The lunar crater recognition plays a key role in lunar exploration. Traditional crater recognition methods are mainly based on the human observation that is usually combined with classical machine learning methods. These methods have some drawbacks,...
Individual tree species (ITS) classification is one of the key issues in forest resource management. Compared with traditional classification methods, deep learning networks may yield ITS classification results with higher accuracy. In this research,...
Detecting and localizing buildings is of primary importance in urban planning tasks. Automating the building extraction process, however, has become attractive given the dominance of Convolutional Neural Networks (CNNs) in image classification tasks....
R2U-Net, or Recurrent Residual U-Net, is a U-Net extension that includes both residual and recurrent connections for image segmentation tasks. R2U-Net is an image segmentation task-focused network that mixes residual and recurrent connections to boos...
Road segmentation is beneficial to build a vision-controllable mission-oriented self-driving bot, e.g., the Self-Driving Sweeping Bot, or SDSB, for working in restricted areas. Using road segmentation, the bot itself and physical facilities may be pr...
Automatic segmentation of brain tumors from magnetic resonance imaging (MRI) is a challenging task due to the uneven, irregular and unstructured size and shape of tumors. Recently, brain tumor segmentation methods based on the symmetric U-Net archite...
Background/Objectives: Accurate fetal head biometry, obtained via ultrasound imaging, is fundamental to prenatal assessment, as it enables fetal growth to be monitored and developmental abnormalities to be detected early. However, conventional manual...
To solve the problem of the low segmentation accuracy of lung nodule CT images using U-Net, an improved method for segmentation of lung nodules by U-Net was proposed. Initially, the dense network connection and sawtooth expanded convolution design wa...
This study aimed to remove motion artifacts from brain magnetic resonance (MR) images using a U-Net model. In addition, a simulation method was proposed to increase the size of the dataset required to train the U-Net model while avoiding the overfitt...
In reconstructive surgery, flaps are the cornerstone for repairing tissue defects, but postoperative monitoring of their viability remains a challenge. Among the imagistic techniques for monitoring flaps, the thermal camera has demonstrated its value...
The pigments of cultural color paintings have faded with the passage of time. Color segmentations are essential for digital color reconstruction, but the complexity of color paintings makes it challenging to achieve high-precision segmentation using...
Crop disease leaf image segmentation (CDLIS) is the premise of disease detection, disease type recognition and disease degree evaluation. Various convolutional neural networks (CNN) and their modified models have been provided for CDLIS, but their tr...
Background/Objectives: Accurate segmentation of brain tumors in Magnetic Resonance Imaging (MRI) scans is critical for diagnosis and treatment planning due to their life-threatening nature. This study aims to develop a robust and automated method cap...
At present, it is challenging to extract landslides from high-resolution remote-sensing images using deep learning. Because landslides are very complex, the accuracy of traditional extraction methods is low. To improve the efficiency and accuracy of...
At present, Synthetic Aperture Radar Interferometry (InSAR) has been an important technique for active landslides recognition in the geological survey field. However, the traditional interpretation method through human–computer interaction high...
The semantic segmentation of fine-resolution remotely sensed images is an urgent issue in satellite image processing. Solving this problem can help overcome various obstacles in urban planning, land cover classification, and environmental protection,...
The main objective of the present study was to develop an integrated approach combining remote sensing techniques and U-Net-based deep learning models for lithology mapping. The methodology incorporates Landsat 8 imagery, ALOS PALSAR data, and field...
In a colonoscopy, accurate computer-aided polyp detection and segmentation can help endoscopists to remove abnormal tissue. This reduces the chance of polyps developing into cancer, which is of great importance. In this paper, we propose a neural net...
To address severe noise interference in high-temperature H2O absorption spectra in metallurgical environments and the limited performance of traditional filtering algorithms, this study proposes an enhanced U-Net neural-network filtering method based...
Urban building segmentation is a prevalent research domain for very high resolution (VHR) remote sensing; however, various appearances and complicated background of VHR remote sensing imagery make accurate semantic segmentation of urban buildings a c...
Background/Objective: This study aims to utilize advanced artificial intelligence (AI) image recog-nition technologies to establish a robust system for identifying features in lung computed tomog-raphy (CT) scans, thereby detecting respiratory infect...
In recent years, deep learning technology for clinical diagnosis has progressed considerably, and the value of medical imaging continues to increase. In the past, clinicians evaluated medical images according to their individual expertise. In contras...
Path planning in cluttered environments constitutes a critical challenge for mobile robotics. Although optimal solutions can be obtained by classical methods such as A*, they have the disadvantage of being computationally expensive in complex environ...
As one of the representative models in the field of image generation, generative adversarial networks (GANs) face a significant challenge: how to make the best trade-off between the quality of generated images and training stability. The U-Net based...
COVID-19 patients require effective diagnostic methods, which are currently in short supply. In this study, we explained how to accurately identify the lung regions on the X-ray scans of such people’s lungs. Images from X-rays or CT scans are c...
