One of the challenges in hyperspectral image (HSI) classification is that there are limited labeled samples to train a classifier for very high-dimensional data. In practical applications, we often encounter an HSI domain (called target domain) with very few labeled data, while another HSI domain (called source domain) may have enough labeled data. Classes between the two domains may not be the same. This article attempts to use source class data to help classify the target classes, including the same and new unseen classes. To address this classification paradigm, a meta-learning paradigm for few-shot learning (FSL) is usually adopted. However, existing FSL methods do not account for domain shift between source and target domain. To solve the FSL problem under domain shift, a novel deep cross-domain few-shot learning (DCFSL) method is proposed. For the first time, DCFSL tackles FSL and domain adaptation issues in a unified framework. Specifically, a conditional adversarial domain adaptation strategy is utilized to overcome domain shift, which can achieve domain distribution alignment. In addition, FSL is executed in source and target classes at the same time, which can not only discover transferable knowledge in the source classes but also learn a discriminative embedding model to the target classes. Experiments conducted on four public HSI data sets demonstrate that DCFSL outperforms the existing FSL methods and deep learning methods for HSI classification. Our source code is available at https://github.com/Li-ZK/DCFSL-2021 .

Cross-domain hyperspectral image classification is one of the major challenges in remote sensing, especially for target domain data without labels. Recently, deep learning approaches have demonstrated effectiveness in domain adaptation. However, most of them leverage unlabeled target data only from a statistical perspective but neglect the analysis at the instance level. For better statistical alignment, existing approaches employ the entire unevaluated target data in an unsupervised manner, which may introduce noise and limit the discriminability of the neural networks. In this article, we propose confident learning-based domain adaptation (CLDA) to address the problem from a new perspective of data manipulation. To this end, a novel framework is presented to combine domain adaptation with confident learning (CL), where the former reduces the interdomain discrepancy and generates pseudo-labels for the target instances, from which the latter selects high-confidence target samples. Specifically, the confident learning part evaluates the confidence of each pseudo-labeled target sample based on the assigned labels and the predicted probabilities. Then, high-confidence target samples are selected as training data to increase the discriminative capacity of the neural networks. In addition, the domain adaptation part and the confident learning part are trained alternately to progressively increase the proportion of high-confidence labels in the target domain, thus further improving the accuracy of classification. Experimental results on four datasets demonstrate that the proposed CLDA method outperforms the state-of-the-art domain adaptation approaches. Our source code is available at https://github.com/Li-ZK/CLDA-2022 .

Deep domain adaptation has achieved promising results in cross-domain hyperspectral image (HSI) classification. However, existing methods often focus on aligning data distributions without sufficient consideration of separability of source and target domain data themselves. In addition, current adversarial domain adaptation methods aim to achieve similar distributions between domains by confusing the discriminator, rather than obtaining a more compact distribution. In particular, existing methods are not discriminative enough for the target domain due to the difficulty of obtaining high-confidence labeled samples of the target domain. To address the above challenges, we propose a supervised contrastive learning-based unsupervised domain adaptation for HSI classification. A supervised contrastive learning strategy is then performed in both the source and target domains, which allows samples from the same category to be pulled closer together and samples from different categories to be pushed further apart, thus enhancing the separability of the data within the domain. The domain adaptation task is treated as a one-class classification (OCC) task, and a novel domain similarity loss based on OCC is introduced to reduce the discrepancy between domains. Finally, a confidence learning-based sample selection strategy is designed to select high-confidence labeled samples from the target domain to fine-tune the domain adaptation model, which can enhance the discrimination of the model to the target domain. Experimental results on three cross-domain datasets demonstrate that our proposed method outperforms existing domain adaptation methods. Our source code is available at https://github.com/Li-ZK/SCLUDA-2023.

Deep learning has achieved great success in hyperspectral image (HSI) classification. However, its success relies on the availability of sufficient training samples. Unfortunately, the collection of training samples is expensive, time-consuming, and even impossible in some cases. Natural image datasets that are different from HSI, such as Image Net and mini-ImageNet, have abundant texture and structure information. Effective knowledge transfer between two heterogeneous datasets can significantly improve the accuracy of HSI classification. In this letter, heterogeneous few-shot learning (HFSL) for HSI classification is proposed with only a few labeled samples per class. First, few-shot learning is performed on the mini-ImageNet datasets to learn the transferable knowledge. Then, to make full use of the spatial and spectral information, a spectral–spatial fusion network is devised. Spectral information is obtained by the residual network with pure 1-D operators. Spatial information is extracted by a convolution network with pure 2-D operators, and the weights of the spatial network are initialized by the weights of the model trained on the mini-ImageNet datasets. Finally, few-shot learning is fine-tuned on HSI to extract discriminative spectral–spatial features and individual knowledge, which can improve the classification performance of the new classification task. Experiments conducted on two public HSI datasets demonstrate that the HFSL outperforms the existing few-shot learning methods and supervised learning methods for HSI classification with only a few labeled samples. Our source code is available at https://github.com/Li-ZK/HFSL .

Hyperspectral image (HSI) classification has drawn increasing attention recently. However, it suffers from noisy labels that may occur during field surveys due to a lack of prior information or human mistakes. To address this issue, this article proposes a novel dual-channel residual network (DCRN) to resolve HSI classification with noisy labels. Currently, the influence of noisy labels is reduced by simply detecting and removing those anomalous samples. Different from such a specifically designed noise cleansing method, DCRN is easy to implement but highly effective. It enhances its model robustness to noisy labels to a great extent by employing a novel dual-channel structure and a noise-robust loss function. In this way, DCRN can mitigate influence from noisy labels while fully utilizing useful information from mislabeled samples for augmented training. Experiments are conducted on several hyperspectral data sets with manually generated noisy labels to demonstrate its excellent performance. The code is available at https://github.com/Li-ZK/DCRN-2021 .

Deep spectral-spatial features fusion has become a research focus in hyperspectral image classification. However, how to exact more robust spectral-spatial features is still a challenging problem. In this paper, a novel deep multi-layer fusion dense network (MFDN) is proposed to improve the performance of hyperspectral image classification. The proposed MFDN simultaneously extracts the spatial and spectral features based on different sample input sizes, which can extract abundant spectral and spatial correlation information. Firstly, the principal component analysis (PCA) algorithm is performed on hyperspectral data to extract low-dimensional HSI data, and then the spatial features are extracted from the low-dimensional 3D HSI data through 2D convolutional, 2D dense block and average-pooling layers. Secondly, the spectral features are extracted directly from the raw 3D HSI data by means of 3D convolutional, 3D dense block and average-pooling layers. Thirdly, the spatial and spectral features are fused together through 3D convolutional, 3D dense block and average-pooling layers. Finally, the fused spectral-spatial features are sent into two full connection layers to extract high-level abstract features. Furthermore, densely-connected structures can help alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse and improve the HSI classification accuracy. The proposed fusion network outperforms the other state-of-the-art methods especially with a small number of labeled samples. Experimental results demonstrate that it can achieve outstanding hyperspectral classification performance.