Learning Disentangled Priors for Hyperspectral Anomaly Detection: A Coupling Model-Driven and Data-Driven Paradigm.

Accurately distinguishing between background and anomalous objects within hyperspectral images poses a significant challenge. The primary obstacle lies in the inadequate modeling of prior knowledge, leading to a performance bottleneck in hyperspectral anomaly detection (HAD). In response to this challenge, we put forth a groundbreaking coupling paradigm that combines model-driven low-rank representation (LRR) methods with data-driven deep learning techniques by learning disentangled priors (LDP).

SpectralGPT: Spectral Remote Sensing Foundation Model.

The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner. While most foundation models are tailored to effectively process RGB images for various visual tasks, there is a noticeable gap in research focused on spectral data, which offers valuable information for scene understanding, especially in remote sensing (RS) applications. To fill this gap, we created for the first time a universal RS foundation model, named SpectralGPT, which is purpose-built to handle spectral RS images using a novel 3D generative pretrained transformer (GPT).

A General Spatial-Frequency Learning Framework for Multimodal Image Fusion.

multimodal image fusion involves tasks like pan-sharpening and depth super-resolution. Both tasks aim to generate high-resolution target images by fusing the complementary information from the texture-rich guidance and low-resolution target counterparts. They are inborn with reconstructing high-frequency information.

Weakly Supervised Solar Panel Mapping via Uncertainty Adjusted Label Transition in Aerial Images.

This paper proposes a novel uncertainty-adjusted label transition (UALT) method for weakly supervised solar panel mapping (WS-SPM) in aerial Images. In weakly supervised learning (WSL), the noisy nature of pseudo labels (PLs) often leads to poor model performance. To address this problem, we formulate the task as a label-noise learning problem and build a statistically consistent mapping model by estimating the instance-dependent transition matrix (IDTM).

Feature Pyramid Fusion Network for Hyperspectral Pansharpening.

Hyperspectral (HS) pansharpening aims at fusing an observed HS image with a panchromatic (PAN) image, to produce an image with the high spectral resolution of the former and the high spatial resolution of the latter. Most of the existing convolutional neural networks (CNNs)-based pansharpening methods reconstruct the desired high-resolution image from the encoded low-resolution (LR) representation. However, the encoded LR representation captures semantic information of the image and is inadequate in reconstructing fine details.

Bayesian Nonlocal Patch Tensor Factorization for Hyperspectral Image Super-Resolution.

The synthesis of high-resolution (HR) hyperspectral image (HSI) by fusing a low-resolution HSI with a corresponding HR multispectral image has emerged as a prevalent HSI super-resolution (HSR) scheme. Recent researches have revealed that tensor analysis is an emerging tool for HSR. However, most off-the-shelf tensor-based HSR algorithms tend to encounter challenges in rank determination and modeling capacity.

A 10-m resolution impervious surface area map for the greater Mekong subregion from remote sensing images.

High-resolution and multi-temporal impervious surface area maps are crucial for capturing rapidly developing urbanization patterns. However, the currently available relevant maps for the greater Mekong subregion suffer from coarse resolution and low accuracy. Addressing this issue, our study focuses on the development of accurate impervious surface area maps at 10-m resolution for this region for the period 2016-2022.

Hyperspectral Image Classification With Multi-Attention Transformer and Adaptive Superpixel Segmentation-Based Active Learning.

Deep learning (DL) based methods represented by convolutional neural networks (CNNs) are widely used in hyperspectral image classification (HSIC). Some of these methods have strong ability to extract local information, but the extraction of long-range features is slightly inefficient, while others are just the opposite. For example, limited by the receptive fields, CNN is difficult to capture the contextual spectral-spatial features from a long-range spectral-spatial relationship.

Noise Prior Knowledge Informed Bayesian Inference Network for Hyperspectral Super-Resolution.

Well-known deep learning (DL) is widely used in fusion based hyperspectral image super-resolution (HS-SR). However, DL-based HS-SR models have been designed mostly using off-the-shelf components from current deep learning toolkits, which lead to two inherent challenges: i) they have largely ignored the prior information contained in the observed images, which may cause the output of the network to deviate from the general prior configuration; ii) they are not specifically designed for HS-SR, making it hard to intuitively understand its implementation mechanism and therefore uninterpretable. In this paper, we propose a noise prior knowledge informed Bayesian inference network for HS-SR.

