Extraction of Urban Objects in Cloud Shadows on the basis of Fusion of Airborne LiDAR and Hyperspectral Data

Man, Qixia; Dong, Pinliang

doi:10.3390/rs11060713

Cited by 5 publications

(3 citation statements)

References 72 publications

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“…It can be concluded that the classification method can obtain excellent results for both random forest classifier and the object-based classifier. Man et al (2019) extracted urban objects in shadow areas with hyperspectral and LiDAR data, and demonstrated the advantage of LiDAR data in improving the classification accuracy of urban objects in shadow areas, including trees and buildings [70]. The study has also shown that the combined use of airborne LiDAR and hyperspectral data could improve the classification accuracy of trees in shadow areas.…”

Section: Performance Of Different Classification Methods For Trees and Grasses Extractionmentioning

confidence: 99%

Automatic Extraction of Grasses and Individual Trees in Urban Areas Based on Airborne Hyperspectral and LiDAR Data

et al. 2020

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Urban vegetation extraction is very important for urban biodiversity assessment and protection. However, due to the diversity of vegetation types and vertical structure, it is still challenging to extract vertical information of urban vegetation accurately with single remotely sensed data. Airborne light detection and ranging (LiDAR) can provide elevation information with high-precision, whereas hyperspectral data can provide abundant spectral information on ground objects. The complementary advantages of LiDAR and hyperspectral data could extract urban vegetation much more accurately. Therefore, a three-dimensional (3D) vegetation extraction workflow is proposed to extract urban grasses and trees at individual tree level in urban areas using airborne LiDAR and hyperspectral data. The specific steps are as follows: (1) airborne hyperspectral and LiDAR data were processed to extract spectral and elevation parameters, (2) random forest classification method and object-based classification method were used to extract the two-dimensional distribution map of urban vegetation, (3) individual tree segmentation was conducted on a canopy height model (CHM) and point cloud data separately to obtain three-dimensional characteristics of urban trees, and (4) the spatial distribution of urban vegetation and the individual tree delineation were assessed by validation samples and manual delineation results. The results showed that (1) both the random forest classification method and object-based classification method could extract urban vegetation accurately, with accuracies above 99%; (2) the watershed segmentation method based on the CHM could extract individual trees correctly, except for the small trees and the large tree groups; and (3) the individual tree segmentation based on point cloud data could delineate individual trees in three-dimensional space, which is much better than CHM segmentation as it can preserve the understory trees. All the results suggest that two- and three-dimensional urban vegetation extraction could play a significant role in spatial layout optimization and scientific management of urban vegetation.

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Section: Performance Of Different Classification Methods For Trees and Grasses Extractionmentioning

confidence: 99%

Automatic Extraction of Grasses and Individual Trees in Urban Areas Based on Airborne Hyperspectral and LiDAR Data

et al. 2020

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“…According to the difference of micro-Doppler effects [45] of rotating parts due to the difference in structure and rotating speed, features of the amplitude deviation coefficient, time domain waveform entropy and frequency domain waveform entropy are extracted to classify targets. Finally, features extracted from multi-wave gates sparse echo data are weighted and fused to train and test the support vector machine (SVM) [46][47][48] model for classification. Experimental results show that the proposed algorithm can improve the classification probability, and four wave gates echo data in weighted features fusion used to extract features is the optimal wave gate number for target classification.…”

Section: Introductionmentioning

confidence: 99%

Aircraft Target Classification for Conventional Narrow-Band Radar with Multi-Wave Gates Sparse Echo Data

Wang¹,

Tang²,

Chen³

et al. 2019

Remote Sensing

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For a conventional narrow-band radar system, the detectable information of the target is limited, and it is difficult for the radar to accurately identify the target type. In particular, the classification probability will further decrease when part of the echo data is missed. By extracting the target features in time and frequency domains from multi-wave gates sparse echo data, this paper presents a classification algorithm in conventional narrow-band radar to identify three different types of aircraft target, i.e., helicopter, propeller and jet. Firstly, the classical sparse reconstruction algorithm is utilized to reconstruct the target frequency spectrum with single-wave gate sparse echo data. Then, the micro-Doppler effect caused by rotating parts of different targets is analyzed, and the micro-Doppler based features, such as amplitude deviation coefficient, time domain waveform entropy and frequency domain waveform entropy, are extracted from reconstructed echo data to identify targets. Thirdly, the target features extracted from multi-wave gates reconstructed echo data are weighted and fused to improve the accuracy of classification. Finally, the fused feature vectors are fed into a support vector machine (SVM) model for classification. By contrast with the conventional algorithm of aircraft target classification, the proposed algorithm can effectively process sparse echo data and achieve higher classification probability via weighted features fusion of multi-wave gates echo data. The experiments on synthetic data are carried out to validate the effectiveness of the proposed algorithm.

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“…Finally, the echo received by the two receivers was used for fusion detection to improve the detection performance of the target. The dual platform cooperative detection system constructed in this paper has the advantages of a bistatic radar system and single radar system [25][26][27]. The detection range was extended and the detection performance improved, which lays a foundation for future research of the airborne radar cooperative detection system.…”

Section: Introductionmentioning

confidence: 99%

Signal Processing and Target Fusion Detection via Dual Platform Radar Cooperative Illumination

Wang¹,

Tang²,

Zhao

et al. 2019

Sensors

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A modified signal processing and target fusion detection method based on the dual platform cooperative detection model is proposed in this paper. In this model, a single transmitter and dual receiver radar system is adopted, which can form a single radar and bistatic radar system, respectively. Clutter suppression is achieved by an adaptive moving target indicator (AMTI). By combining the AMTI technology and the traditional radar signal processing technology (i.e., pulse compression and coherent accumulation processing), the SNR is improved, and false targets generated by direct wave are suppressed. The decision matrix is obtained by cell averaging constant false alarm (CA-CFAR) and order statistics constant false alarm (OS-CFAR) processing. Then, the echo signals processed in the two receivers are fused by the AND-like fusion rule and OR-like fusion rule, and the detection probability after fusion detection in different cases is analyzed. Finally, the performance of the proposed method is quantitatively analyzed. Experimental results based on simulated data demonstrate that: (1) The bistatic radar system with a split transceiver has a larger detection distance than the single radar system, but the influence of clutter is greater; (2) the direct wave can be eliminated effectively, and no false target can be formed after suppression; (3) the detection probability of the bistatic radar system with split transceivers is higher than that of the single radar system; and (4) the detection probability of signal fusion detection based on two receivers is higher than that of the bistatic radar system and single radar system.

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Extraction of Urban Objects in Cloud Shadows on the basis of Fusion of Airborne LiDAR and Hyperspectral Data

Cited by 5 publications

References 72 publications

Automatic Extraction of Grasses and Individual Trees in Urban Areas Based on Airborne Hyperspectral and LiDAR Data

Automatic Extraction of Grasses and Individual Trees in Urban Areas Based on Airborne Hyperspectral and LiDAR Data

Aircraft Target Classification for Conventional Narrow-Band Radar with Multi-Wave Gates Sparse Echo Data

Signal Processing and Target Fusion Detection via Dual Platform Radar Cooperative Illumination

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