Visual object trackers based on Siamese networks perform well in visual object tracking (VOT); however, degradation of the tracking accuracy occurs when the target has fast motion, large-scale changes, and occlusion. In this study, in order to solve...
Improving sensitivity in short-wave infrared (SWIR) detection is crucial for low-signal applications, such as astronomy and hyperspectral imaging, which demand readout integrated circuits (ROICs) with minimal noise and high density. However, conventi...
Determining vegetation variation and its influential factors in a desert steppe under the impacts of climate change and human activities is crucial and meaningful for improving the understanding of desertification and taking targeted measures in ecol...
Convolutional neural networks (CNNs) and graph convolutional networks (GCNs) have led to promising advancements in hyperspectral image (HSI) classification; however, traditional CNNs with fixed square convolution kernels are insufficiently flexible t...
Images, as carriers of rich information, are generated, stored, and transmitted in various forms across diverse scenarios. It has become an important issue in the field of information security today to encrypt images to ensure information security. T...
Image-level weakly supervised semantic segmentation is a challenging problem in computer vision and has gained a lot of attention in recent years. Most existing models utilize class activation mapping (CAM) to generate initial pseudo-labels for each...
Digital image correlation techniques are well known for motion extraction from video images. Following a two-stage approach, the pixel-level displacement is first estimated by maximizing the cross-correlation between two images, then the estimation i...
Recently, the deep learning technology has been adopted in the study of traditional village landscape. More precisely, it’s usually used to explore the representation of cultural heritage and the diversity of heritage information. In this study...
The AVHRR aerosol optical depth (AOD) is inverted from measured reflectances in the red band using a statistical correlation of surface reflectance with mid-infrared channel reflectances and a modelling climatology of the aerosol type. For such a sen...
To ensure the security of digital images during transmission and storage, an efficient and secure chaos-based color image encryption scheme using bit-level permutation is proposed. Our proposed image encryption algorithm belongs to symmetric cryptogr...
Confusion, diffusion, and encryption keys affect the quality of image encryption. This research proposes combining bit- and pixel-level permutation and substitution methods based on three advanced chaotic logistic map methods. The three chaotic metho...
Forest edge effects are commonly interpreted as radial gradients from the edge toward the interior, but this assumption may oversimplify the spatial organization of heterogeneous tropical forest fragments. Here, we integrated field-based phytosociolo...
Rapid estimation of above-ground biomass (AGB) with high accuracy is essential for monitoring crop growth status and predicting crop yield. Recently, remote sensing techniques using unmanned aerial systems (UASs) have exhibited great potential in obt...
Accurate data on gross domestic product (GDP) at pixel level are needed to understand the dynamics of regional economies. GDP spatialization is the basis of quantitative analysis on economic diversities of different administrative divisions and areas...
In this paper, we propose a new approach for sub-pixel co-registration based on Fourier phase correlation combined with the Harris detector. Due to the limitation of the standard phase correlation method to achieve only pixel-level accuracy, another...
The deep image prior (DIP) suggests that it is possible to train a randomly initialized network with a suitable architecture to solve inverse imaging problems by simply optimizing its parameters to reconstruct a single degraded image. However, the pr...
Decision fusion plays a crucial role in achieving a cohesive and unified outcome by merging diverse perspectives. Within the realm of remote sensing classification, these methodologies become indispensable when synthesizing data from multiple sensors...
The goal of this paper was to investigate poverty and inequities that are associated with vegetation. First, we performed a pixel-level linear regression on time-series and Normalized Difference Vegetation Index (NDVI) for 72 United States (U.S.) cit...
Global burned area (BA) datasets from satellite Earth observations provide information for carbon emission and for Dynamic Global Vegetation Model (DGVM) benchmarking. Fire patch identification from pixel-level information recently emerged as an addi...
The Three-River Source Region (TRSR) is an environmentally fragile area in China and on the Qinghai–Tibet Plateau that is extremely vulnerable to climate change. The time lag between climate change and vegetation responses in high-altitude regi...
Clouds are core components of the global climate system and hydrological cycle, and high-precision cloud detection is a key prerequisite for quantitative optical remote sensing applications. Traditional threshold methods often miss thin clouds and mi...
Spatializing crop yield statistical data with administrative divisions as the basic unit helps reveal the spatial distribution characteristics of crop yield and provides necessary spatial information to support field management and government decisio...
The advent of quantum computers could enable the resolution of complex computational problems that conventional cryptographic protocols find challenging. As a result, the formidable computing capabilities of quantum computers may render all present-d...
In encryption technology, image scrambling is a common processing operation. This paper proposes a quantum version of the 3D Mobius scrambling transform based on the QRCI model, which changes not only the position of pixels but also the gray values....
Mars topographic data, such as digital orthophoto maps (DOMs) and digital elevation models (DEMs) are essential to planetary science and exploration missions. The main objective of our study is to generate a higher resolution DEM using the Mars Expre...
