Lidar data provide both geometric and radiometric information. Radiometric information is influenced by sensor and target factors and should be calibrated to obtain consistent energy responses. The radiometric correction of airborne lidar system (ALS) converts the amplitude into a backscatter cross-section with physical meaning value by applying a model-driven approach. The radiometric correction of terrestrial mobile lidar system (MLS) is a challenging task because it does not completely follow the inverse square range function at near-range. This study proposed a radiometric normalization workflow for MLS using a data-driven approach. The scope of this study is to normalize amplitude of road points for road surface classification, assuming that road points from different scanners or strips should have similar responses in overlapped areas. The normalization parameters for range effect were obtained from crossroads. The experiment showed that the amplitude difference between scanners and strips decreased after radiometric normalization and improved the accuracy of road surface classification.
We present a new approach to the theory of k-forms on self-similar fractals. We work out the details for two examples, the standard Sierpinski gasket and 3dimensional Sierpinski gasket (SG 3 ), but the method is expected to be effective for many PCF fractals, and also infinitely ramified fractals such as the Sierpinski carpet (SC). Our approach is to construct k-forms and deRham differential operators d and δ for a sequence of graphs approximating the fractal, and then pass to the limit with suitable renormalization, in imitation of Kigami's approach on constructing Laplacians on functions. One of our results is that our Laplacian on 0-forms is equal to Kigami's Laplacian on functions. We give explicit construction of harmonic 1-forms for our examples. We also prove that the measures on line segments provided by 1-forms are not absolutely continuous with respect to Lebesgue measures.
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