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- W2183426801 abstract "LiDAR surveying provides two different kinds of data: 1) elevation, the primary data, obtained by differences in time of the emitted and received laser signals; and 2) intensity, the secondary one, obtained by differences of reflected laser beam according to dissimilar materials present on the surface. Combining both data by using objectbased classification permits more efficient data mining about scanned surfaces. Using elevation and intensity together, more details on surface features can be extracted. However, the effective use of object-based classification depends on characteristics of objects/segments. Our research hypothesizes that LiDAR intensity images contain significant information about the objects sensed by the LiDAR sensor, and that segmentation can be conducted to detect semi-homogeneous objects of interest. However within the intensity data there also exist noise and signal eccentricity caused by sensor scanning patterns and a receiver’s adjusted gain response. Traditional low-pass filters used to minimize this problem cause blurred edges of objects of interest that results in inefficient segmentation processing. Anisotropic diffusion filtering provides smoothing of intra-region areas preferentially over inter-region areas, thereby providing a good prospective tool for removing unwanted noise while preserving the edges of desired objects. This research compares different segmentation parameters over three images: an original LiDAR intensity image; a customized kernel low-pass filtered image and an anisotropic diffused filtered image. Filter parameters were adjusted to produce test images resulting in the effective removal of noise and artifacts as determined through visual inspection. Considering roads and buildings as objects of interest, comparisons between objects generated by segmentation and real objects were performed." @default.
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- W2183426801 date "2007-01-01" @default.
- W2183426801 modified "2023-09-27" @default.
- W2183426801 title "A NOISE-REMOVAL APPROACH FOR LIDAR INTENSITY IMAGES USING ANISOTROPIC DIFFUSION FILTERING TO PRESERVE OBJECT SHAPE CHARACTERISTICS" @default.
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