Journal
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
Volume 101, Issue -, Pages 221-232Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.isprsjprs.2014.12.013
Keywords
Fusion; LiDAR; Imagery; Land cover; Classification; High resolution; Multispectral
Categories
Funding
- King Saud University
Ask authors/readers for more resources
High spatial resolution (HSR) imagery and high density LiDAR data provide complementary horizontal and vertical information. Therefore, many studies have focused on fusing the two for mapping geographic features. It has been demonstrated that the synergetic use of LiDAR and HSR imagery greatly improves classification accuracy. This is especially true with waveform LiDAR data since they provide more detailed vertical profiles of geographic objects than discrete-return LiDAR data. Fusion of discrete-return LiDAR and HSR imagery mostly takes place at the object level due to the superiority of object-based image analysis (OBIA) for classifying HSR imagery. However, the fusion of the waveform LiDAR and HSR imagery at the object level has not been adequately studied. To fuse LiDAR waveform and image objects, the waveform for the objects derived from image segmentation are needed. However, the footprints of existing waveform are usually of fixed size and fixed shape, while those of building are of different size and shape. In order to obtain waveforms with footprints that match those of image objects, we proposed synthesizing object-based pseudo-waveforms using discrete-returns LiDAR data by utilizing count or intensity based histogram over the footprints of the objects. The pseudo-waveforms were then fused with the object-level spectral histograms from HSR WorldView-2 imagery to classify the image objects using a Kullback-Leibler divergence-based curve matching approach. The fused dataset achieved an overall classification accuracy of 97.58%, a kappa coefficient of 0.97, and producer's accuracies and user's accuracies all larger than 90%. The use of the fused dataset improved the overall accuracy by 7.61% over the use of HSR imagery alone, and McNemar's test indicated that such improvement was statistically significant (p < 0.001). This study demonstrates the great potential of pseudo-waveform in improving object-based image analysis. This is especially true since currently the majority of commercial LiDAR data are of discrete return while waveform data are still not widely available. (C) 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available