Changes in the dynamics of glaciers must be assessed, as they are important for sea level changes. Glacier velocity is the most important parameter used in glacier dynamics studies. Various image matching techniques, which are implemented in differen...
In recent years, vision–language models (VLMs) have been introduced into remote sensing semantic segmentation to provide richer semantic representations through visual–textual alignment. However, most existing VLM-based segmentation metho...
Shallow water bathymetry data underpins maritime shipping and marine resource survey/protection, but its accuracy is constrained by water heterogeneity and spectral interference. To address this, this study proposes a Spatio-Temporal Coupling and Cor...
Hyperspectral and multispectral image fusion requires enhancing spatial details while preserving the pixel-wise spectral fidelity of hyperspectral observations. Transformer-based approaches provide effective contextual modeling, yet hierarchical toke...
Hundreds of image encryption schemes have been conducted (as the literature review indicates). The majority of these schemes use pixels as building blocks for confusion and diffusion operations. Pixel-level operations are time-consuming and, thus, no...
The Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS/DNB) nighttime light data is a powerful remote sensing data source. However, due to stray light pollution, there is a lack of VIIRS data in mid-high latitudes during the summer, resu...
Hyperspectral imaging (HSI) is an important imaging modality in the field of optical remote sensing, capable of simultaneously capturing spatial structures and continuous spectral responses related to material properties. Therefore, compared with con...
Moving cast shadows of moving objects significantly degrade the performance of many high-level computer vision applications such as object tracking, object classification, behavior recognition and scene interpretation. Because they possess similar mo...
We propose a deep-learning algorithm that directly compensates for luminance degradation because of the deterioration of organic light-emitting diode (OLED) devices to address the burn-in phenomenon of OLED displays. Conventional compensation circuit...
This paper skillfully incorporates the memristor model into a chaotic system, creating a two-dimensional (2D) hyperchaotic map. The system’s exceptional chaotic performance is verified through methods such as phase diagrams, bifurcation diagram...
Producing high-quality local land cover data can be cost-prohibitive, leaving gaps in reliable estimates of forest cover and loss for environmental policy and planning. Remote sensing data (RSD) offer accessible, globally consistent layers for forest...
Spaceborne detection of solar-induced chlorophyll fluorescence (SIF) requires extremely high radiometric accuracy, and the polarization characteristics of an ultra-wide swath spaceborne fluorescence ultraspectral camera directly affect the accuracy o...
Pixelwise dense representations are more prevalent in the field of iris recognition, also known as iris templates or IrisCodes. Almost all previous works of this kind are deterministic. To be specific, pixel-level representations are exclusively deri...
Wetlands play a vital role in regulating the global carbon cycle, providing biodiversity, and reducing flood risks. These functions maintain ecological balance and ensure human well-being. Timely, accurate monitoring of wetlands is essential, not onl...
Power-system reliability is crucial for sustainable development, but large-scale, long-term monitoring remains challenging. Existing nighttime light (NTL)-based outage detection methods often rely on fixed thresholds or prior information, limiting cr...
In order to increase the security and robustness of quantum images, this study combined the quantum DNA codec with quantum Hilbert scrambling to offer an enhanced quantum image encryption technique. Initially, to accomplish pixel-level diffusion and...
For binocular stereo matching techniques, the most advanced method currently is using an iterative structure based on GRUs. Methods in this class have shown high performance on both high-resolution images and standard benchmarks. However, simply repl...
The accurate mapping of impervious surfaces is of key significance for various urban applications. Usually, traditional methods extract the proportion image of impervious surfaces from remote sensing images; however, the proportion image cannot speci...
Accurate estimates of growth and structural changes are key for forest management tasks such as determination of optimal rotation times, optimal rotation times, site indices and for identifying areas experiencing difficulties to regenerate. Estimatio...
Efficient and robust evaluation of kernel processing from corn silage is an important indicator to a farmer to determine the quality of their harvested crop. Current methods are cumbersome to conduct and take between hours to days. We present the ado...
Disentangling the impacts of ecological restoration from climate change is an ongoing challenge in remote sensing since the traditional correlative approaches often cannot elucidate causal mechanisms. To overcome this, we introduce a Causal Remote Se...
The presence of pests is one of the main problems in crop production, and obtaining reliable statistics of pest infestation is essential for pest management. Detection of pests should be automated because human monitoring of pests is time-consuming a...
Cloud classification is fundamental to advancing climate research and improving weather forecasting. However, existing cloud classification models are constrained by several limitations. For instance, simple statistical methods depend heavily on prio...
Winter wheat is one of the major crops for global food security. Accurate statistics of its planting area play a crucial role in agricultural policy formulation and resource management. However, the existing semantic segmentation methods for remote s...
This study investigates the spatial relationship between population distribution and tree cover loss in Nepal from 2000 to 2020, using satellite-based forest cover and population data along with statistical and geospatial analysis. Two statistical me...
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