Classification of LiDAR Data Combined Octave Convolution With Capsule Network

IEEE Access, vol. 8, pp. 16155-16165 2020 H. WuM. CaoA. WangM. Wang

Light Detection and Ranging (LiDAR) data are widely used for high-resolution land cover mapping, which can provide very valuable information about the height of the surveyed area for the discrim- ination of classes. In order to utilize the advantages of deep models for the classification of LiDAR-derived features, a new classification algorithm combined Octave Convolution (OctConv) with Capsule Network (CapsNet), is proposed here to hierarchically extract robust and discriminant features of the input data, called as OctConv-CapsNet... (read more)

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