ATMOSPHERE 2021年第5期12卷 644-644页

作者： Huang, Jingyan Ng, Michael Kwok Po Chan, Pak Wai Natl Univ Singapore Dept Stat & Appl Probabil Singapore 119077 Singapore Univ Hong Kong Dept Math Hong Kong 999077 Peoples R China Hong Kong Observ Aviat Weather Serv Hong Kong 999077 Peoples R China

The main aim of this paper is to propose a statistical indicator for wind shear prediction from light detection and ranging (LIDAR) observational data. Accurate warning signal of wind shear is particularly important for aviation safety. The main challenges are that wind shear may result from a sustained change of the headwind and the possible velocity of wind shear may have a wide range. Traditionally, aviation models based on terrain-induced setting are used to detect wind shear phenomena. Different from traditional methods, we study a statistical indicator which is used to measure the variation of headwinds from multiple headwind profiles. Because the indicator value is nonnegative, a decision rule based on one-side normal distribution is employed to distinguish wind shear cases and non-wind shear cases. Experimental results based on real data sets obtained at Hong Kong International Airport runway are presented to demonstrate that the proposed indicator is quite effective. The prediction performance of the proposed method is better than that by the supervised learning methods (LDA, KNN, SVM, and logistic regression). This model would also provide more accurate warnings of wind shear for pilots and improve the performance of Wind shear and Turbulence Warning System.

关键词： machine learning methods light detection and ranging data prediction models wind shear detection

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Point Spread Function (PSF) Noise Filter Strategy for Geiger Mode LiDAR

Point Spread Function (PSF) Noise Filter Strategy for Geiger...

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Conference on Laser Radar Technology and Applications XVIII

作者： Smith, O'Neil Stark, Robert Smith, Philip St Romain, Randall Blask, Steve Harris Corp Melbourne FL 32902 USA

ISBN: (纸本)9780819495228

LiDAR is an efficient optical remote sensing technology that has application in geography, forestry, and defense. The effectiveness is often limited by signal-to-noise ratio (SNR). Geiger mode avalanche photodiode (APD) detectors are able to operate above critical voltage, and a single photoelectron can initiate the current surge, making the device very sensitive. These advantages come at the expense of requiring computationally intensive noise filtering techniques. Noise is a problem which affects the imaging system and reduces the capability. Common noise-reduction algorithms have drawbacks such as over aggressive filtering, or decimating in order to improve quality and performance. In recent years, there has been growing interest on GPUs (Graphics Processing Units) for their ability to perform powerful massive parallel processing. In this paper, we leverage this capability to reduce the processing latency. The Point Spread Function (PSF) filter algorithm is a local spatial measure that has been GPGPU accelerated. The idea is to use a kernel density estimation technique for point clustering. We associate a local likelihood measure with every point of the input data capturing the probability that a 3D point is true target-return photons or noise (background photons, dark-current). This process suppresses noise and allows for detection of outliers. We apply this approach to the LiDAR noise filtering problem for which we have recognized a speed-up factor of 30-50 times compared to traditional sequential CPU implementation.

关键词： Noise Filtering Geiger Mode Foliage Penetration FOPEN LiDAR light detection and ranging data Laser Radar LADAR

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3D Graph Segmentation for Target detection in FOPEN LiDAR data

3D Graph Segmentation for Target Detection in FOPEN LiDAR Da...

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Conference on Laser Radar Technology and Applications XVIII

作者： Shorter, Nicholas Locke, Judson Smith, O'Neil Keating, Emma Smith, Philip Harris Corp Melbourne FL 32902 USA

ISBN: (纸本)9780819495228

A novel use of Felzenszwalb's graph based efficient image segmentation algorithm* is proposed for segmenting 3D volumetric foliage penetrating (FOPEN) light detection and ranging (LiDAR) data for automated target detection. The authors propose using an approximate nearest neighbors algorithm to establish neighbors of points in 3D and thus form the graph for segmentation. Following graph formation, the angular difference in the points' estimated normal vectors is proposed for the graph edge weights. Then the LiDAR data is segmented, in 3D, and metrics are calculated from the segments to determine their geometrical characteristics and thus likelihood of being a target. Finally, the bare earth within the scene is automatically identified to avoid confusion of flat bare earth with flat targets. The segmentation, the calculated metrics, and the bare earth all culminate in a target detection system deployed for FOPEN LiDAR. General purpose graphics processing units (GPGPUs) are leveraged to reduce processing times for the approximate nearest neighbors and point normal estimation algorithms such that the application can be run in near real time. Results are presented on several data sets. Felzenszwalb, P. F. and D. P. Huttenlocher, "Efficient Graph Based Image Segmentation", International Journal of Computer Vision, 59( 2), September 2004

