Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 15, Issue 5, Pages 774-778Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2018.2810276
Keywords
Convolutional neural network (CNN); deep learning; feature extraction (FE); light detection and ranging (LiDAR); morphological profile (MP); multiattribute profile (MAP); sigmoid-weighted linear units (SiLUs)
Categories
Funding
- Natural Science Foundation of China [61771171]
Ask authors/readers for more resources
In recent years, deep learning-based methods, especially convolutional neural networks (CNNs), have shown their capabilities in remote sensing data processing. The efficacy of light detection and ranging (LiDAR) has been already proven in a wide variety of research areas. Most of the existing methods do not extract the informative features from LiDAR-derived rasterized digital surface models (LiDAR-DSM) data in a deep manner. In order to utilize the advantages of deep models for the classification of LiDAR-derived features, deep CNN is proposed here to hierarchically extract the robust and discriminant features of the input data. Moreover, morphological profiles and multiattribute profiles (MAPs) are investigated to enrich the inputs of the CNN and further to improve the ultimate classification performance. Furthermore, a new activation function, sigmoid-weighted linear units (SiLUs), is introduced. The proposed frameworks are tested on two LiDAR-DSMs (i.e., Bayview Park and Houston data sets). The MAP-CNNs with SiLU outperform original CNNs by 6.62% and 6.88% in terms of overall accuracy on Bayview Park and Houston data sets, respectively, when the number of training samples of each class is 40.
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