The primary task of hyperspectral anomaly detection (HAD) is to distinguish targets with noticeable spectral variances from their surroundings. Background purification for dictionary construction is vital for representation based HAD. In this letter, we present a novel HAD method based on background purification via deep autoencoding gaussian mixture Model. First, the low-rank representation (LRR) model is applied to separate the sparse anomalies from the low-rank background component. Secondly, we innovatively apply the deep autoencoding gaussian mixture model (DAGMM) for dimensionality reduction and background purification, which combines the feature mapping and Gaussian density estimation for joint training in the deep latent space. In addition, we propose a dictionary construction strategy based on the gaussian mixture model, achieving the exclusion of possible anomalies and inclusion of background features. Experiments on two real datasets illustrated the superior performance for HAD task of the proposed method.

We proposed a learned masked robust principal component analysis (LMRPCA) algorithm for single-frame infrared small target detection. Firstly, the original images are constructed into patch images, which are separated into low-rank and sparse components corresponding to the backgrounds and foreground masks. The optimization function is solved by alternating directions of multipliers method (ADMM), which is mapped to trainable convolutional layers. We use elements of convolutional sparse coding to improve representation learning for foreground masks and side information in the auxiliary transform domain. By doing so, we assign learnable weights to different feature maps by using a reweighted−l1− l1minimization. Numerical experiments show that our proposed LMRPCA can segment and locate the targets precisely.

Detection of infrared dim and small targets with diverse and cluttered background plays a significant role in many applications. In this paper, we propose a deep low-rank and sparse patch-image network, termed as Deep-LSP-Net, to effectively detect small targets in a single infrared image. Specifically, by using the local patch construction scheme, we first transform the original infrared image into a patch-image, which can be decomposed as a superposition of the low-rank background component and the sparse target component. The target detection is thus formulated as an optimization problem with low-rank and sparse regularizations, which can be solved by the alternating direction method of multipliers (ADMM). We unroll the iterative algorithm into deep neural networks, where a generalized sparsifying transform and a singular value thresholding operator are learned by the convolutional neural networks (CNNs) to avoid tedious parameter tuning and improve the interpretability of the neural networks. We conduct comprehensive experiments on two public datasets. Both qualitative and quantitative experimental results demonstrate that the proposed algorithm can obtain improved performance in small infrared target detection compared with state-of-the-art algorithms.

Long-wave infrared (LWIR) bands in multispectral datasets are extremely useful in many applications. However, the LWIR bands usually suffer from undesirable stripe noise, which impedes their further application. Compared with emission-dominated LWIR, the mid-wave infrared (MWIR) bands containing both emitted and reflected radiation usually exhibit higher image quality. In this article, we propose a novel two-stage MWIR energy separation and image guidance (MES-IG) algorithm to destripe the LWIR images with the assistance of the MWIR bands. In the first stage, we decompose the MWIR image into the emitted and reflected components by solving a constrained optimization problem. Specifically, we impose the low-rank penalty to enforce the similarities between MWIR and LWIR, and we use the total variation (TV) regularization to exploit the similarities between MWIR and visible and near-infrared (VNIR) images. In the second stage, the obtained emitted component of MWIR is considered as the guidance image to remove the stripes in the LWIR images by adopting the 1-D guided filter algorithm. Numerical experiments on the Chinese Gaofen-5 satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) data demonstrate the utility of the proposed method in providing improved LWIR image destriping performance over the state-of-the-art algorithms.

Small infrared target detection is a significant technique in both civil and military applications. Regularized optimization methods that exploit both the sparsity and the low-rank prop-erties of the infrared image have achieved good performance. In this paper, we propose to unroll the sparse and low-rank regularized model to a deep neural network to effectively sep-arate the infrared target and the background. Specifically, we adopt the infrared patch-image (IPI) model to transform the original infrared image into a patch-image using local patch construction. A deep network flow graph is proposed by si-multaneously exploiting a learned low-rank prior and a spar-sity prior to promote the target detection performance. Exper-imental results demonstrate that the proposed IPI-net is able to provide improved performance in small infrared target de-tection compared with the state-of-the-art algorithms.

Cloud contamination is a common phenomenon in the optical remote sensing field, which limits their application in land surface studies and causes the waste of satellite images. This letter presents a new framework for removing thin clouds from visible images based on multiscale dark channel prior (MDCP). The cloud removal of cloudy images (target images) is carried out with the assistance of a different temporal cloudless image (reference image) from the perspective view of multiscale transform (MST). In order to make it more suitable for the application of thin cloud removal, two improvements are made to this traditional fusion method. For one thing, a dark channel prior module is integrated into the low-frequency component of the target image in the framework of MST. For another, we choose the weighted average for high-frequency components and sparse representation (SR) for low-frequency components as fusion rules. After the fusion process, the modified Laplacian sharpening whose model is optimized is carried out. The performance of MDCP was evaluated with both simulated and real cloudy images. Experimental results show that the proposed MDCP has a good performance.