Dual-Stage Approach Toward Hyperspectral Image Super-Resolution.

Hyperspectral image produces high spectral resolution at the sacrifice of spatial resolution. Without reducing the spectral resolution, improving the resolution in the spatial domain is a very challenging problem. Motivated by the discovery that hyperspectral image exhibits high similarity between adjacent bands in a large spectral range, in this paper, we explore a new structure for hyperspectral image super-resolution (DualSR), leading to a dual-stage design, i.

Semi-Supervised Multiscale Dynamic Graph Convolution Network for Hyperspectral Image Classification.

In recent years, convolutional neural networks (CNNs)-based methods achieve cracking performance on hyperspectral image (HSI) classification tasks, due to its hierarchical structure and strong nonlinear fitting capacity. Most of them, however, are supervised approaches that need a large number of labeled data to train them. Conventional convolution kernels are fixed shape of rectangular with fixed sizes, which are good at capturing short-range relations between pixels within HSIs but ignore the long-range context within HSIs, limiting their performance.

Graph-in-Graph Convolutional Network for Hyperspectral Image Classification.

With the development of hyperspectral sensors, accessible hyperspectral images (HSIs) are increasing, and pixel-oriented classification has attracted much attention. Recently, graph convolutional networks (GCNs) have been proposed to process graph-structured data in non-Euclidean domains and have been employed in HSI classification. But most methods based on GCN are hard to sufficiently exploit information of ground objects due to feature aggregation.

SpaSSA: Superpixelwise Adaptive SSA for Unsupervised Spatial-Spectral Feature Extraction in Hyperspectral Image.

Singular spectral analysis (SSA) has recently been successfully applied to feature extraction in hyperspectral image (HSI), including conventional (1-D) SSA in spectral domain and 2-D SSA in spatial domain. However, there are some drawbacks, such as sensitivity to the window size, high computational complexity under a large window, and failing to extract joint spectral-spatial features. To tackle these issues, in this article, we propose superpixelwise adaptive SSA (SpaSSA), that is superpixelwise adaptive SSA for exploiting local spatial information of HSI.

Self-spectral learning with GAN based spectral-spatial target detection for hyperspectral image.

To alleviate the shortcomings of target detection in only one aspect and reduce redundant information among adjacent bands, we propose a spectral-spatial target detection (SSTD) framework in deep latent space based on self-spectral learning (SSL) with a spectral generative adversarial network (GAN). The concept of SSL is introduced into hyperspectral feature extraction in an unsupervised fashion with the purpose of background suppression and target saliency. In particular, a novel structure-to-structure selection rule that takes full account of the structure, contrast, and luminance similarity is established to interpret the mapping relationship between the latent spectral feature space and the original spectral band space, to generate the optimal spectral band subset without any prior knowledge.

Deep learning based regression for optically inactive inland water quality parameter estimation using airborne hyperspectral imagery.

Airborne hyperspectral remote sensing has the characteristics of high spatial and spectral resolutions, and provides an opportunity for accurate and efficient inland water qauality monitoring. Many studies have focused on evaluating and quantifying the concentrations of the optically active water quality parameters, for parameters such as chlorophyll-a (Chla), cyanobacteria, and colored dissolved organic matter (CDOM). For the optically inactive parameters, such as the permanganate index (COD), total nitrogen (TN), total phosphorus (TP), ammoniacal nitrogen (NH3-N), and heavy metals, it is difficult to estimate the concentrations directly, and the traditional indirect estimation models cannot meet the accuracy requirements, especially in heavily polluted inland waters.

Moving Vehicle Detection for Remote Sensing Video Surveillance With Nonstationary Satellite Platform.

With satellite platforms gazing at a target territory, the captured satellite videos exhibit local misalignment and local intensity variation on some stationary objects that can be mistakenly extracted as moving objects and increase false alarm rates. Typical approaches for mitigating the effect of moving cameras in moving object detection (MOD) follow domain transformation technique, where the misalignment between consecutive frames is restricted to the image planar. However, such technique cannot properly handle satellite videos, as the local misalignment on them is caused by the varying projections from the 3D objects on the Earth's surface to 2D image planar.

