Citation: Pei, S.; Song, H.; Lu, Y.
Small Sample Hyperspectral Image
Classification Method Based on
Dual-Channel Spectral Enhancement
Network. Electronics 2022, 11, 2540.
https://doi.org/10.3390/
electronics11162540
Academic Editor: Silvia Liberata
Ullo
Received: 28 June 2022
Accepted: 7 August 2022
Published: 13 August 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
Small Sample Hyperspectral Image Classification Method
Based on Dual-Channel Spectral Enhancement Network
Songwei Pei, Hong Song * and Yinning Lu
School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and
Telecommunications, Beijing 100876, China
* Correspondence: songhongcode@163.com
Abstract:
Deep learning has achieved significant success in the field of hyperspectral image (HSI)
classification, but challenges are still faced when the number of training samples is small. Feature
fusing approaches based on multi-channel and multi-scale feature extractions are attractive for HSI
classification where few samples are available. In this paper, based on feature fusion, we proposed a
simple yet effective CNN-based Dual-channel Spectral Enhancement Network (DSEN) to fully exploit
the features of the small labeled HSI samples for HSI classification. We worked with the observation
that, in many HSI classification models, most of the incorrectly classified pixels of HSI are at the
border of different classes, which is caused by feature obfuscation. Hence, in DSEN, we specially
designed a spectral feature extraction channel to enhance the spectral feature representation of the
specific pixel. Moreover, a spatial–spectral channel was designed using small convolution kernels
to extract the spatial–spectral features of HSI. By adjusting the fusion proportion of the features
extracted from the two channels, the expression of spectral features was enhanced in terms of the
fused features for better HSI classification. The experimental results demonstrated that the overall
accuracy (OA) of HSI classification using the proposed DSEN reached 69.47%, 80.54%, and 93.24%
when only five training samples for each class were selected from the Indian Pines (IP), University
of Pavia (UP), and Salinas Scene (SA) datasets, respectively. The performance improved when the
number of training samples increased. Compared with several related methods, DSEN demonstrated
superior performance in HSI classification.
Keywords:
HSI classification; small sample; CNN; dual channel network model; 3D–2D convolution
1. Introduction
Hyperspectral remote sensing is an important research field in remote sensing sci-
ence [
1
]. Typically, the number of spectral segments and the data size of HSI are much
greater than that of ordinary images, thereby presenting challenges to the storage and
analysis of HSI. However, due to the rich spatial and spectral information contained, HSI
plays an especially important role in a wide range of applications, such as vegetation
research [
2
], fine agriculture [
3
,
4
], agricultural product detection [
5
], and environmental
monitoring [
6
]. The classification and recognition of ground cover based on HSI represents
an important step in promoting the application of hyperspectral remote sensing technology.
HSI classification is used to determine the class of each pixel of HSI and has become a hot
research topic in the field of hyperspectral remote sensing [7].
The traditional HSI classification methods include support vector machine (SVM) [
8
],
random forest [
9
,
10
], etc. Due to the spectrum of HSI, the Hughes phenomenon easily
occurs in HSI classification. Therefore, researchers proposed various methods for the
dimensionality reduction of HSI, such as PCA [
11
], PPCA [
12
], and ICA [
13
]. Dimension-
ality reduction can effectively eliminate the redundancy of HSI data, thereby extracting
HSI features better. In the traditional HSI classification, the classification method and the
intermediate parameter setting depend on past experience, resulting in an unsatisfactory
classification result and robustness.
Electronics 2022, 11, 2540. https://doi.org/10.3390/electronics11162540 https://www.mdpi.com/journal/electronics