Image dehazing, a fundamental problem in computer vision, involves the recovery of clear visual cues from images marred by haze. Over recent years, deploying deep learning paradigms has spurred significant strides in image dehazing tasks. However, ma...
This paper focuses on the problems of omission, misclassification, and inter-adhesion due to overly dense distribution, intraclass diversity, and interclass variability when extracting winter wheat (WW) from high-resolution images. This paper propose...
To address the problem of waveform distortion in the existing seismic signal denoising method when removing co-band noise, further improving the signal-to-noise ratio (SNR) of seismic signals and enhancing their quality, this paper designs a seismic...
Retinal blood vessel segmentation is a valuable tool for clinicians to diagnose conditions such as atherosclerosis, glaucoma, and age-related macular degeneration. This paper presents a new framework for segmenting blood vessels in retinal images. Th...
Black soil is one of the most productive soils with high organic matter content. Crop residue covering is important for protecting black soil from alleviating soil erosion and increasing soil organic carbon. Mapping crop residue covered areas accurat...
Traditional deep learning algorithms struggle to effectively utilize local spectral info in forest HS images and adequately capture subtle feature differences, often causing model confusion and misclassification. To tackle these issues, we present SF...
Tree species classification is important for the management and sustainable development of forest resources. Traditional object-oriented tree species classification methods, such as support vector machines, require manual feature selection and genera...
Accurate segmentation of retinal blood vessels is a key step in the diagnosis of fundus diseases, among which cataracts, glaucoma, and diabetic retinopathy (DR) are the main diseases that cause blindness. Most segmentation methods based on deep convo...
Brain tumors are a type of disease that affects people’s health and have received extensive attention. Accurate segmentation of Magnetic Resonance Imaging (MRI) images for brain tumors is essential for effective treatment strategies. However, t...
The advanced development of deep learning methods has recently made significant improvements in medical image segmentation. Encoder–decoder networks, such as U-Net, have addressed some of the challenges in medical image segmentation with an out...
Vessel segmentation is a fundamental task in medical image analysis and plays an important role in disease diagnosis and treatment assessment. However, existing segmentation methods often show limited adaptability to feature extraction from single-ch...
The selection and representation of classification features in remote sensing image play crucial roles in image classification accuracy. To effectively improve the features classification accuracy, an improved U-Net remote sensing classification algo...
Accurate paranasal sinus segmentation is essential for reducing surgical complications through surgical guidance systems. This study introduces a multiclass Convolutional Neural Network (CNN) segmentation model by comparing four 3D U-Net variations&m...
Colorectal Cancer is one of the most common cancers found in human beings, and polyps are the predecessor of this cancer. Accurate Computer-Aided polyp detection and segmentation system can help endoscopists to detect abnormal tissues and polyps duri...
Change detection (CD) is a particularly important task in the field of remote sensing image processing. It is of practical importance for people when making decisions about transitional situations on the Earth’s surface. The existing CD methods...
As the acquisition of very high resolution (VHR) images becomes easier, the complex characteristics of VHR images pose new challenges to traditional machine learning semantic segmentation methods. As an excellent convolutional neural network (CNN) st...
This study investigates the generation of synthetic CT (sCT) images from zero echo time (ZTE) MRI to support MR-only radiotherapy, which can reduce image registration errors and lower treatment planning costs. Since MRI lacks the electron density dat...
Marine floating raft aquaculture (FRA) monitoring is significant for marine ecological environment and food security assessment. Synthetic aperture radar-based monitoring is considered to be an effective means of FRA identification because of its cap...
Seismic landslides are the most common and highly destructive earthquake-triggered geological hazards. They are large in scale and occur simultaneously in many places. Therefore, obtaining landslide information quickly after an earthquake is the key...
With the rapid development of urbanization in China, monitoring urban changes is of great significance to city management, urban planning, and cadastral map updating. Spaceborne synthetic aperture radar (SAR) sensors can capture a large area of radar...
The black soil region of Northeast China is one of the most fertile soil areas in the world and serves as a crucial grain-producing region in China. However, excessive development and improper utilization have led to severe land use issues. Conductin...
Accurate delineation of individual agricultural plots, the foundational units for agriculture-based activities, is crucial for effective government oversight of agricultural productivity and land utilization. To improve the accuracy of plot segmentat...
Green apples exhibit a coloration that closely matches their surrounding environment, leading to low recognition accuracy for existing artificial intelligence models. This paper presents a green apple recognition algorithm that integrates an improved...
Chronic kidney disease–mineral and bone disorder (CKD-MBD) frequently occurs in hemodialysis patients and is a common cause of osteoporosis. Regular dual-energy X-ray absorptiometry (DXA) scans are used to monitor these patients, but frequent,...
The extraction of sandy roads from remote sensing images is important for field ecological patrols and path planning. Extraction studies on sandy roads face limitations because of various factors (e.g., sandy roads may have poor continuity, may be ob...
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