Automatic Building Extraction from Multispectral LiDAR Using Novel 3D Spatial Indices and Deep Learning Point CNN

Journal of the Indian Society of Remote Sensing, Journal Year: 2024, Volume and Issue: 52(10), P. 2267 - 2280

Published: July 16, 2024

Language: Английский

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ANN-Based Filtering of Drone LiDAR in Coastal Salt Marshes Using Spatial–Spectral Features

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(18), P. 3373 - 3373

Published: Sept. 11, 2024

Salt marshes provide diverse habitats for a wide range of creatures and play key defensive buffering role in resisting extreme marine hazards coastal communities. Accurately obtaining the terrains salt is crucial comprehensive management conservation resources ecology. However, dense vegetation coverage, periodic tide inundation, pervasive ditch distribution create challenges measuring or estimating marsh terrains. These environmental factors make most existing techniques methods ineffective terms data acquisition resolution, accuracy, efficiency. Drone multi-line light detection ranging (LiDAR) has offered fire-new perspective 3D point cloud potentially exhibited great superiority accurately deriving The prerequisite terrain characterization from drone LiDAR filtering, which means that ground points must be discriminated non-ground points. Existing filtering typically rely on either geometric intensity features. may not perform well with dense, diverse, complex vegetation. This study proposes new method clouds based artificial neural network (ANN) machine learning model. First, series spatial–spectral features at individual (e.g., elevation, distance, intensity) neighborhood eigenvalues, linearity, sphericity) scales are derived original data. Then, selected to remove related redundant ones optimizing performance ANN Finally, reserved integrated as input variables model characterize their nonlinear relationships categories (ground non-ground) different perspectives. A case two typical mouth Yangtze River, using 6-line LiDAR, demonstrates effectiveness generalization proposed method. average G-mean AUC achieved were 0.9441 0.9450, respectively, outperforming traditional information-based other advanced methods, deep (RandLA-Net). Additionally, integration individual–neighborhood results better outcomes than single-type single-scale offers an innovative strategy derivation under novel solution deeply integrating radiometric

Language: Английский

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Filtering Airborne LiDAR Data in Forested Environments Based on Multi-Directional Narrow Window and Cloth Simulation

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(5), P. 1400 - 1400

Published: March 2, 2023

Ground filtering is one of the essential steps for processing airborne light detection and ranging data in forestry applications. However, performance existing methods still limited forested areas due to complex terrain dense vegetation. To overcome this limitation, we proposed an improved surface-based filter based on multi-directional narrow window cloth simulation. The innovations mainly involve two aspects as follows: (1) sufficient uniformly distributed ground seeds are identified by merging lowest points line segments from point clouds within a window; (2) complete accurate extracted using cyclic scheme that includes incorrect elimination internal force adjustment simulation, reconstruction with moving least-squares plane fitting, extraction progressively refined terrain. method was tested five sites various characteristics vegetation distributions. Experimental results showed could accurately separate non-ground different environments, average kappa coefficient 88.51% total error 4.22%. Moreover, comparative experiments proved performed better than classical involving slope-based, mathematical morphology-based methods.

Language: Английский

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Classification of Forest LiDAR Data Using Deep Learning Pipeline Algorithm and Geometric Feature Analysis

International Journal of Environmental Sciences & Natural Resources, Journal Year: 2023, Volume and Issue: 32(3)

Published: July 3, 2023

This paper adapts the deep learning pipeline algorithm based on Multi-Layer Perceptron (MLP) Neural Network to automatically classify forest Light Detection And Ranging (LiDAR) point cloud.To achieve this, Machine Learning (ML) parameters such as input layer elements, number of hidden layers, activation functions, and alpha value are optimized best possible performance.Regarding important role geometric features in layer, most suggested literature analyzed employ more effective ones layer.As a result, seven features, addition 3D coordinates cloud, chosen represent first layer.The proposed classifies LiDAR cloud into two classes: vegetation terrain.The approach was tested using points clouds, one flat area other mountain area.The results provide an accuracy score greater than 98%.The obtained result confirms high efficiency classification regarding envisaged approaches literature.Finally, next step is generalize this complicated scenes urban areas.

Language: Английский

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A new extraction method of underglaze brown decorative pattern based on the coupling of single scale gamma correction and gray sharpening

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(8), P. e0305118 - e0305118

Published: Aug. 29, 2024

In order to solve the problem of image quality and morphological characteristics primary underglaze brown decorative pattern extraction, this paper proposes a method extraction based on coupling single scale gamma correction gray sharpening. The single-scale is combined with sharpening method. improves contrast brightness by nonlinear transformation, but may lead loss detail. Gray can enhance high frequency component improve clarity image, it will introduce noise. Combining these two technologies compensate for their shortcomings. experimental results show that efficiency last element enhancing retention detail reducing influence showed F1Score, Miou(%), Recall, Precision Accuracy(%) were 0.92745, 0.82253, 0.97942, 0.92458 respectively.

