Support Vector Machines Based Filtering of Lidar Data: A Grid Based Method

SUMMARY This study introduces a method for filtering lidar data based on a Support Vector Machines (SVMs) classification method. Four study areas with different sensors and scene characteristics were investigated. First, the Digital Surface Model (DSM) was generated for the first and last pulses and then the differences between the first and last pulses (FP-LP) were computed. A total of 25 uncorrelated feature attributes have been generated from the aerial images, the lidar intensity image, DSM and FP-LP. The generated attributes were applied in seven separate groups which include those from: Red, Green and Blue bands of the aerial image; Intensity/IR image; DSM; FP-LP and the Total group of attributes. Finally, SVMs were used to automatically classify buildings, trees, roads and ground from aerial images, lidar data and the generated attributes, with the most accurate average classifications of 95% being achieved. The Gaussian Radius Basis Function (RBF) kernel model was applied to find the separating hyperplane for the SVMs classification. A binary image was then generated by converting the digital numbers of roads and grass to one while the digital numbers of buildings and trees were converted to zeros and all DSM’s pixels which correspond to a pixel value of one in the binary image were interpolated into a grid DTM. The interpolated DTM was then smoothed by a low-pass filter to remove low vegetation and other objects which might be classified as ground. After that the original 3D lidar point clouds was compared against the smoothed DTM and labeled as ground or non-ground based on a predefined threshold of 30 cm. To meet the objectives, the filtered data was compared against reference data that was generated manually and both omission and commission errors were calculated. Further, we evaluated the contributions of each group of attributes to the quality of the filtering process. The results showed that the accuracy of the results was improved by fusing lidar data with multispectral images regardless of the complexity of the terrain being filtered.

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