Shallow Network Based on Depthwise Over-Parameterized Convolution for Hyperspectral Image Classification

TL;DR

This paper introduces a shallow hyperspectral image classification model using depthwise over-parameterized convolution to enhance feature extraction, reduce overfitting, and preserve spatial details, outperforming deeper models.

Contribution

The paper proposes a novel shallow CNN model with depthwise over-parameterized convolution and dense residual connections for improved hyperspectral image classification.

Findings

Outperforms state-of-the-art methods in accuracy

Achieves higher computational efficiency

Effectively preserves spatial edge features

Abstract

Recently, convolutional neural network (CNN) techniques have gained popularity as a tool for hyperspectral image classification (HSIC). To improve the feature extraction efficiency of HSIC under the condition of limited samples, the current methods generally use deep models with plenty of layers. However, deep network models are prone to overfitting and gradient vanishing problems when samples are limited. In addition, the spatial resolution decreases severely with deeper depth, which is very detrimental to spatial edge feature extraction. Therefore, this letter proposes a shallow model for HSIC, which is called depthwise over-parameterized convolutional neural network (DOCNN). To ensure the effective extraction of the shallow model, the depthwise over-parameterized convolution (DO-Conv) kernel is introduced to extract the discriminative features. The depthwise over-parameterized Convolution kernel is composed of a standard convolution kernel and a depthwise convolution kernel, which can extract the spatial feature of the different channels individually and fuse the spatial features of the whole channels simultaneously. Moreover, to further reduce the loss of spatial edge features due to the convolution operation, a dense residual connection (DRC) structure is proposed to apply to the feature extraction part of the whole network. Experimental results obtained from three benchmark data sets show that the proposed method outperforms other state-of-the-art methods in terms of classification accuracy and computational efficiency.