Self-supervised contrastive learning performs well in representing hyperspectral data under limited labels. However, the heterogeneity poses challenges in balancing a fixed temperature to prevent model collapse with accurately identifying hard negatives. In this paper, we propose a hardness-aware prototypical contrastive learning method (HAPC) for hyperspectral coastal wetlands classification. Firstly, we propose a spatial-spectral integration data augmentation method to enhance the construction of positive counterparts. Additionally, we introduce a hardness-aware temperature reweighting strategy to effectively identify challenging negatives and preserve manifold integrity of data. Experiments on two hyperspectral datasets captured by Zhuhai-1 satellite indicate that HAPC can improve feature representation of hyperspectral coastal wetlands. Our code and datasets is available at https://github.com/sakurashine/HAPC.

In this letter, a novel wavelet-inspired sparse learning network (WISLNet) is proposed for hyperspectral image change detection (HSI-CD), which introduces wavelet convolution transform into a data-driven low-rank and sparse representation (LRSR) model to build a deep unfolding network in the frequency domain. The WISLNet mainly involves the following key steps. First, the difference image (DI) of the bi-temporal hyperspectral images (Bi-HSIs) is obtained through pixel-by-pixel and band-by-band subtraction operations. Then, the LRSR model is designed as a deep unfolding network with modules for low-rank learning, sparse learning, and DI reconstruction to capture deep semantics related to change identification in the DI. Meanwhile, wavelet convolution is embedded in the above modules to progressively mine the low-rank and sparse features of the DI in the frequency domain. Next, an iterative optimization scheme based on low-rank and sparse features is used to capture the change semantic details between Bi-HSIs. Finally, a wavelet convolution filter is designed and applied to the sparse components to reflect the image change information. Experiments on River and Farmland Bi-HSIs demonstrated that the proposed WISLNet method is able to achieve superior CD results compared to well-known and state-of-the-art deep networks. The code is available at: https://github.com/chengle-zhou/WISLNet.

Representing hyperspectral images (HSI) is a complex and challenging task, primarily due to spectral uncertainty. Learnable prototypical contrastive learning is specialized in discriminative instance representation. However, it requires a much low temperature to prevent model collapse, which can hinder the encoder’s ability to capture category relationships. Furthermore, self-supervision at network terminal could obscure specific semantics in hyperfine spectra. In this paper, we propose a hierarchical and bidirectional learnable prototypical contrastive learning method (HiBiCo) for HSI representation. We build learnable prototype dictionaries at shallow and final layers of the network for deep contrastive supervision. Moreover, we introduce reverse contrastive learning under negative sample dominance to address excessively uniform representations caused by traditional positive-dominated contrastive loss in dual-dictionary hierarchical supervision. By allowing deep contrastive supervision and two-way information flow along with the general InfoNCE loss, our approach alleviates the uniform distribution, maintains the latent data manifold, and enables diverse and effective representation. Experiments with linear probing demonstrate the effectiveness of our HiBiCo framework in handling complex scenes, highlighting the potential of self-supervised pretraining for hyperspectral image representation. The code is available at https://github.com/sakurashine/HiBiCo.

Hyperspectral image change detection (HSI-CD) is a technique that intelligently checks the changed details in bitemporal hyperspectral images (Bi-HSIs). Deep learning (DL), with the ability to model nonlinear changing features, has achieved promising results in HSI-CD, but the feature mining mechanism is unclear and the architecture design lacks transparency in such DL models. To alleviate this problem, this paper proposes a new low-rank and sparse representation-based deep unfolding network (LRSRNet) for HSI-CD. For feature mining mechanism, the LRSRNet adopts a low-rank and sparse subnetwork (LRSnet) and a change detection sub-network (CDnet). The former is responsible for extracting low-rank features with valuable information and suppressing sparse features containing interference information, while the latter aims to obtain change information from low-rank features. For architecture design, the LRSnet formulates the HSI as a low-rank estimation, sparse estimation, and hyperspectral reconstruction in a low-rank and sparse model, and iteratively optimizes and updates the above sub-problems through deep networks. A new CDnet is designed as a concise convolutional architecture to extract change information from representative Bi-HSIs features. Experiments on three real datasets demonstrate the performance superiority of the proposed LRSRNet method over nine model-driven, datadriven, and model-data-joint-driven HSI-CD algorithms in both qualitative and quantitative evaluations. The proposed LRSRNet is available online: https://github.com/chengle-zhou/LRSRNet.

