Ms. Lian Ma Profile

Lian Ma

at Academy of Opto-Electronics CAS

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Publications (2)

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Proceedings Article | 9 September 2019 Presentation + Paper

An elevation correction method for colored point cloud in building areas

Mei Zhou, Hongcan Guan, Geer Teng, Chuanrong Li, Hui Jing Zhang, Jiuying Chen, Lian Ma, Wei Li

Proceedings Volume 11127, 1112712 (2019) https://doi.org/10.1117/12.2525363

KEYWORDS: Clouds, Image fusion, 3D image processing, LIDAR, Feature extraction, Charge-coupled devices, Sensors, Image processing, Image sensors, Image classification

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Generating colored point cloud by the fusion of CCD images and point cloud data can exert both of their superiorities sufficiently, which has been a major method to obtain spatial information of the buildings for building reconstruction, object detection and other applications. Airborne LiDAR and CCD cameras are usually combined on one platform to carry out colored point cloud based on registration. In addition, there is also a new 3D imaging sensor that can acquire point cloud and CCD images with a stable relationship by the mechanism of common optical system, which could generate colored point cloud faster than the former. In the process of fusion, the colored point cloud is possible to absence some building information such as corners and boundaries. Interpolation is an optimistic method to solve the above issue. However, due to the unclear boundaries between building and ground in the point cloud data, the elevation error of the building area is large after interpolation. Therefore, a correction method for the elevation of colored point cloud in building area is proposed in this paper by combining point cloud contour extraction, image region merging and contour regularization. The new method can accurately obtain the edge of the building by the using of stable relationship, thus reducing the elevation interpolation error of the colored point cloud. The effectiveness of the method is validated based on the flight test data of 3D imaging sensor. The accuracy is improved by 33% after elevation correction.

Proceedings Article | 15 October 2015 Paper

The research of land covers classification based on waveform features correction of full-waveform LiDAR

Mei Zhou, Menghua Liu, Zheng Zhang, Lian Ma, Huijing Zhang

Proceedings Volume 9643, 96431P (2015) https://doi.org/10.1117/12.2193867

KEYWORDS: Backscatter, LIDAR, Feature extraction, Buildings, Image classification, Expectation maximization algorithms, Roads, CCD image sensors, Data modeling, Charge-coupled devices

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In order to solve the problem of insufficient classification types and low classification accuracy using traditional discrete LiDAR, in this paper, the waveform features of Full-waveform LiDAR were analyzed and corrected to be used for land covers classification. Firstly, the waveforms were processed, including waveform preprocessing, waveform decomposition and features extraction. The extracted features were distance, amplitude, waveform width and the backscattering cross-section. In order to decrease the differences of features of the same land cover type and further improve the effectiveness of the features for land covers classification, this paper has made comprehensive correction on the extracted features. The features of waveforms obtained in Zhangye were extracted and corrected. It showed that the variance of corrected features can be reduced by about 20% compared to original features. Then classification ability of corrected features was clearly analyzed using the measured waveform data with different characteristics. To further verify whether the corrected features can improve the classification accuracy, this paper has respectively classified typical land covers based on original features and corrected features. Since the features have independently Gaussian distribution, the Gaussian mixture density model (GMDM) was put forward to be the classification model to classify the targets as road, trees, buildings and farmland in this paper. The classification results of these four land cover types were obtained according to the ground truth information gotten from CCD image data of the targets region. It showed that the classification accuracy can be improved by about 8% when the corrected features were used.

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