LiteDenseNet: A Lightweight Network for Hyperspectral Image Classification
This work addresses the resource-intensive nature of deep learning for hyperspectral image classification, offering a more efficient solution for researchers and practitioners in remote sensing and related fields, though it is incremental as it builds on existing architectures like DenseNet.
The authors tackled the problem of high computational and data annotation costs in hyperspectral image classification by proposing LiteDenseNet, a lightweight network that reduces parameters and calculations while achieving state-of-the-art performance on six datasets, even with limited labeled samples.
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.