Lightweight Heterogeneous Kernel Convolution for Hyperspectral Image Classification With Noisy Labels

Convolutional neural networks (CNNs) have exhibited commendable performance in the hyperspectral images (HSIs) classification task with manually annotated limited available training data for supervision. The accurate classification of pixel-wise land covers using traditional CNNs is often hampered by the presence of wrong (noisy) labels in the training data and can easily be overfitted to the label noises. However, training on noisy labeled data inevitably suffers from performance degradation since CNNs tend to overfit the label noises. To overcome this problem, we propose a lightweight heterogeneous kernel convolution (
HetConv3D
) for HSI classification with noisy labels, where
HetConv3D
uses two different types of convolutional kernels, i.e., spectral and spatial domains, and fuses them to produce the final feature maps that are less prompted to the noises and also reduces the computation time. The experiments are conducted using three well-known HSI datasets, i.e., Kennedy Space Center (KSC), Salinas Scene (SA), and University of Pavia (UP), and results are compared with traditional supervised classification methods, including support vector machine (SVM), random forest (RF), CNN3D, ContextNet, MS3DNet, lightweight dual-channel residual network (DCRN), and HetConv3DNet. The superior performance exhibited by the proposed model
HetConv3D-HSI
confirms the importance of learning a fusion of spatial and spectral kernel features. The source code will be made available publicly at
https://github.com/purbayankar/HetConv3DNet
.