The degradation of spaceborne hyperspectral images (HSIs) usually results from various types of noise. In this letter, we propose a 3D hybrid higher degree total variation regularized nonconvex local low-rank tensor recovery (H2DTV-NLRTR) model to restore the HSIs. Inspired by the good performance of the higher degree total variation penalty in image denoising, we first develop a 3D hybrid higher degree total variation penalty term, which is able to capture the fine image details and edges along the spatial dimensions and spectral dimension. The tensor multi-Schatten-pnorm is chosen as the relaxation of the low-rank tensor constraint, which can not only separate the low-rank clean HSI patches from noisy images effectively but also improve the computational efficiency. The proposed H2DTV-NLRTR model can simultaneously characterize the spectral correlation and the spatial structure of the HSI dataset by incorporating the H2DTV penalty in the nonconvex local low-rank tensor recovery problem. In addition, we adopt a fast iterative majorize-minimize algorithm to efficiently solve the corresponding optimization problem. The numerical experiments on both simulated and real HSI datasets demonstrate that the proposed algorithm provides consistently improved restoration results compared with the state-of-the-art algorithms.

Long-wave infrared (LWIR) images have important applications in retrieving land surface temperature. However, LWIR images are often inevitably suffered from stripe noise, a special type of spatial domain fixed pattern noise. This paper proposes a novel spectral separation algorithm for LWIR image destriping. Specifically, since the mid-wave infrared (MWIR) bands contain both radiant and reflective energy, we use MWIR as reference images to remove the stripe noise in the LWIR bands. We formulate the spectral separation problem as a convex optimization problem, where the difference between LWIR and the radiant component of MWIR, and the difference between the visible and near infrared (VNIR) image and the reflective component of MWIR are regularized to exploit the similarities between the corresponding bands. The obtained radiant component is then utilized to recover the LWIR band. Experimental results using Gaofen-5 datasets demonstrate that the proposed algorithm has good performance in removing the stripe noise.

Remote sensing images contaminated by clouds cannot be used for target recognition, image classification, and other applications, which leads to a lot of remote sensing data being wasted. We propose a low-rank and sparse constrained dark channel prior for cloud removal in remote sensing image sequence (LRSC-DCP). The sparse and low-rank constraints are used to find the position of thick clouds and remove thick clouds in cloud-contaminated images respectively. The remaining thin clouds can be eliminated by the dark channel prior. We compare the algorithm proposed in this article with the dark channel prior. Experimental results prove that the proposed method has better performance.

Hyperspectral image (HSI) classification methods based on deep learning (DL) algorithms have achieved significant improvements on abundant samples. However, due to the limitation of practically available samples, the difficulty of representative feature extraction from small-sized samples and the loss of subtle diagnostic features in DL iteration results in the accuracy reduction of interclass and intraclass in refined classification, respectively. To address these issues, a water retrieval embedded deep network is proposed in this paper. The relative water content retrieval (RWCR) of the proposed network is embedded as a subnet, which is responsible for extracting subtle diagnostic features of relative water content (RWC) to enhance the representation of features in classification. The experimental results verify the effectiveness of RWCR for improving the interclass and intraclass accuracy in refined classification. Moreover, the superiority of the proposed network is also demonstrated in comparison with state-of-the-art methods.

The cloud-contaminated phenomenon in the field of remote sensing has a serious impact on image processing so that a large number of images are unusable. To achieve cloud removal for single visible image, we propose a novel method based on modified dark channel prior with multiple scale (MDCPMS). In the structure of multiple scale, the cloudy image is firstly decomposed into high-frequency and low-frequency components. The former is uniformly amplified to enhance its weak contour, and the latter is processed by modified dark channel prior (DCP), whose estimation of atmospheric light is optimized for better cloud removal. Finally, the cloud-removed image is obtained through multi-scale reconstruction. Experimental results show that the proposed MDCPMS obtains a significant performance with slightest color distortion and is closest to the corresponding real image, compared with DCP and nonlocal method.

The disadvantage of low resolution and low signal-to-noise ratio (SNR) of thermal infrared (TIR) data makes it difficult to be applied in practice, although it has been studied for decades. In contrast, visible and near infrared (VNIR) and shortwave infrared (SWIR) data have higher resolution and SNR. In this letter, an ensemble extreme learning machine (EELM) is proposed, which is based on the extreme learning machine (ELM), to fuse together VNIR, SWIR, and TIR to improve TIR resolution and SNR. The associated ELM nets are trained with correlated training data which are selected from the random forest method. The temperature estimation cube can be obtained after putting SWIR data and VNIR data into these networks. Finally, the cube structure is adjusted by an ensemble rule to obtain the final temperature estimation results. We test the EELM algorithm on the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) L1B data set and compare it with other scholars' algorithms. The experimental results show that the EELM algorithm can not only improve the resolution of TIR data, but also estimate the subpixel temperature of TIR more accurately.

In remote sensing images, it is usually hard to extract narrow roads with only several pixels width. To address this problem, the original remote sensing images are processed with super-resolution to enlarge the details of the narrow roads by a convolutional neural network method. Then the One-Class Support Vector Machine (OCSVM) classifier is applied after super-resolution for exact extraction of narrow roads. Experiments are conducted on an open dataset of remote sensing images to verify the performance of the new method and the results are compared with the method without image super-resolution. The experimental results demonstrate the validity and superiority of the new method.