Abstract
Deep learning has achieved great success in hyperspectral image classification. However, when processing new hyperspectral images, the existing deep learning models must be retrained from scratch with sufficient samples, which is inefficient and undesirable in practical tasks. This paper aims to explore how to accurately classify new hyperspectral images with only a few labeled samples, i.e., the hyperspectral images few-shot classification. Specifically, we design a new deep classification model based on relational network and train it with the idea of meta-learning. Firstly, the feature learning module and the relation learning module of the model can make full use of the spatial–spectral information in hyperspectral images and carry out relation learning by comparing the similarity between samples. Secondly, the task-based learning strategy can enable the model to continuously enhance its ability to learn how to learn with a large number of tasks randomly generated from different data sets. Benefitting from the above two points, the proposed method has excellent generalization ability and can obtain satisfactory classification results with only a few labeled samples. In order to verify the performance of the proposed method, experiments were carried out on three public data sets. The results indicate that the proposed method can achieve better classification results than the traditional semisupervised support vector machine and semisupervised deep learning models.
Highlights
Hyperspectral remote sensing, as an important means of earth observation, is one of the most important technological advances in the field of remote sensing
In order to alleviate the Hughes phenomenon caused by band redundancy, researchers introduced a series of feature extraction methods to extract spectral features conducive to classification from abundant spectral information
In order to further explore the influence of the number of labeled samples on the classification effect of relation network [49] for HSI few-shot classification (RN-FSC), we conducted comparative experiments on Salinas and Indian Pines data sets with reference to [57,58,59]
Summary
Hyperspectral remote sensing, as an important means of earth observation, is one of the most important technological advances in the field of remote sensing. As HSI have the unique advantage of “spatial–spectral unity” (HSI contain both abundant spectral and spatial information), hyperspectral remote sensing has been widely used in fine agriculture, land-use planning, target detection, and many other fields. The above feature extraction method can achieve some results, but ignoring spatial structure information in HSI still seriously hinders the increase of classification accuracy. To this end, a series of spatial information utilization methods are introduced, such as extended morphological profile (EMP) [9], local binary patterns (LBP) [10], 3D Gabor features [11], Markov random field (MRF) [12], spatial filtering [13], variants of non-negative matrix underapproximation (NMU) [14], and so on. The classification results of traditional methods largely depend on the accumulated experience and parameter setting, which lacks stability and robustness
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