Many deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN), have been successfully applied to extracting deep features for hyperspectral tasks. Hyperspectral image classification allows distinguishing the characterization of land covers by utilizing their abundant information. Motivated by the attention mechanism of the human visual system, in this study, we propose a spectral-spatial attention network for hyperspectral image classification. In our method, RNN with attention can learn inner spectral correlations within a continuous spectrum, while CNN with attention is designed to focus on saliency features and spatial relevance between neighboring pixels in the spatial dimension. Experimental results demonstrate that our method can fully utilize the spectral and spatial information to obtain competitive performance.

Anomaly detection of hyperspectral data has been gaining particular attention for its ability in detecting targets in an unsupervised manner. Autoencoder (AE), together with its variants can not only extract intrinsic features automatically but also detect anomalies that differ dramatically from others. Many AE-driven algorithms are, thus, proposed for anomaly detection in hyperspectral imagery (HSI), but they suffer from two problems: 1) when there exist anomalies in the training set, AE can generalize so well that it can also learn the abnormal patterns well, thereby reducing the ability to distinguish anomalies from the background and 2) geometric structure among samples are lost in latent space of AE, which is vital in hyperspectral anomaly detection. To tackle these problems, we propose a robust anomaly detector based on the AE framework, named robust graph AE (RGAE) detector, in this article. To be specific, we propose a robust AE framework with $\ell _{2,1}$ -norm that is robust to noise and anomalies during training. Meanwhile, we embed a superpixel segmentation-based graph regularization term (SuperGraph) into AE. This strategy can preserve the geometric structure and the local spatial consistency of HSI simultaneously and also effectively reduce the searching space and execution time for each pixel. Extensive experiments are conducted on five datasets, and the results demonstrate that our method has a better detection performance, after comparing with other state-of-the-art hyperspectral anomaly detectors.

Effective and efficient infrared (IR) small target detection is essential for IR search and tracking (IRST) systems. The current methods have some limitations in background suppression or detection of targets close to each other. In this letter, a double-neighborhood gradient method (DNGM) is proposed. First, a new technology of the tri-layer sliding window is designed to measure the double-neighborhood gradient. Then, the DNGM is obtained by multiplying the double-neighborhood gradient. In this way, even the sizes of the targets may vary, ranging from $2 \times 1$ to $9 \times 9$ pixels, the target can be better highlighted under a fixed scale, and background interference can be suppressed. Finally, the target is segmented from the DNGM salience map by an adaptive threshold. Experiments illustrate that the proposed method can avoid the “expansion effect” of the traditional multiscale human vision system (HVS) method and can accurately detect multiple targets close to each other. Besides, the proposed method is more robust and real-time than the existing methods.

Spectral unmixing (SU), which refers to extracting basic features (i.e., endmembers) at the subpixel level and calculating the corresponding proportion (i.e., abundances), has become a major preprocessing technique for the hyperspectral image analysis. Since the unmixing procedure can be explained as finding a set of low-dimensional representations that reconstruct the data with their corresponding bases, autoencoders (AEs) have been effectively designed to address unsupervised SU problems. However, their ability to exploit the prior properties remains limited, and noise and initialization conditions will greatly affect the performance of unmixing. In this article, we propose a novel technique network for unsupervised unmixing which is based on the adversarial AE, termed as adversarial autoencoder network (AAENet), to address the above problems. First, the image to be unmixed is assumed to be partitioned into homogeneous regions. Then, considering the spatial correlation between local pixels, the pixels in the same region are assumed to share the same statistical properties (means and covariances) and abundance can be modeled to follow an appropriate prior distribution. Then the adversarial training procedure is adapted to transfer the spatial information into the network. By matching the aggregated posterior of the abundance with a certain prior distribution to correct the weight of unmixing, the proposed AAENet exhibits a more accurate and interpretable unmixing performance. Compared with the traditional AE method, our approach can greatly enhance the performance and robustness of the model by using the adversarial procedure and adding the abundance prior to the framework. The experiments on both the simulated and real hyperspectral data demonstrate that the proposed algorithm can outperform the other state-of-the-art methods.

Capturing accurate multiscale semantic information from the images is of great importance for high-quality semantic segmentation. Over the past years, a large number of methods attempt to improve the multiscale information capturing ability of the networks via various means. However, these methods always suffer unsatisfactory efficiency (e.g., speed or accuracy) on the images that include a large number of small-scale objects, for example, aerial images. In this article, we propose a new network named cross fusion net (CF-Net) for fast and effective extraction of the multiscale semantic information, especially for small-scale semantic information. In particular, the proposed CF-Net can capture more accurate small-scale semantic information from two aspects. On the one hand, we develop a channel attention refinement block to select the informative features. On the other hand, we propose a cross fusion block to enlarge the receptive field of the low-level feature maps. As a result, the network can encode more accurate semantic information from the small-scale objects, and the segmentation accuracy of the small-scale objects is improved accordingly. We have compared the proposed CF-Net with several state-of-the-art semantic segmentation methods on two popular aerial image segmentation data sets. Experimental results reveal that the average $F_{1}$ score gain brought by our CF-Net is about 0.43% and the $F_{1}$ score gain of the small-scale objects ( e.g. , cars) is about 2.61%. In addition, our CF-Net has the fastest inference speed, which proves its superiority in the aerial scenes. Our code will be released at: https://github.com/pcl111/CF-Net .

Denoising is an important preprocessing step to further analyze the hyperspectral image (HSI), and many denoising methods have been used for the denoising of the HSI data cube. However, the traditional denoising methods are sensitive to outliers and non-Gaussian noise. In this paper, by utilizing the underlying low-rank tensor property of the clean HSI data and the sparsity property of the outliers and non-Gaussian noise, we propose a new model based on the robust low-rank tensor recovery, which can preserve the global structure of HSI and simultaneously remove the outliers and different types of noise: Gaussian noise, impulse noise, dead lines, and so on. The proposed model can be solved by the inexact augmented Lagrangian method, and experiments on simulated and real hyperspectral images demonstrate that the proposed method is efficient for HSI denoising.