Land Cover Classification From Full-Waveform Lidar Data Based on Support Vector Machines

Zhou, Mei; Li, C. R.; Ma, Lingling; Guan, Hongtao

doi:10.5194/isprs-archives-xli-b3-447-2016

Cited by 12 publications

(8 citation statements)

References 16 publications

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“…In this article, the RF classifier is demonstrated under the complex environments. We also compared its performance with the other classifiers, for example, linear-SVM and radial basis function SVM (RBF-SVM) [53], LR [54], KNN [55], and NB [56]. And the quantitative analysis is also evaluated.…”

Section: B Classification Based On Waveform Featuresmentioning

confidence: 99%

Classification of Laser Footprint Based on Random Forest in Mountainous Area Using GLAS Full-Waveform Features

Liu

Wang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Full-waveform spaceborne laser altimeter can provide more characteristic parameters of the laser footprint and rich vertical structure information on the target surface. This technology has the potential for land-cover classification, especially in hard-to-reach mountain areas. Classifying the land types based on the returned waveform can provide a convenient way for the online classification needs and assess the quality of footprint used as the ground control point in photogrammetry. This article presents a random forest (RF) classification using geoscience laser altimeter system waveform, in the west-central Yunnan Province, China. First, an improved threshold wavelet is performed to denoise the waveform, and then Gaussian decomposition is used to extract the typical characteristic features of footprint. Second, an RF algorithm is implemented to clarify the footprints into five types: flat, building, terrace, forest, and mountain. Finally, quantitative analysis is conducted with producer's accuracy (PA), user's accuracy (UA), overall accuracy (OA), precision, recall rate, F1-score, and kappa coefficient to compare the performance of RF with other classifiers, including linear support vector machine (linear-SVM), radial basis function SVM (RBF-SVM), logistic regression (LR), K-nearest neighbor (KNN), and naive Bayes (NB). The results show that all the six methods can accurately classify the flat land with 100.00% PA and UA. The RF also has the best performances in other four land types, with PA and UA of 98.14% and 100.00%, 97.24% and 95.49%, 98.64% and 96.03%, and 94.64% and 100.00%, respectively. The OA, precision, recall, F1-score, and kappa coefficient for the RF are 97.95%,

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Section: B Classification Based On Waveform Featuresmentioning

confidence: 99%

Classification of Laser Footprint Based on Random Forest in Mountainous Area Using GLAS Full-Waveform Features

Liu

Wang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Close distances between multiple targets will cause echo overlap so deconvolution and multi-Gaussian fitting need to be used before feature extraction [12,13]. After the decomposition of echoes into multiple Gaussian components, the manual features combined with the support vector machine (SVM) [6] and random forest (RF) [14,15] methods can be used for classification. However, the accuracy is mainly subject to the quality of the manual features, which requires specialized knowledge.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, a combination of artificial features and machine learning can be used for land cover classification [2] and ocean engineering [3][4][5]. However, such algorithms are extremely dependent on a large number of manual features and annotation data [6] so the universality of those algorithms is limited.…”

Section: Introductionmentioning

confidence: 99%

A Classification Method for Airborne Full-Waveform LiDAR Systems Based on a Gramian Angular Field and Convolution Neural Networks

Zhao

et al. 2022

Electronics

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The data processing of airborne full-waveform light detection and ranging (LiDAR) systems has become a research hotspot in the LiDAR field in recent years. However, the accuracy and reliability of full-waveform classification remain a challenge. The manual features and deep learning techniques in the existing methods cannot fully utilize the temporal features and spatial information in the full waveform. On the premise of preserving temporal dependencies, we convert them into Gramian angular summation field (GASF) images using the polar coordinate method. By introducing spatial attention modules into the neural network, we emphasize the importance of the location of texture information in GASF images. Finally, we use open source and simulated data to evaluate the impact of using different network architectures and transformation methods. Compared with the performance of the state-of-art method, our proposed method can achieve higher precision and F1 scores. The results suggest that transforming the full waveform into GASF images and introducing a spatial attention module outperformed other classification methods.

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“…For example, a rule-based algorithm was used for classification tasks [2,36]. Other methods are based on classic machine learning that uses handcrafted features, such as nonlinear classification and support vector machine (SVM) classifiers [24], which have been widely used with point cloud classification tasks using handcrafted features from full-waveform LiDAR data [6,7,14,23,54]. Furthermore, for land-use classification tasks, Wang et al [37] demonstrated the importance of spatial distributional and handcrafted features of waveforms.…”

Section: Full-waveform Lidar Data Analysismentioning

confidence: 99%

Semantic Segmentation for Full-Waveform LiDAR Data Using Local and Hierarchical Global Feature Extraction

Shinohara

Xiu

Matsuoka

2020

Proceedings of the 28th International Conference on Advances in Geographic Information Systems

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During the last few years, in the field of computer vision, sophisticated deep learning methods have been developed to accomplish semantic segmentation tasks of 3D point cloud data. Additionally, many researchers have extended the applicability of these methods, such as PointNet or PointNet++, beyond semantic segmentation tasks of indoor scene data to large-scale outdoor scene data observed using airborne laser scanning systems equipped with light detection and ranging (LiDAR) technology. Most extant studies have only investigated geometric information (x, y, and z or longitude, latitude, and height) and have omitted rich radiometric information. Therefore, we aim to extend the applicability of deep learning-based model from the geometric data into radiometric data acquired with airborne full-waveform LiDAR without converting the waveform into 2D images or 3D voxels. We simultaneously train two models: a local module for local feature extraction and a global module for acquiring wide receptive fields for the waveform. Furthermore, our proposed model is based on waveform-aware convolutional techniques. We evaluate the effectiveness of the proposed method using benchmark large-scale outdoor scene data. By integrating the two outputs from the local module and the global module, our proposed model had achieved higher mean recall value 0.92 than previous methods and higher F1 scores for all six classes than the other 3D Deep Learning method. Therefore, our proposed network consisting of the local and global module successfully resolves the semantic segmentation task of full-waveform LiDAR data without requiring expert knowledge. CCS CONCEPTS • Computing methodologies → Scene understanding.

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Land Cover Classification From Full-Waveform Lidar Data Based on Support Vector Machines

Cited by 12 publications

References 16 publications

Classification of Laser Footprint Based on Random Forest in Mountainous Area Using GLAS Full-Waveform Features

Classification of Laser Footprint Based on Random Forest in Mountainous Area Using GLAS Full-Waveform Features

A Classification Method for Airborne Full-Waveform LiDAR Systems Based on a Gramian Angular Field and Convolution Neural Networks

Semantic Segmentation for Full-Waveform LiDAR Data Using Local and Hierarchical Global Feature Extraction

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