Land Cover Segmentation of Airborne LiDAR Data Using Stochastic Atrous Network
AbstractInspired by the success of deep learning techniques in dense-label prediction and the increasing availability of high precision airborne light detection and ranging (LiDAR) data, we present a research process that compares a collection of well-proven semantic segmentation architectures based on the deep learning approach. Our investigation concludes with the proposition of some novel deep learning architectures for generating detailed land resource maps by employing a semantic segmentation approach. The contribution of our work is threefold. (1) First, we implement the multiclass version of the intersection-over-union (IoU) loss function that contributes to handling highly imbalanced datasets and preventing overfitting. (2) Thereafter, we propose a novel deep learning architecture integrating the deep atrous network architecture with the stochastic depth approach for speeding up the learning process, and impose a regularization effect. (3) Finally, we introduce an early fusion deep layer that combines image-based and LiDAR-derived features. In a benchmark study carried out using the Follo 2014 LiDAR data and the NIBIO AR5 land resources dataset, we compare our proposals to other deep learning architectures. A quantitative comparison shows that our best proposal provides more than 5% relative improvement in terms of mean intersection-over-union over the atrous network, providing a basis for a more frequent and improved use of LiDAR data for automatic land cover segmentation. View Full-Text
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Arief, H.A.; Strand, G.-H.; Tveite, H.; Indahl, U.G. Land Cover Segmentation of Airborne LiDAR Data Using Stochastic Atrous Network. Remote Sens. 2018, 10, 973.
Arief HA, Strand G-H, Tveite H, Indahl UG. Land Cover Segmentation of Airborne LiDAR Data Using Stochastic Atrous Network. Remote Sensing. 2018; 10(6):973.Chicago/Turabian Style
Arief, Hasan A.; Strand, Geir-Harald; Tveite, Håvard; Indahl, Ulf G. 2018. "Land Cover Segmentation of Airborne LiDAR Data Using Stochastic Atrous Network." Remote Sens. 10, no. 6: 973.
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