A CNN-based Spatial Feature Fusion Algorithm for Hyperspectral Imagery Classification

TL;DR

This paper introduces a CNN-based spatial feature fusion algorithm that enhances hyperspectral image classification by effectively combining spectral and spatial information without increasing training data or using pixel patches during training.

Contribution

The proposed CSFF algorithm innovatively fuses spatial information with spectral features using a CNN-based discriminant model, achieving state-of-the-art results without additional training data.

Findings

Achieves 20-50% reduction in classification errors.

Does not require pixel patches during training.

Outperforms existing methods on benchmark datasets.

Abstract

The shortage of training samples remains one of the main obstacles in applying the artificial neural networks (ANN) to the hyperspectral images classification. To fuse the spatial and spectral information, pixel patches are often utilized to train a model, which may further aggregate this problem. In the existing works, an ANN model supervised by center-loss (ANNC) was introduced. Training merely with spectral information, the ANNC yields discriminative spectral features suitable for the subsequent classification tasks. In this paper, a CNN-based spatial feature fusion (CSFF) algorithm is proposed, which allows a smart fusion of the spatial information to the spectral features extracted by ANNC. As a critical part of CSFF, a CNN-based discriminant model is introduced to estimate whether two paring pixels belong to the same class. At the testing stage, by applying the discriminant model to the pixel-pairs generated by the test pixel and its neighbors, the local structure is estimated and represented as a customized convolutional kernel. The spectral-spatial feature is obtained by a convolutional operation between the estimated kernel and the corresponding spectral features within a neighborhood. At last, the label of the test pixel is predicted by classifying the resulting spectral-spatial feature. Without increasing the number of training samples or involving pixel patches at the training stage, the CSFF framework achieves the state-of-the-art by declining $20\%-50\%$ classification failures in experiments on three well-known hyperspectral images.