LiteDenseNet: A Lightweight Network for Hyperspectral Image
Classification
- URL: http://arxiv.org/abs/2004.08112v2
- Date: Sun, 26 Apr 2020 14:15:17 GMT
- Title: LiteDenseNet: A Lightweight Network for Hyperspectral Image
Classification
- Authors: Rui Li and Chenxi Duan
- Abstract summary: We propose a lightweight network architecture (LiteDenseNet) based on DenseNet for Hyperspectral Image Classification.
Inspired by GoogLeNet and PeleeNet, we design a 3D two-way dense layer to capture the local and global features of the input.
As convolution is a computationally intensive operation, we introduce group convolution to decrease calculation cost and parameter size further.
- Score: 2.696926374562295
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Hyperspectral Image (HSI) classification based on deep learning has been an
attractive area in recent years. However, as a kind of data-driven algorithm,
deep learning method usually requires numerous computational resources and
high-quality labelled dataset, while the cost of high-performance computing and
data annotation is expensive. In this paper, to reduce dependence on massive
calculation and labelled samples, we propose a lightweight network architecture
(LiteDenseNet) based on DenseNet for Hyperspectral Image Classification.
Inspired by GoogLeNet and PeleeNet, we design a 3D two-way dense layer to
capture the local and global features of the input. As convolution is a
computationally intensive operation, we introduce group convolution to decrease
calculation cost and parameter size further. Thus, the number of parameters and
the consumptions of calculation are observably less than contrapositive deep
learning methods, which means LiteDenseNet owns simpler architecture and higher
efficiency. A series of quantitative experiences on 6 widely used hyperspectral
datasets show that the proposed LiteDenseNet obtains the state-of-the-art
performance, even though when the absence of labelled samples is severe.
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