Language: Английский

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Advancing Physically Informed Autoencoders for DTM Generation

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(11), P. 1841 - 1841

Published: May 22, 2024

The combination of Remote Sensing and Deep Learning (DL) has brought about a revolution in converting digital surface models (DSMs) to terrain (DTMs). DTMs are used various fields, including environmental management, where they provide crucial topographical data accurately model water flow identify flood-prone areas. However, current DL-based methods require intensive processing, limiting their efficiency real-time use. To address these challenges, we have developed an innovative method that incorporates physically informed autoencoder, embedding physical constraints refine the extraction process. Our approach utilizes normalized DSM (nDSM), which is updated by autoencoder enable DTM generation defining as difference between input nDSM. This reduces sensitivity variations, improving model’s generalizability. Furthermore, our framework innovates using subtractive skip connections instead traditional concatenative ones, network’s flexibility adapt variations significantly enhancing performance across diverse environments. novel demonstrates superior adaptability compared other versions autoencoders ten datasets, urban areas, mountainous regions, predominantly vegetation-covered landscapes,

Language: Английский

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Structural wheat trait estimation using UAV-based laser scanning data: Analysis of critical aspects and recommendations based on a case study

Precision Agriculture, Journal Year: 2024, Volume and Issue: 26(1)

Published: Dec. 27, 2024

Abstract Purpose The use of UAVs (Unmanned Aerial Vehicles) equipped with sensors such as laser scanners offers an alternative to conventional, labor-intensive manual measurements in agriculture, they enable precise and non-destructive field surveys. Methods This paper evaluates the UAV-based scanning (RIEGL miniVUX-SYS) for estimating crop height plant area index (PAI) winter wheat. (Methods) It further introduces a novel ground classification method, enhancing early growth stage through sensor attributes like intensity pulse shape deviation. Results estimation shows high $$R^2$$ R 2 score $$99.69~\%$$ 99.69 % but systematically lower estimate mean absolute error 7.4 cm . potential PAI derivation is analyzed three different strategies provides overview limitations approach. Additional weighting based on scan angle adaptation extinction coefficient present results $$97.66~\%$$ 97.66 0.25. Conclusion investigation discusses impact calculated gap fraction, which describes ratio beams penetrating canopy comparison total number measurements.

Language: Английский

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Plant Population Classification Based on PointCNN in the Daliyabuyi Oasis, China

Forests, Journal Year: 2023, Volume and Issue: 14(10), P. 1943 - 1943

Published: Sept. 24, 2023

Populus euphratica and Tamarix chinensis hold significant importance in wind prevention, sand fixation, biodiversity conservation. The precise extraction of these species can offer technical assistance for vegetation studies. This paper focuses on the located within Daliyabuyi, utilizing PointCNN as primary research method. After decorrelating stretching images, deep learning techniques were applied, successfully distinguishing between various types, thereby enhancing precision information extraction. On validation dataset, model showcased a high degree accuracy, with respective regular accuracy rates being 92.106% 91.936%. In comparison to two-dimensional models, classification is superior. Additionally, this study extracted individual tree euphratica, such height, crown width, area, volume. A comparative analysis data attested results. Furthermore, concluded that batch size block training could influence outcomes. summary, compared 2D point cloud approach exhibits higher reliability classifying extracting poplars tamarisks. These findings valuable references insights remote sensing image processing domains.

Language: Английский

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A Performance Analysis of Point CNN and Mask R-CNN for Building Extraction from Multispectral LiDAR Data

International Journal of Advanced Computer Science and Applications, Journal Year: 2023, Volume and Issue: 14(9)

Published: Jan. 1, 2023

The extraction of buildings from multispectral Light Detection and Ranging (LiDAR) data holds significance in various domains such as urban planning, disaster response, environmental monitoring. State-of-the-art deep learning models, including Point Convolutional Neural Network (Point CNN) Mask Region-based (Mask R-CNN), have effectively addressed this particular task. Data application characteristics affect model performance. This research compares LiDAR building CNN R-CNN. Models are tested for accuracy, efficiency, capacity to handle irregularly spaced point clouds using data. extracts more accurately efficiently than CNN-based cloud feature avoids preprocessing like voxelization, improving accuracy processing speed over CNNs can with variable spacing. R-CNN outperforms some cases. uses image-like instead clouds, making it better at detecting categorizing objects different angles. study emphasizes selecting the right or accurate depends on application. For data, two approaches were compared utilizing precision, recall, F1 score. point-CNN outperformed had 93.40% 92.34% 92.72% has moderate F1.

Language: Английский

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Comparative Analysis of Algorithms to Cleanse Soil Micro-Relief Point Clouds

Geomatics, Journal Year: 2023, Volume and Issue: 3(4), P. 501 - 521

Published: Nov. 26, 2023

Detecting changes in soil micro-relief farmland helps to understand degradation processes like sheet erosion. Using the high-resolution technique of terrestrial laser scanning (TLS), we generated point clouds three 2 × 3 m plots on a weekly basis from May mid-June 2022 cultivated Germany. Three well-known applications for eliminating vegetation points cloud were tested: Cloth Simulation Filter (CSF) as filtering method, variants CANUPO machine learning and ArcGIS PointCNN deep sub-category using neural networks. We assessed methods with hard criteria such F1 score, balanced accuracy, height differences, their standard deviations reference surface, resulting data gaps robustness, soft time-saving capacity, accessibility, user knowledge. All algorithms showed low performance at initial measurement epoch, increasing later epochs. While most results demonstrate better PointCNN, this algorithm revealed an exceptionally plot 1, which is describable by generalization gap. Although created highest amount gaps, recommend that include colour values combination CSF.

Language: Английский

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