Recently, the low-rank and sparse decomposition model (LSDM) has been used for anomaly detection in hyperspectral imagery. The traditional LSDM assumes that component where anomalies noise reside can be modeled by a single distribution which often potentially confuses weak noise. Actually, cannot accurately describe different characteristics. In this article, combination of mixture with background may more characterize complex distribution. A modified LSDM, modeling as Gaussian (MoG), is...

The key to hyperspectral anomaly detection is effectively distinguish anomalies from the background, especially in case that background complex and are weak. Hyperspectral imagery (HSI) as an image-spectrum merging cube data can be intrinsically represented a third-order tensor integrates spectral information spatial information. In this article, prior-based approximation (PTA) proposed for detection, which HSI decomposed into tensor. tensor, low-rank prior incorporated dimension by...

Compared with radar and visible light imaging, infrared imaging has its own unique advantages, in recent years, it become a topic of intense research interest. Robust small-target detection is one the key techniques search tracking (IRST) applications, there no doubt that an investigatory hot spot. In real targets backgrounds usually change quickly very high velocities. addition, rapidly moving sensor platform typically makes motion traces inconsistent. These factors reduce performance...

With the increasing availability of multitemporal hyperspectral imagery, change detection under heterogeneous backgrounds is a challenging task. Due to complexity background features, traditional algorithms in spectral domain cannot effectively detect changed features. A novel method using multiple morphological profiles (MMPs) proposed for make full use spatial information. In designed framework, first, max-tree/min-tree strategy applied extract different attributes images (HSIs), i.e.,...

Infrared small-target detection under heterogeneous background, as a challenging task, plays an important role in many applications. In practice, there are not only bright targets but also dim targets, e.g., rescue aircraft and vehicles the forest fire scene. Considering that most existing infrared methods merely aimed at novel method using multiple morphological profiles (MMP) is proposed, which can detect various types of whose brightness varies greatly. designed feature extraction,...

Existing infrared small-target detection methods tend to perform unsatisfactorily when encountering complex scenes, mainly due the following: 1) image itself has a low signal-to-noise ratio (SNR) and insufficient detailed/texture knowledge; 2) spatial structural information is not fully excavated. To avoid these difficulties, an effective method based on three-order tensor creation Tucker decomposition (TCTD) proposed, which detects targets with various brightness, sizes, intensities. In...

Tensor-based fusion that couples the high spatial resolution of a multispectral image (MSI) to spectral hyperspectral (HSI) is considered. The problem first formulated mathematically as convex optimization tensor trace norm imposing low-rank spatially well spectrally, with an alternating-directions featuring linearization providing solution. Although prior tensor-based approaches typically resort decomposition, proposed algorithm exploits ideas from field completion directly impose property...

Existing infrared (IR) small target detection methods are divided into local priors-based and nonlocal ones. However, due to heterogeneous structures in IR images, using either or information is always suboptimal, which causes performance be unstable unrobust. To solve the issue, a comprehensive method, exploiting both priors, proposed. The proposed method framework includes dual-window contrast (DW-LCM) multiscale-window patch-image (MW-IPI). In first stage, DW-LCM designs compensate for...

The low-rank and sparse decomposition model has been favored by the majority of hyperspectral image anomaly detection personnel, especially robust principal component analysis(RPCA) model, over recent years. However, in RPCA ℓ0 operator minimization is an NP-hard problem, which applicable both items. A general approach to relax ℓ1-norm traditional so as approximately transform it convex optimization field. solution obtained approximation often brings problem excessive punishment inaccuracy....

In recent years, the fusion of hyperspectral and multispectral images in remote sensing image processing still faces challenges, primarily due to their complexity multimodal characteristics. Diffusion models, known for stable training process exceptional generation capabilities, have shown good application potential this field. However, when dealing with data, it may prove challenging models fully capture intricate relationships between modalities, which result incomplete information...

Change detection (CD) in remote sensing images is a critical task that has achieved significant success by deep learning. Current networks often employ pixel-based differencing, proportion, classification-based, or feature concatenation methods to represent changes of interest. However, these fail effectively detect the desired changes, as they are highly sensitive factors such atmospheric conditions, lighting variations, and phenological resulting errors. Inspired Transformer structure, we...

Visual Place Recognition (VPR) is an important component in both computer vision and robotics applications, thanks to its ability determine whether a place has been visited where specifically. A major challenge VPR handle changes of environmental conditions including weather, season illumination. Most methods try improve the recognition performance by ignoring factors, leading decreased accuracy decreases when change significantly, such as day versus night. To this end, we propose end-to-end...

Abstract Polyethylene terephthalate (PET) fabric has been widely used in textile industry for decades. However, it faces poor flame retardant performance and dripping problem during burning. In our study, a novel intumescent coating containing bio‐materials ammonium phytate (APA) cyclodextrin (CD) had constructed on PET fabrics surface via pad‐dry‐cure method. The fire performance, mechanical properties bio‐safety of APA/CD coated were evaluated. with 19%APA/6%CD showed limiting oxygen index...

To verify the performance of high-resolution fully polarimetric synthetic aperture radar (SAR) sensor carried by Xinzhou 60 remote-sensing aircraft, we used corner reflectors to calibrate acquired data. The target mechanism in SAR images is more complex than it low-resolution images, impact point pointing error on calibration results obvious, and echo signal easily affected speckle noise; thus, accurate extraction position response energy required. solve this problem, paper introduces image...

The illegal misuse of non-cooperative UAVs poses huge threats to society and life safety. Infrared imaging is reliable monitor unmanned aerial vehicles (UAVs) the anti-UAVs technology via infrared images has attracted more attention. In order provide sufficient time for follow-up, are acquired at long distances, usually exhibiting features weak small. Furthermore, with low signal-to-clutter ratio (SCR). These factors make correct detection a challenge. Existing methods do not fully exploit...

Place recognition is one of the major challenges for LiDAR-based effective localization and mapping task. Traditional methods are usually relying on geometry matching to achieve place recognition, where a global map need be restored. In this paper, we accomplish task based an end-to-end feature learning framework with LiDAR inputs. This method consists two core modules, dynamic octree module that generates local 2D maps consideration robot's motion; unsupervised which improved adversarial...