Hyperspectral videos (HSVs) play an important role in the monitoring domain, as they can provide more information than RGB videos about the movement of interesting objects from the perspective of material interpretation. However, the acquisition of HSV data is expensive and time-consuming, whereas RGB videos are readily available. In order to obtain HSV data from its corresponding RGB data, this paper proposes a lightweight frequency-spectrum unfolding network (FSUF-Net) for spectral super-resolution (SSR) of RGB video data. Specifically, the proposed FSUF-Net method belongs to a data-knowledge-driven joint paradigm, which is an interpretable SSR model instead of an end-to-end black-box architecture. The FSUF-Net consists of five main steps. First, the conversion representation of RGB video data to HSV data is derived into an initial recovery term, a data term, and a prior term according to a variable splitting method. Second, the spectral response function between hyperspectral images (HSIs) and RGB images is utilized to achieve the initial recovery term. Third, a convolutional neural network (CNN)-based frequency-domain subnetwork (called F-Net) is designed to solve the data subproblem for recovering the spatial detail information from the HSI, and a Transformer-based spectrum-domain subnetwork (called S-Net) is developed to solve the prior subproblem for reconstructing the spectral information of the HSI. Fourth, two network modules are employed to conduct parametric self-learning. Finally, the HSV data can be obtained in a fixed number of iterations, including alternately solving the above data subproblem and the prior subproblem. Experiments performed on several real datasets demonstrated that the FSUF-Net can effectively reconstruct HSV from RGB videos as compared to traditional and state-of-the-art SSR methods. The proposed method is available online: https://github.com/chengle-zhou/HSV-SSR_FSUF-Net.

In this letter, a novel spatial peak-aware collaborative representation (SPaCR) method is proposed for hyperspectral imagery (HSI) classification, which introduces spectral–spatial information among superpixel clusters into regularization terms to construct a new collaborative representation (CR)-based closed-form solution. The proposed method is composed of the following key steps. First, the raw HSI is clustered into many superpixels according to an oversegmentation strategy. Then, cluster pixels are determined based on spectral–spatial correlation between pixels within each superpixel. Next, spectral distance and spatial coherence of superpixel clusters corresponding to training samples and testing pixels are fused to define differences between pixels. Finally, the difference information between clusters as a spectral–spatial feature-induced regularization term is incorporated into the objective function. Experimental results on the Indian Pines and the University of Pavia HSIs indicated that the proposed SPaCR method, without any preprocessing and postprocessing, outperforms well-known and state-of-the-art classifiers on the limited labeled samples.

This letter introduces a novel spatial-spectral classification method for hyperspectral images (HSIs) based on a structural-kernel collaborative representation (SKCR), which considers one weak assumption of spatial neighborhood that of the pixels in a superpixel belong to the same class when exploiting contextual information in HSI. The proposed method consists of the following steps. First, a superpixel segmentation strategy is used to construct self-adaptive regions for the HSI. Then, the structural information within each superpixel block is extracted based on the density peak and K nearest neighbors. Next, dual kernels are separately utilized for the exploitation of the spectral and the spatial information. Finally, the dual kernels are combined and incorporated into a support-vector-machine classifier. Since the weak assumption of spatial neighborhood is well considered in the collaborative representation, the proposed method showed excellent classification performance for two widely used real hyperspectral data sets even when the number of training samples was relatively small.

Spectral–spatial information plays an essential role in hyperspectral image (HSI) classification compared to pure spectral information. However, the neighbor spectral–spatial information of a pixel tends to be mixed into other ground coverings due to various external factors such as the weather and sensor jitter, and mainstream HSI classification methods present low sensitivity for spatial information in this situation. This article proposes a novel feature extraction method via 3-D block characteristics sharing (3-D-BCS) for HSI classification that redefines spatial–spectral information of a local region based on a superpixel perspective to overcome the spectral–spatial weak assumptions in feature extraction that consists of the following steps. First, 3-D blocks are obtained by performing an oversegmentation method on the raw HSI. Then, instead of global operation, a 3-D block-based Gabor filter is applied to the principal components of an HSI to extract the textural features. Next, an average operation is conducted on each shape adaptive region to address the spatial weak assumption and Gaussian weight is introduced into each superpixel block to overcome the spectral weak assumption. Thus, 3-D characteristics sharing blocks can be constructed by reshaping the above three kinds of spectral–spatial feature. Finally, the majority-based support vector machine (SVM) classifier is utilized to determine the final class labels of HSI at the decision fusion level. Experiments performed on several real hyperspectral data sets with limited training samples show that the proposed 3-D-BCS method outperforms the other types of the classification method.

