Research On Classiifcation Technologies Of Land Cover By Fusing Airborne LiDAR Point Clouds And Remote Sensing Imagery

Airborne Light Detection and Ranging(LiDAR) is widely applied in the domain ofGeo-spatial information sciences as an emerging means which is a quick access to spatial data.Compared with remote sensing imagery data in tradition, the3D point clouds data acquired byLiDAR technology have a lot of flaws, such as the scarcity of semantic information,discontinuous characteristics and so on. Thus, it is more difficult in intelligent classification andrecognition of land cover types. Meanwhile, though very high resolution imagery can provide alarge amount of spectral features and texture information, the phenomenon of spectralheterogeneity even within the same class and different objects becoming more spectrally similarmake the reliability of automatic target interpretation in imagery decrease a lot. Consequently,according to the specific advantages and disadvantages of different sensors, integrating pointclouds and imagery to complement single data resource to perform intelligent classification andidentification of land cover types has important research values.Based on the theories of multi-source data fusion and classifier of Support VectorMachine(SVM) and decision tree, this thesis aims to investigate an intensive and deep study onthe theory and technology of classification of land cover by fusing airborne LiDAR point cloudsand high spatial resolution multi-spectral remote sensing imagery. Subsequently, profoundsignificance is revealed through experiment and feature space analysis. The main contents andinnovations in this thesis can be summarized as follows:1. The subjects of registration and filtering involved in classification of fusing airborneLiDAR and remote sensing imagery are researched from a technical level. Especially a filteringalgorithm based on skewness balancing for point clouds is achieved by exploiting statisticalmoments. The main advantages of the algorithm are threshold-freedom and independence fromLiDAR data format and resolution, and it separates ground points and non-ground pointsautomatically by unsupervised classification method. Compared with conventional algorithms, inspite of its drawback of the typeⅡerror control, it has great values of practical application due toits high efficiency and intelligence.2. In connection with the shortcomings of "few features, low accuracy" which is existed inthe classification of fusing point clouds and imagery, the emphasis is placed on the optimizedextraction strategy for point clouds. According to the different principles of extraction, thefeatures for point clouds are divided into direct and indirect features. The way of adding localgeometric properties of raw point clouds to feature vector to improve the classification accuracyand stability is proposed, and five kinds of features belonging to normal vectors are extracted.The analysis of feature images indicates that the normal vector can not only distinguish roads, buildings with different shapes and vegetation, but also have a strong sensitivity in recognition ofvehicles and vegetation.3. SVM is introduced to the classification of fusing point clouds and imagery, two types ofmulti-features classification modes which are point clouds complemented by imagery andimagery complemented by point clouds are developed. The classification results show that ourmethods exhibit great adaptability to various types of data, and both of the modes achieve highaccuracy. In particular, the overall classification accuracy and Kappa of point clouds reach above90%, and the vehicles are successfully identified as a special kind of object. The contrastexperiments prove that the methods developed in this thesis evidently outperforms the traditionalclassification methods, no matter on the accuracy or on the quantity of land cover types.4. Combined methods of pros and cons are conducted to analyze deeply the feature spacesassociated with the two classification modes of the point clouds complemented by imagery andthe imagery complemented by point clouds, the conclusion are considered as follows: Firstly, inthe mode of point clouds complemented by imagery, normalized height(NH) of point clouds hasthe greatest impact on the classification accuracy; Secondly, after adding the local geometricproperties of point clouds, the misclassification probability between buildings and trees reducesconsiderably, and the problems in extracting building edge points are tackled; Thirdly,comparative analysis of the two classification modes indicate that point clouds contribute a lot tothe imagery classification, which is far superior to the mode of point clouds complemented byimagery, so the classification mode of imagery complemented by point clouds presents furtherpractical significance; Fourthly, combined classification technologies of land cover, whosecomplementary data are airborne LiDAR point clouds, will have important strategic status in thefuture.5. A nonlinear Mode filter is designed and improved to enhance the classification resultquality of point clouds and imagery. According to the different characteristics of point clouds andimagery, k-nearest neighbor and window-based Mode filters are respectively developed toremove speckle and salt and pepper noises. Contrast experimental results demonstrate that theimproved Mode filters can boost the classification accuracy of point clouds and imagery dataeffectively.6. The method of refined classification in image with the support of point clouds elevationdata is proposed and carried out. In order to create high accuracy of subdividing the same kind ofland cover type, four factors are taken into consideration, which includes supplementary datasource, Mode filter, point clouds density and image spatial resolution and point clouds firstechoes. Decision tree is developed to improve remarkably the classification quantity of buildingsand vegetation, which represents further superiority of classification of fusing point clouds and imagery, and achieves the desired goal of the unity of classification accuracy and quantity.7. The method of classification of tree species in point clouds data complemented bymulti-spectral imagery is proposed and carried out. In order to avoid the low accuracy caused byinadequate features, besides the direct features, such as normalized height, intensity, and so on,this thesis focuses on extracting the indirect features from point clouds which can depict thecanopy information of tree species, including the echoes of canopy, geometric properties ofcanopy, vertical structure of canopy. The experimental results show that, by synergistic use ofairborne LiDAR point clouds and multi-spectral imagery, the method based on SVM classifierobtains higher classification accuracy and more accurate categories of tree species.