Data Fusion Of Aerial LiDAR Data And Hyperspectral Imagery For Urban Classification

In recent years,with the accelerating process of urbanization,geospatial information is increasingly demanded for urban planning and development.With the continuous advances of platforms and sensors,remote sensing data is developing towards multi-sources and multi-direction,which launched a new era of earth observation.Optical imageries are one of the main data sources of urban observation,which can be convenient and intuitive to show spatial pattern distribution.In recent years,optical imageries have developed towards the direction of hyperspectral and high spatial resolution,which can obtain richer spectral information and spatial features.However,this phenomena does not necessarily improve the accuracy of image interpretation.Due to the presence of spectral heterogeneity,there is a certain bottleneck in the accuracy of hyperspectral image classification.As the high-spatial-resolution,although providing many details of the object,but also makes the variance within the class increases the variance between the class decreases,and eventually lead to the decrease of spectral separability.Therefore,the use of a single data source is not enough to complete the urban classification with high-precision.LiDAR is a new type of earth observation technology.It can quickly and accurately obtain the elevation information which supplements the information of 2D optical image.Therefore,LiDAR is widely promoted in urban remote sensing task.However,the data format of LiDAR is a discrete point cloud,which cannot be directly fused with image features.It is one of the hottest research and most important application that fuse the complementary information of multi-source data.Multi-source data fusion includes three levels:pixel level,feature level and decision level.The pixel level fusion is a simple stack of multiple features,and employ the support vector machine or random forest classifier;feature level considers the effective combination of different features to achieve feature recognition and classification;decision level fusion is a comprehensive combination of features and classifiers,which can significantly improve the reliability and accuracy of classification results.The purpose of this paper is to fuse airborne LiDAR point cloud data and hyperspectral imagery to improve the accuracy of multi-source data classification.In this paper,the information of elevational,spectral and spatial features is extracted from LiDAR data and hyperspectral image.In addition,the method of spatial feature extraction is proposed,which combines the pseudo-waveform of LiDAR and hyperspectral data.The pseudo-waveform can be used to enhance the spectral separability of hyperspectral data,and then the spectral angle distance(SAD)is utilized for the pixel shape index(PSI),which can fully extract the information of fused data,and obtain a more reliable PSI feature.On the basis of feature extraction,we use the multi-source features for pixel-level fusion classification with support vector machine(SVM)and random forest(RF)classifier.Then,the elevation information is employed to assist the spectral and spatial data and carry on the multi-scale segmentation,resulting with image object.Finally,object-oriented decision fusion is carried out with pixel-level classification results.In this experiment,we use a set of LiDAR data and hyperspectral remote sensing image in the same region to verify the framework we proposed.The experimental results show that the SAD-PSI feature with pseudo-waveform and hyperspectral images is better than the original PSI feature in the classification.At the same time,the SVM and RF classification accuracy of pixel level fusion with elevation information is increased about 10%and 12%respectively,compared with the original spectral classification.In the object-oriented decision fusion classification experiment,the elevation-assisted segmentation method achieves better classification effect compared with the spectral-based segmentation method.And object-oriented decision fusion classification achieves an overall accuracy of 94.05%,with 6%and 4%higher than to the pixel level classification with SVM and RF,respectively.