关键词： Graph based Segmentation Foliage Penetration FOPEN Target detection LiDAR light detection and ranging data LADAR GPGPU

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A deep-learning-based tree species classification for natural secondary forests using unmanned aerial vehicle hyperspectral images and LiDAR

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ECOLOGICAL INDICATORS 2024年 159卷

作者： Ma, Ye Zhao, Yuting Im, Jungho Zhao, Yinghui Zhen, Zhen Northeast Forestry Univ Sch Forestry Key Lab Sustainable Forest Ecosyst Management Minist Educ Harbin Peoples R China Ulsan Natl Inst Sci & Technol UNIST Dept Civil Urban Earth & Environm Engn Ulsan South Korea

Accurate tree species classification is essential for forest resource management and biodiversity assessment. However, classifying tree species becomes challenging in natural secondary forests due to the difficulties in outlining the tree crown boundary. In this study, an object -based framework for tree species classification in the Experimental Forestry Farm of Northeast Forestry University, located in Heilongjiang Province, China, was developed based on unmanned aerial vehicle (UAV) hyperspectral images (HSIs) and UAV light detection and ranging (LiDAR) data using convolutional neural networks (CNNs). The study area was characterized by representative natural secondary forests that encompass diverse tree species, such as Korean pine (Pinus koraiensis Sieb. et Zucc.), White birch (Betula platyphylla Suk.), Siberian elm (Ulmus pumila L.), and Manchurian ash (Fraxinus mandshurica Rupr.). This study included two key processes: (1) the u -shaped network (U -net) algorithm was employed with the simple linear iterative clustering (SLIC) algorithm, that is, the U-SLIC algorithm, for individual tree crown delineation (ITCD), and (2) the performances of one-dimensional CNN (1D -CNN), twodimensional CNN (2D -CNN), and three-dimensional CNN (3D -CNN) models for tree species classification were compared while investigating the role of an attention mechanism (convolutional block attention module, CBAM) added to CNN models (1D -/2D -/3D -CNN + CBAM). The results showed that the U-SLIC algorithm obtained a satisfactory accuracy for the ITCD procedure, with a recall of 0.92, precision of 0.79, and F -score of 0.85. The feature selection effectively enhanced the CNN models' performances for tree species classification. Furthermore, adding the CBAM resulted in overall accuracy (OA) improvements of 0.08, 0.11, and 0.09 for the 1D -CNN, 2DCNN, and 3D -CNN, respectively. The 1D -CNN + CBAM model performed best with an OA of 0.83 when utilizing the selected HSI and LiDAR features. This

关键词： light detection and ranging data Hyperspectral image Convolutional neural network Individual tree crown delineation Deep learning model interpretation

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CITNet: Convolution Interaction Transformer Network for Hyperspectral and LiDAR Image Classification

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷

作者： Wang, Minhui Sun, Yaxiu Xiang, Jianhong Zhong, Yu Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China Harbin Engn Univ Key Lab Adv Ship Commun & Informat Technol Harbin 150001 Peoples R China Agile & Intelligent Comp Key Lab Sichuan Prov Chengdu 610000 Peoples R China

Transformers are increasingly popular in computer vision, which treat an image as a sequence of image patches and learn robust global features from the sequence. However, pure transformers are not entirely suitable for hyperspectral and light detection and ranging (LiDAR) image classification because image classification requires both robust global features and discriminative local features. Therefore, this article introduces a novel convolution interaction transformer network (CITNet) for jointly classifying hyperspectral and LiDAR images. The process begins with a carefully designed multiscale asymmetric depthwise convolution (MADC) module that exploits the local-global correlations of shallow features. On this basis, a novel local-global transformer (LGTM) is equipped with a local-global feed-forward (LGF) network to extract in-depth local-global joint features from the multimodal data. Then, an optimization convolution cross-attention (OCA) module, incorporating a convolutional layer, is developed to simulate the spatial relationships of semantic tokens. Finally, extensive experiments are conducted on the well-known Trento (TR), Augsburg (AU), MUUFL (MU), and Houston2013 (HU) datasets. The overall accuracy (OA) reaches 99.76%, 97.40%, 91.06%, and 99.90%, respectively, which are 0.2%-1.66%, 0.32%-7.37%, 1.52%-12.71%, and 0.14%-93.79% higher than the state-of-the-art (SOTA) methods, demonstrating the effectiveness of CITNet in improving the joint classification accuracy of hyperspectral and LiDAR images.

关键词： Convolutional neural network (CNN) cross attention (CA) hyperspectral image (HSI) hyperspectral image (HSI) light detection and ranging data light detection and ranging data vision transformer vision transformer vision transformer

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