Multispectral pedestrian detection, which consists of a color stream and thermal stream, is essential under conditions of insufficient illumination because the fusion of the two streams can provide complementary information for detecting pedestrians...
Although the fusion of multispectral (MS) and hyperspectral (HS) images in remote sensing has become relatively mature, and different types of fusion methods have their own characteristics in terms of fusion effect, data dependency, and computational...
Multispectral and hyperspectral image fusion (MS/HS fusion) aims to generate a high-resolution hyperspectral (HRHS) image by fusing a high-resolution multispectral (HRMS) and a low-resolution hyperspectral (LRHS) images. The deep unfolding-based MS/H...
Aerial object detection plays a critical role in numerous fields, utilizing the flexibility of airborne platforms to achieve real-time tasks. Combining visible and infrared sensors can overcome limitations under low-light conditions, enabling full-ti...
Thermal infrared imagery plays an important role in a variety of fields, such as surface temperature inversion and urban heat island effect analysis, but the spatial resolution has severely restricted the potential for further applications. Data fusi...
Abstract: High spatial resolution hyperspectral data often used in precision farming applications are not available from current satellite sensors, and difficult or expensive to acquire from standard aircraft. Alternatively, in precision farming, unm...
In order to reconstruct a high spatial and high spectral resolution image (H2SI), one of the most common methods is to fuse a hyperspectral image (HSI) with a corresponding multispectral image (MSI). To effectively obtain both the spectral correlatio...
Multispectral information fusion technology is a practical approach to enhance pedestrian detection performance in low light conditions. However, current methods often overlook the impact of illumination on modal weights and the significance of inter...
An adequate fusion of the most significant salient information from multiple input channels is essential for many aerial imaging tasks. While multispectral recordings reveal features in various spectral ranges, synthetic aperture sensing makes occlud...
Limited by the imaging capabilities of sensors, research based on single modality is difficult to cope with faults and dynamic perturbations in detection. Effective multispectral object detection, which can achieve better detection accuracy by fusing...
The process of fusing the rich spectral information of a low spatial resolution hyperspectral image (LR-HSI) with the spatial information of a high spatial resolution multispectral image (HR-MSI) to obtain an HSI with the spatial resolution of an MSI...
Compared with multispectral sensors, hyperspectral sensors obtain images with high- spectral resolution at the cost of spatial resolution, which constrains the further and precise application of hyperspectral images. An intelligent idea to obtain hig...
To date, countless satellite image fusions have been made, mainly with panchromatic spatial resolution to a multispectral image ratio of 1/4, fewer fusions with lower ratios, and relatively recently fusions with much higher spatial resolution ratios...
Unmanned Aerial Vehicles (UAVs) are emerging and promising platforms for carrying different types of cameras for remote sensing. The application of multispectral vegetation indices for ground cover classification has been widely adopted and has prove...
Compared with traditional mine detection methods, UAV-based measures are more suitable for the rapid detection of large areas of scatterable landmines, and a multispectral fusion strategy based on a deep learning model is proposed to facilitate mine...
Scanning micrο X-ray fluorescence (μ-XRF) and multispectral imaging (MSI) were applied to study philately stamps, selected for their small size and intricate structures. The μ-XRF measurements were accomplished using the M6 Jetstream Br...
Multispectral detection leverages visible and infrared imaging to improve detection performance in complex environments. However, conventional convolution-based fusion methods predominantly rely on local feature interactions, limiting their capacity...
This article focuses on the image fusion of high-resolution panchromatic and multispectral images. We propose a new image fusion method based on a Hue-Saturation-Value (HSV) color space model and bidimensional empirical mode decomposition (BEMD), b...
For many decades the multispectral images of the earth’s surface and its objects were taken from multispectral sensors placed on satellites. In recent years, the technological evolution produced similar sensors (much smaller in size and weight)...
Despite significant strides in achieving vehicle autonomy, robust perception under low-light conditions still remains a persistent challenge. In this study, we investigate the potential of multispectral imaging, thereby leveraging deep learning model...
Hyperspectral and multispectral remote sensing image fusion is an optimal approach for generating hyperspectral–spatial-resolution images, effectively overcoming the physical limitations of sensors. In transformer-based image fusion methods con...
The fusion of low-resolution hyperspectral images (LR-HSI) with high-resolution multispectral images (HR-MSI) provides a cost-effective approach to obtaining high-resolution hyperspectral images (HR-HSI). Existing methods primarily based on convoluti...
Multispectral object detection is a crucial technology in remote sensing image processing, particularly in low-light environments. Most current methods extract features at a single scale, resulting in the fusion of invalid features and the failure to...
Hyperspectral–multispectral (HSI–MSI) image fusion aims to reconstruct high-spatial-resolution hyperspectral images (HR-HSIs) by combining the spectral fidelity of low-resolution HSIs (LR-HSIs) with the spatial details of high-resolution...
Infrared–visible-oriented object detection aims to combine the strengths of both infrared and visible images, overcoming the limitations of a single imaging modality to achieve more robust detection with oriented bounding boxes under diverse en...
