Deep Induction Network for Small Samples Classification of Hyperspectral Images

Abstract

Recently, the deep learning models have achieved great success in hyperspectral images classification. However, most of the deep learning models fail to obtain satisfactory results under the condition of small samples due to the contradiction between the large parameter space of the deep learning models and the insufficient labeled samples in hyperspectral images. To address the problem, a deep model based on the induction network is designed in this paper to improve the classification performance of hyperspectral images under the condition of small samples. Specifically, the typical meta-training strategy is adopted, enabling the model to acquire stronger generalization ability, so as to accurately distinguish the new classes with only a few labeled samples (e.g., 5 samples per class). Moreover, in order to deal with the disturbance caused by the various characteristics of the samples in the same class in HSI, the classwise induction module is introduced utilizing the dynamic routing algorithm, which can induce the sample-wise representations to the class-wise level representations. The obtained class-wise level representations possess better separability, allowing the designed model to generate more accurate and robust classification results. Extensive experiments are carried out on three public hyperspectral images to verify the effectiveness of the proposed method. The results demonstrate that our method outperforms existing deep learning methods under the condition of small samples.

Graph inductive learning method for small sample classification of hyperspectral remote sensing images

“…For this reason, how to improve the classification accuracy of hyperspectral images under the conditions of small samples has become a research hotspot. Therefore, many existing technologies were used to address this problem, such as semi-supervised learning (Fang et al, 2018), active learning (Fang et al, 2018), unsupervised feature extraction (Mei et al, 2019), metal learning (Gao et al, 2020) and so on.…”

Hyperspectral Image Classification Via Spectral-Spatial Random Patches Network

“…where D ∈ R r×c×P is the tensor matrix composed by P replications of the mean matrix of M in the third dimension, F (1) represents feature in the first layer.…”

“…Deep feature extraction of the SSRPNet. After F (1) passes through the second layer of the SSRPNet, the feature F (2) can be obtained. In this way, we can get the features of any layer.…”

“…The combined feature F com ∈ R r×c×(LP +2m+t) used for classification will be combination of feature F ∈ {F (1) , F (2) , . .…”

“…H YPERSPECTRAL image (HSI) classification is one of the most important steps in HSI analysis and application, and has become an active research subject in the field of remote sensing [1], which attempts to identify the category for each hyperspectral pixel. Hyperspectral image (HSI) is a three-dimensional (3D) data, which is composed of onedimensional spectral signatures and two-dimensional (2D) spatial information [2].…”