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.

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.

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.

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.

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.