Multispectral object detection faces challenges in effectively integrating complementary information from different modalities in complex environmental conditions. This paper proposes SDRFPT-Net (Spectral Dual-stream Recursive Fusion Perception Targe...
In recent years, the fusion of hyperspectral and multispectral images in remote sensing image processing still faces challenges, primarily due to their complexity and multimodal characteristics. Diffusion models, known for their stable training proce...
Detecting pedestrians in varying illumination conditions poses a significant challenge, necessitating the development of innovative solutions. In response to this, we introduce Prior-AttentionNet, a pedestrian detection model featuring a Prior-Attent...
Synthetic aperture radar (SAR) is an important remote sensing sensor whose application is becoming more and more extensive. Compared with traditional optical sensors, it is not easy to be disturbed by the external environment and has a strong penetra...
Fusing the panchromatic (PAN) image and low spatial-resolution multispectral (LR MS) images is an effective technology for generating high spatial-resolution MS (HR MS) images. Some image-fusion methods inspired by neighbor embedding (NE) are propose...
Hyperspectral–multispectral image fusion reconstructs high-spatial-resolution hyperspectral images (HR-HSIs) by combining low-resolution hyperspectral images (LR-HSIs) with high-resolution multispectral images (HR-MSIs). Many methods only suppo...
In order to overcome the insufficiency of single remote sensing data in change detection, synthetic aperture radar (SAR) and optical image data can be used together for supplementation. However, conventional image fusion methods fail to address the d...
Enhancing the spatial resolution of hyperspectral image (HSI) is of significance for applications. Fusing HSI with a high resolution (HR) multispectral image (MSI) is an important technology for HSI enhancement. Inspired by the success of deep learni...
Reconstructing high-resolution hyperspectral images (HR-HSIs) from low-resolution hyperspectral images (LR-HSIs) and high-resolution multispectral images (HR-MSIs) is an important multimodal remote sensing task for applications requiring both fine sp...
By integrating the thermal characteristics from thermal-infrared remote sensing with the physiological and structural information of vegetation revealed by multispectral remote sensing, a more comprehensive assessment of the crop soil-moisture-status...
In this paper, a detail-injection method based on a coupled convolutional neural network (CNN) is proposed for hyperspectral (HS) and multispectral (MS) image fusion with the goal of enhancing the spatial resolution of HS images. Owing to the excelle...
The paper presents a method for generating a pseudo-panchromatic (PPAN) orthophotomosaic that is spectrally compatible with the multispectral (MS) orthophotomosaic, and it targets the fusion of unmanned aircraft system (UAS) RGB–MS orthophotomo...
Verticillium wilt is characterized by chlorosis in leaves and is a devastating disease in eggplant. Early diagnosis, prior to the manifestation of symptoms, enables targeted management of the disease. In this study, we aim to detect early leaf wilt i...
ZY-1 02D is China’s first civil hyperspectral (HS) operational satellite, developed independently and successfully launched in 2019. It can collect HS data with a spatial resolution of 30 m, 166 spectral bands, a spectral range of 400~2500 nm, and a...
As the role of remote sensing continues to grow, the fusion technology of low-spatial-resolution hyperspectral images and high-spatial-resolution multispectral images has become increasingly critical. Traditional methods rely on fixed rules and exhib...
Broccoli is a highly nutritious vegetable that is favored worldwide. Assessing and predicting the shelf life of broccoli holds considerable importance for effective resource optimization and management. The physicochemical parameters and spectral cha...
Multispectral and 3D LiDAR remote sensing data sources are valuable tools for characterizing the 3D vegetation structure and thus understanding the relationship between forest structure, biodiversity, and microclimate. This study focuses on mapping r...
Remotely sensed multi-spectral and -spatial data facilitates the study of mosquito-borne disease vectors and their response to land use and cover composition in the urban environment. In this study we assess the feasibility of integrating remotely se...
Object recognition is conducted using red, green, and blue (RGB) images in object recognition studies. However, RGB images in low-light environments or environments where other objects occlude the target objects cause poor object recognition performa...
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...
Pedestrian detection is a critical task for safety-critical systems, but detecting pedestrians is challenging in low-light and adverse weather conditions. Thermal images can be used to improve robustness by providing complementary information to RGB...
With the decline in the protective function for agricultural ecosystems of farmland shelterbelts due to tree withering and dying caused by pest and disease, quickly and accurately identifying the distribution of canopy damage is of great significance...
Efforts in the domain of building studies involve the use of a diverse array of geomatic sensors, some providing invaluable information in the form of three-dimensional point clouds and associated registered properties. However, managing the vast amo...
Weed segmentation in soybean fields is essential to improving herbicide use efficiency and supporting precision variable-rate spraying. This study developed an SiLU–CPCA–Gate U-Net (SCG-UNet) using fused UAV RGB and multispectral imagery...
Extracting roads accurately from remote sensing images is important for map updates, traffic analysis, and infrastructure monitoring. Medium-resolution multispectral images can provide useful surface and background information, but when used alone, t...
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