Recently, deep learning-based methods have drawn increasing attention in hyperspectral imagery (HSI) classification, due to their strong nonlinear mapping capability. However, these methods suffer from a time-consuming training process because of many network parameters. In this paper, the concept of broad learning is introduced into HSI classification. Firstly, to make full use of abundant spectral and spatial information of hyperspectral imagery, hierarchical guidance filtering is performing on the original HSI to get its spectral-spatial representation. Then, the class-probability structure is incorporated into the broad learning model to obtain a semi-supervised broad learning version, so that limited labeled samples and many unlabeled samples can be utilized simultaneously. Finally, the connecting weights of broad structure can be easily computed through the ridge regression approximation. Experimental results on three popular hyperspectral imagery datasets demonstrate that the proposed method can achieve better performance than deep learning-based methods and conventional classifiers.

Hyperspectral image (HSI) classification remains a challenging problem due to unique characteristics of HSI data (such as numerous bands and strong correlations in the spectral and spatial domains) and small sample size. To address such concerns, we propose a novel spectral–spatial feature extraction method for HSI classification by employing graph embedding and deep learning (DL) models. Since the conventional graph cannot capture the complex manifold relationship of HSI data, and there exist the observations of within-class variation as well as the similarity between different classes in the spectral domain, we construct the supervised within-class/between-class hypergraph (SWBH) to extract the spectral features of HSI. Since it is difficult for DL models to learn representative features for HSI data when the labeled training samples are limited, we propose the random zero settings to newly generate a large amount of labeled HSI samples for the training of convolutional neural network (CNN). The designed sample expanded CNN (SECNN) is used to extract the HSI spatial features. Thus, the spectral–spatial features of HSI can be learned by integrating the features extracted from SWBH and SECNN, respectively. Experiments on three real HSI datasets demonstrate higher classification accuracy of the proposed SWBH–SECNN method.

Due to the difficulty of labeling a large number of training samples of a hyperspectral image (HSI), unsupervised clustering methods have drawn great attention. The recently proposed broad learning (BL) can implement both linear and nonlinear mappings. However, the original BL is a supervised model. In this letter, a novel method named unsupervised BL (UBL) is introduced for HSI clustering. First, a graph-regularized sparse autoencoder is performed on the input and mapped feature of UBL in order to maintain the intrinsic manifold structure of origin HSI. Then, the objective function of UBL composed of an l₂-norm of output-layer weights and a graph regularization term is designed, which can be easily solved by choosing eigenvectors corresponding to the smallest eigenvalues. Finally, the HSI clustering results can be obtained by applying spectral clustering on the output of UBL. Experiments on three popular real HSI data sets demonstrate that, compared with several competitive methods, UBL can achieve better clustering performance.

The pre-trained vision-language models (VLMs) have achieved outstanding performance in various visual tasks, primarily due to the knowledge they’ve acquired from massive image-text pairs. This enables VLMs to generalize to a wide range of downstream tasks. This paper presents the first attempt to adapt VLMs for the joint classification task of hyperspectral image (HSI) and LiDAR data, aiming to leverage the well-learned VLMs to extract more generalizable features from diverse remote sensing image sources. Initially, by utilizing a patch encoder, low-dimensional patches of HSI and LiDAR data are transformed into high-dimensional latent feature representations, meeting the dimensional requirements of VLMs for visual input data. Unlike traditional classifiers that rely on discrete class labels, VLM-based classification methods depend on continuous vectors, which can be derived from textual templates with class names, i.e ., prompts. The classification performance of VLM-based methods heavily relies on these prompts, but prompt engineering not only demands extensive expert knowledge but is also extremely time-consuming. To address this, prompt tuning methods are introduced to enhance the generalizability of VLMs by adding spectral-based prompts to the vision encoder and incorporating randomly initialized, learnable text prompts into the text encoder. Finally, through a novel class-discriminative loss function, the distance between text features of different classes is increased, thereby enhancing the model’s discriminative ability. Experimental results on the Houston 2013, Trento, and MUUFL datasets demonstrate that the proposed method can achieve competitive classification accuracy with a limited number of labeled pixels.

At present many researchers pay attention to a combination of spectral features and spatial features to enhance hyperspectral image (HSI) classification accuracy. However, the spatial features in some methods are utilized insufficiently. In order to further improve the performance of HSI classification, the spectral-spatial joint classification of HSI based on the broad learning system (BLS) (SSBLS) method was proposed in this paper; it consists of three parts. Firstly, the Gaussian filter is adopted to smooth each band of the original spectra based on the spatial information to remove the noise. Secondly, the test sample’s labels can be obtained using the optimal BLS classification model trained with the spectral features smoothed by the Gaussian filter. At last, the guided filter is performed to correct the BLS classification results based on the spatial contextual information for improving the classification accuracy. Experiment results on the three real HSI datasets demonstrate that the mean overall accuracies (OAs) of ten experiments are 99.83% on the Indian Pines dataset, 99.96% on the Salinas dataset, and 99.49% on the Pavia University dataset. Compared with other methods, the proposed method in the paper has the best performance.

Adversarial learning-based unsupervised hyperspectral image (HSI) classification methods usually adapt probability distributions by minimizing the statistical distance between similar pixels of different HSIs. Since the adversarial learning may weaken the discriminability of features, the extracted features will contain a lot of non-discriminative information, pixels with similar features may be classified as different classes. Therefore, directly reducing the statistical distance between similar pixels in a latent space may aggravate misclassification. To this end, we propose an unsupervised HSI classification method called soft instance-level domain adaptation with virtual classifier. First, the domain-invariant features of HSI are extracted by a graph convolutional network. Then, a feature similarity metric-based virtual classifier is constructed to output class probabilities of target-domain samples. Furthermore, to enable similar features of HSIs from different domains to be classified into the same class, the divergence between the real and virtual classifiers is reduced by minimizing the real and virtual classifier determinacy disparity. Finally, to reduce the influence of noisy pseudo-labels, a soft instance-level domain adaptation method is proposed. For each target-domain sample, the confidence coefficients are assigned to its corresponding positive and negative samples in the source domain, and a soft prototype contrastive loss is constructed and minimized to adapt two domains in an instance-level way. Experimental results on five real HSI datasets including Botswana, Kennedy Space Center, Pavia Center, Pavia University, and HyRANK demonstrate the effectiveness of our proposed method.