Hyperspectral anomaly detection is one of the most active topics in hyperspectral image (HSI) analysis. The fine spectral information of HSIs allows us to uncover anomalies with very high accuracy. Recently, an intrinsic image decomposition (IID) model has been introduced for low-rank IID (LRIID) in multispectral images. Inspired by the LRIID, which is able to effectively recover the reflectance and shading components of the multispectral image, this article adapts the LRIID for obtaining the reflectance component of HSIs (which is the key feature for the discrimination of different objects). In order to exploit the reflectance component, we also propose a new dual window density (DWD)-based detector for anomaly detection, which is based on the idea that anomalies are usually rare pixels and, thus, exhibit low density in the image. The density analysis of DWD is intended not only to circumvent the Gaussian assumption regarding the distribution of HSI data, but also to mitigate the contamination of background statistics caused by anomalies. The dual window operation of our DWD is specifically designed to adaptively calculate the density of each pixel under test, so as to identify anomalies with nonspecific sizes. Our experimental results, obtained on a database of real HSIs including Airport, Beach, and Urban scenes, demonstrate the superiority of the proposed method in terms of detection performance when compared to other widely used anomaly detection methods.

Classification is an important technique for remotely sensed hyperspectral image (HSI) exploitation. Often, the presence of wrong (noisy) labels presents a drawback for accurate supervised classification. In this article, we introduce a new framework for noisy label detection that combines a superpixel-to-pixel weighting distance (SPWD) and density peak clustering. The proposed method is able to accurately detect and remove noisy labels in the training set before HSI classification. It considers two weak assumptions when exploiting the spectral-spatial information contained in the HSI: 1) all the pixels in a superpixel belong to the same class and 2) close pixels in spectral space have the same label. The proposed method consists of the following steps. First, a superpixel segmentation step is used to obtain self-adaptive spatial information for each training sample. Then, a metric is utilized to measure the spectral distance information between each superpixel and pixel. Meanwhile, in order to overcome the first weak assumption, we use K nearest neighbors to obtain the closest neighborhoods of pixels around each superpixel, and a Gaussian weight is employed to mitigate the second weak assumption by adapting the original distance information. Next, the noisy labels in the original training set are removed by a density threshold-based decision function. Finally, the support vector machine (SVM) classifier is employed to evaluate the effectiveness of the proposed SPWD detection method in terms of classification accuracy. Experiments performed on several real HSI data sets demonstrate that the method can effectively improve the performance of classifiers trained with noisy training sets in terms of classification accuracy.

The clustering methods have a good application in many aspects, in which the density peak (DP) clustering can effectively cluster similar neighboring pixels, so that the features can be extracted well for hyperspectral images (HSIs) classification. In this work, a density peak based covariance matrix (DPCM) method is proposed for HSIs classification, which not only can effectively extracts features, but also can reduce the within-class variations and the between-class interference. The proposed method consists of the following steps: first, maximum noise fraction (MNF) is employed on the original HSI to reduce the computational complexity and eliminate noise. Second, the local densities of sample is calculated by the DP clustering. Therefore, the density map can be obtained in which each pixel has a density value in the original image. Then, the covariance matrix between each density pixel in the density map is calculated. Last, the extracted covariance matrices are fed back to the support vector machine (SVM) based on the logarithm Euclidean kernel for label assignment. Experiments on the Indian pine data set show that this method is superior to other classification methods.

This letter presents the hyperspectral imagery (HSI) noisy label detection using a spectral angle and the local outlier factor (SALOF) algorithm. The noisy label is caused by a mislabeled training pixel, and thus, noisy training samples mixed with correct and incorrect labels are formed in the supervised classification. The LOF algorithm is first used in the noisy label detection of the HSI to improve the supervised classification accuracy. The proposed method SALOF mainly includes the following steps. First, k nearest neighbors of different training samples of each class are calculated based on the spectral angle mapper. Second, the reachability distance and local reachability density of all training samples are obtained. Third, the LOF is determined among different classes of training samples. Then, a segmentation threshold of the LOF is established to achieve an abnormal probability of these training samples. Finally, the support vector machines are applied to measure the detection efficiency of the proposed method. The experiments performed on the Kennedy Space Center data set demonstrate that the proposed method can effectively detect noisy labels.