Hyperspectral image classification with residual learning networks

Shengliang Pu; Lianru Gao; Yining Song; Yingyao Chen; Yating Li; Lingxin Luo; Guangyu Xu; Xiaowei Xie; Yunju Nie

doi:10.1117/12.2605472

24 November 2021 Hyperspectral image classification with residual learning networks

Shengliang Pu, Lianru Gao, Yining Song, Yingyao Chen, Yating Li, Lingxin Luo, Guangyu Xu, Xiaowei Xie, Yunju Nie

Proceedings Volume 12065, AOPC 2021: Optical Sensing and Imaging Technology; 120651E (2021) https://doi.org/10.1117/12.2605472
Event: Applied Optics and Photonics China 2021, 2021, Beijing, China

Abstract

Hyperspectral imaging is particularly useful for per-pixel thematic classification by unique spectral signatures of landscape materials. Deep learning techniques such as convolutional neural networks have boosted the performance of image classification. Recently, several composite learning-based convolutional networks, i.e., deep residual networks (ResNets) and dense convolutional networks (DenseNets), have been presented to learn deep feature representation for image classification, and achieve high classification accuracies. In this paper, we present a fairly comparable architecture, including two kinds of modified residual learning networks with a shallow depth using small training data. First, we perform the extraction of key components from deep residual networks and dense convolutional networks, which is a set of composite learning structures with skip connections. Second, the plain convolutional neural networks (PNets) have been constituted by a stack of plain blocks that also have been placed in the presented network architecture as the baseline networks. Third, we make them as comparable as possible with the plain convolutional network structures, so that the more profound exploration and improvement could be further done. Finally, we wrap them together and design a comparable architecture. Experiments demonstrate that the presented residual learning networks show special characteristics for hyperspectral image classification, which have not been revealed before.

Citation Download Citation

Shengliang Pu, Lianru Gao, Yining Song, Yingyao Chen, Yating Li, Lingxin Luo, Guangyu Xu, Xiaowei Xie, and Yunju Nie "Hyperspectral image classification with residual learning networks", Proc. SPIE 12065, AOPC 2021: Optical Sensing and Imaging Technology, 120651E (24 November 2021); https://doi.org/10.1117/12.2605472

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KEYWORDS

Data modeling

Network architectures

Image classification

Composites

Performance modeling

Hyperspectral imaging

Networks

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