As a new data source of remote sensing, airborne LiDAR data quality evaluation is of great importance. This paper
focused on the comprehensive quality evaluation of airborne LiDAR data in the following aspects: data integrity, data
accuracy , data density, interpretation ability of intensity and the quality of spatial data products from LiDAR data,
which aimed to provide a complete reference to airborne LiDAR data quality for practical applications. For data
integrity, a data void extraction method based on region growing was proposed to check the completeness of spatial
distribution. Data accuracy is a key quality index for LiDAR data quality. Height offset statistics among overlapping
strips were calculated to evaluate the relative accuracy. Data density is an important factor influencing the products
quality from LiDAR data. The average point spacing can only demonstrate the whole density of the data, whereas the
local density is much more important for specific applications. In this paper, the density distribution map is used to
reflect the density variations for the whole data. Besides, interpretation ability of intensity is also used to evaluate the
quality of the airborne LiDAR data. Quality of DTM was used to evaluate the LiDAR data at last.
The error sources of airborne laser measurement device which mainly include laser beam misalignment with respect to
scanning mirror, clock error, scanner error and scanner torsion are discussed, and their effects to airborne LIDAR(Light
Detection and Ranging)position accuracy are analyzed. Specially, laser beam misalignment's influences to LIDAR
scanning line distortion and positioning accuracy are analyzed in detail quantitatively and qualitatively for oscillating
scanner. The analysis demonstrates that laser beam misalignment influences the scanning line distortion and positioning
accuracy more and more with the increasing height and scanning angle and can't be eliminated by common calibration
methods but by the calibration method in factory for it is related to other error sources.
Considering the deficiency of mapping model in traditional image registration, a new image registration method based on evolutionary modeling is proposed in this paper. Multi Expression Programming has been used as modeling tool to build mapping model. To avoid over fitting and improve actual effective, constraints of the mapping function's slope and curvature were added during modeling process. SAR image and optical image rectifying experiment is given in the last. The experiment result indicated that the evolutionary model has high precision for image registration. This method is fit for image registration.
Vehicle license plate (VLP) recognition is of great importance to many traffic applications. Though researchers have paid much attention to VLP recognition there has not been a fully operational VLP recognition system yet for many reasons. This paper discusses a valid and practical method for vehicle license plate recognition based on geometry restraints and multi-feature decision including statistical and structural features. In general, the VLP recognition includes the following steps: the location of VLP, character segmentation, and character recognition. This paper discusses the three steps in detail. The characters of VLP are always declining caused by many factors, which makes it more difficult to recognize the characters of VLP, therefore geometry restraints such as the general ratio of length and width, the adjacent edges being perpendicular are used for incline correction. Image Moment has been proved to be invariant to translation, rotation and scaling therefore image moment is used as one feature for character recognition. Stroke is the basic element for writing and hence taking it as a feature is helpful to character recognition. Finally we take the image moment, the strokes and the numbers of each stroke for each character image and some other structural features and statistical features as the multi-feature to match each character image with sample character images so that each character image can be recognized by BP neural net. The proposed method combines statistical and structural features for VLP recognition, and the result shows its validity and efficiency.
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