Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 19, Issue -, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2021.3071252
Keywords
Laser radar; Feature extraction; Remote sensing; Sensors; Task analysis; Labeling; Image sensors; Capsule feature attention; capsule network; land cover classification; land use mapping; multispectral light detection and ranging (LiDAR)
Categories
Funding
- National Natural Science Foundation of China [62076107, 51975239, 41971414]
- Six Talent Peaks Project in Jiangsu Province [XYDXX-098]
- National Key Research and Development Program of China [2018YFB1004904]
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In this paper, an efficient self-attention capsule network (ESA-CapsNet) is proposed for land cover classification of multispectral LiDAR data, achieving promising performance in extracting high-level feature semantics and enhancing feature saliency and robustness. The ESA-CapsNet demonstrates advantageous overall accuracy, average accuracy, and kappa coefficient, and comparative experiments validate its effectiveness and applicability in land cover classification tasks.
Periodically conducting land cover mapping plays a vital role in monitoring the status and changes of the land use. The up-to-date and accurate land use database serves importantly for a wide range of applications. This letter constructs an efficient self-attention capsule network (ESA-CapsNet) for land cover classification of multispectral light detection and ranging (LiDAR) data. First, formulated with a novel capsule encoder-decoder architecture, the ESA-CapsNet performs promisingly in extracting high-level, informative, and strong feature semantics for pixel-wise land cover classification by using the five types of rasterized feature images. Furthermore, designed with a novel capsule-based attention module, the channel and spatial feature encodings are comprehensively exploited to boost the feature saliency and robustness. The ESA-CapsNet is evaluated on two multispectral LiDAR data sets and achieves an advantageous performance with the overall accuracy, average accuracy, and kappa coefficient of over 98.42%, 95.15%, and 0.9776, respectively. Comparative experiments with the existing methods also demonstrate the effectiveness and applicability of the ESA-CapsNet in land cover classification tasks.
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