Unsupervised spectral mapping and feature selection for hyperspectral anomaly detection.

Exploring techniques that breakthrough the unknown space or material species is of considerable significance to military and civilian fields, and it is a challenging task without any prior information. Nowadays, the use of material-specific spectral information to detect unknowns has received increasing interest. However, affected by noise and interference, high-dimensional hyperspectral anomaly detection is difficult to meet the requirements of high detection accuracy and low false alarm rate.

Spectral constraint adversarial autoencoders approach to feature representation in hyperspectral anomaly detection.

Anomaly detection in hyperspectral images (HSIs) faces various levels of difficulty due to the high dimensionality, redundant information and deteriorated bands. To address these problems, we propose a novel unsupervised feature representation approach by incorporating a spectral constraint strategy into adversarial autoencoders (AAE) without any prior knowledge in this paper. Our approach, called SC_AAE (spectral constraint AAE), is based on the characteristics of HSIs to obtain better discrimination represented by hidden nodes.

Incorporating Negative Sample Training for Ship Detection Based on Deep Learning.

While ship detection using high-resolution optical satellite images plays an important role in various civilian fields-including maritime traffic survey and maritime rescue-it is a difficult task due to influences of the complex background, especially when ships are near to land. In current literatures, land masking is generally required before ship detection to avoid many false alarms on land. However, sea⁻land segmentation not only has the risk of segmentation errors, but also requires expertise to adjust parameters.

Effective Sequential Classifier Training for SVM-Based Multitemporal Remote Sensing Image Classification.

The explosive availability of remote sensing images has challenged supervised classification algorithms such as Support Vector Machines (SVM), as training samples tend to be highly limited due to the expensive and laborious task of ground truthing. The temporal correlation and spectral similarity between multitemporal images have opened up an opportunity to alleviate this problem. In this study, a SVM-based Sequential Classifier Training (SCT-SVM) approach is proposed for multitemporal remote sensing image classification.

Mild volume acute normovolemic hemodilution is associated with lower intraoperative transfusion and postoperative pulmonary infection in patients undergoing cardiac surgery -- a retrospective, propensity matching study.

Background: Perioperative allogenic transfusion is required in almost 50% of patients undergoing cardiac surgery and is associated with higher risk of mortality and morbidity (Xue et al., Lancet 387:1905, 2016; Ferraris et al., Ann Thorac Surg 91:944-82, 2011).

A Dynamic Neighborhood Learning-Based Gravitational Search Algorithm.

Balancing exploration and exploitation according to evolutionary states is crucial to meta-heuristic search (M-HS) algorithms. Owing to its simplicity in theory and effectiveness in global optimization, gravitational search algorithm (GSA) has attracted increasing attention in recent years. However, the tradeoff between exploration and exploitation in GSA is achieved mainly by adjusting the size of an archive, named , which stores those superior agents after fitness sorting in each iteration.

A multistaged automatic restoration of noisy microscopy cell images.

Automated cell segmentation for microscopy cell images has recently become an initial step for further image analysis in cell biology. However, microscopy cell images are easily degraded by noise during the readout procedure via optical-electronic imaging systems. Such noise degradations result in low signal-to-noise ratio (SNR) and poor image quality for cell identification.

[Correlation study between ischemic stroke and polymorphism of human leucocyte antigen gene].

Objective: To explore the correlation between ischemic stroke (IS) and the polymorphism of human leucocyte antigen (HLA) gene.

Methods: Antigen, allele, haplotype of HLA-A, B, C, DRB1, DQB1 in 94 IS patients and 503 healthy controls were detected by PCR-SBT.

Results: (1) There were 11 antigens, 17 alleles in HLA-A locus, 20 antigens, 34 alleles in HLA-B locus, 11 antigens, 16 alleles in HLA-C locus, 13 antigens, 26 alleles in HLA-DRB1 locus, 5 antigens, 13 alleles in HLA- DQB1 locus in IS group.