Remote sensing (RS) scene classification has received considerable attention due to its wide applications in the RS community. Many methods based on convolutional neural networks (CNNs) have been proposed to classify complex RS scenes, but they canno...
Despite the increasing amount of spaceborne synthetic aperture radar (SAR) images and optical images, only a few annotated data can be used directly for scene classification tasks based on convolution neural networks (CNNs). For this situation, self-...
Remote sensing scene image classification has extensive application scenarios in fields such as land use monitoring and environmental assessment. However, traditional methodologies based on convolutional neural networks (CNNs) face considerable chall...
With the development of remote sensing scene image classification, convolutional neural networks have become the most commonly used method in this field with their powerful feature extraction ability. In order to improve the classification performanc...
With the development of computer vision, attention mechanisms have been widely studied. Although the introduction of an attention module into a network model can help to improve classification performance on remote sensing scene images, the direct in...
Classifying remote sensing images is vital for interpreting image content. Presently, remote sensing image scene classification methods using convolutional neural networks have drawbacks, including excessive parameters and heavy calculation costs. Mo...
The main challenge of scene classification is to understand the semantic context information of high-resolution remote sensing images. Although vision transformer (ViT)-based methods have been explored to boost the long-range dependencies of high-res...
This study aims at improving the efficiency of remote sensing scene classification (RSSC) through lightweight neural networks and to provide a possibility for large-scale, intelligent and real-time computation in performing RSSC for common devices. I...
The large intra-class difference and inter-class similarity of scene images bring great challenges to the research of remote-sensing scene image classification. In recent years, many remote-sensing scene classification methods based on convolutional...
At present, the classification accuracy of high-resolution Remote Sensing Image Scene Classification (RSISC) has reached a quite high level on standard datasets. However, when coming to practical application, the intrinsic noise of satellite sensors...
In recent years, convolution neural networks (CNNs) have been widely used in the field of remote sensing scene image classification. However, CNN models with good classification performance tend to have high complexity, and CNN models with low comple...
For remote sensing scene image classification, many convolution neural networks improve the classification accuracy at the cost of the time and space complexity of the models. This leads to a slow running speed for the model and cannot realize a trad...
Existing remote sensing scene classification (RSSC) models mainly rely on convolutional neural networks (CNNs) to extract high-level features from remote sensing images, while neglecting the importance of low-level features. To address this, we propo...
Object detection is an essential function for mobile robots, allowing them to carry out missions efficiently. In recent years, various deep learning models based on convolutional neural networks have achieved good performance in object detection. How...
In the process of extracting tailings ponds from large scene remote sensing images, semantic segmentation models usually perform calculations on all small-size remote sensing images segmented by the sliding window method. However, some of these small...
In recent years, supervised learning, represented by deep learning, has shown good performance in remote sensing image scene classification with its powerful feature learning ability. However, this method requires large-scale and high-quality handcra...
As the demand for land use monitoring continues to grow, high-precision remote sensing products have become increasingly important. Compared to traditional methods, deep learning networks demonstrate significant advantages in automatic feature extrac...
Domain adaptation is a learning strategy that aims to improve the performance of models in the current field by leveraging similar domain information. In order to analyze the effects of feature disentangling on domain adaptation and evaluate a model&...
In recent years, with the rapid development of computer vision, increasing attention has been paid to remote sensing image scene classification. To improve the classification performance, many studies have increased the depth of convolutional neural...
In indoor environments, reverberation can distort the signalseceived by active noise cancelation devices, posing a challenge to sound classification. Therefore, we combined three speech spectral features based on different frequency scales into a den...
Vision-based mobile robot navigation is a vibrant area of research with numerous algorithms having been developed, the vast majority of which either belong to the scene-oriented simultaneous localization and mapping (SLAM) or fall into the category o...
The development of convolution neural networks (CNNs) has become a significant means to solve the problem of remote sensing scene image classification. However, well-performing CNNs generally have high complexity and are prone to overfitting. To hand...
The representation and utilization of environmental information by service robots has become increasingly challenging. In order to solve the problems that the service robot platform has, such as high timeliness requirements for indoor environment rec...
Real-time acquisition and intelligent classification of pole-like street-object point clouds are of great significance in the construction of smart cities. Efficient point cloud processing technology in road scenes can accelerate the development of i...
Compound eyes, also known as insect eyes, have a unique structure. They have a hemispheric surface, and a lot of single eyes are deployed regularly on the surface. Thanks to this unique form, using the compound images has several advantages, such as...
Automatic classification of buildings within port areas from synthetic aperture radar (SAR) images is crucial for effective port monitoring and planning. Yet, the unique challenges of SAR imaging, such as side-looking geometry, multi-bouncing scatter...
This study proposes a simple convolutional neural network (CNN)-based model for vehicle classification in low resolution surveillance images collected by a standard security camera installed distant from a traffic scene. In order to evaluate its effe...
In object detection, targets in adverse and complex scenes often have limited information and pose challenges for feature extraction. To address this, we designed a lightweight feature extraction network based on the Convolutional Block Attention Mod...
In hyperspectral image (HSI) classification, feature learning and label accuracy play a crucial role. In actual hyperspectral scenes, however, noisy labels are unavoidable and seriously impact the performance of methods. While deep learning has achie...
Compared with ordinary image classification tasks, fine-grained image classification is closer to real-life scenes. Its key point is how to find the local areas with sufficient discrimination and perform effective feature learning. Based on a bilinea...
This study presents a unified low-parameter approach to multi-class classification of microorganisms (micrococci, diplococci, streptococci, and bacilli) based on automated machine learning. The method is designed to produce interpretable taxonomic de...
Forest is an important resource for human survival, and forest fires are a serious threat to forest protection. Therefore, the early detection of fire and smoke is particularly important. Based on the manually set feature extraction method, the detec...
The sophistication of ship detection technology in remote sensing images is insufficient, the detection results differ substantially from the practical requirements, mainly reflected in the inadequate support for the differentiated application of mul...
Indoor 3D reconstruction is particularly challenging due to complex scene structures involving object occlusion and overlap. This paper presents a hybrid indoor reconstruction method that segments the room point cloud into internal and external compo...
This review presents a comprehensive survey on deep learning-driven 3D object detection, focusing on the synergistic innovation between sensor modalities and technical architectures. Through a dual-axis “sensor modality–technical architec...
The spatial distribution of remote-sensing scene images is highly complex in character, so how to extract local key semantic information and discriminative features is the key to making it possible to classify accurately. However, most of the existin...
Power line inspection is an important part of the smart grid. Efficient real-time detection of power devices on the power line is a challenging problem for power line inspection. In recent years, deep learning methods have achieved remarkable results...
An airborne hyperspectral imaging system is typically equipped on an aircraft or unmanned aerial vehicle (UAV) to capture ground scenes from an overlooking perspective. Due to the rotation of the aircraft or UAV, the same region of land cover may be...
SAR ship detection and recognition are important components of the application of SAR data interpretation, allowing for the continuous, reliable, and efficient monitoring of maritime ship targets, in view of the present situation of SAR interpretatio...
This article proposes a lightweight YOLO-ACG detection algorithm that balances accuracy and speed, which improves on the classification errors and missed detections present in existing steel plate defect detection algorithms. To highlight the key ele...
Semantic segmentation has emerged as a critical research area in Earth observation. This paper proposes a novel end-to-end semantic segmentation network, the Nested Cross-Scale and Bidirectional Feature Fusion Network (NCSBFF-Net), to address issues...
The continuous development of intelligent video surveillance systems has increased the demand for enhanced vision-based methods of automated detection of anomalies within various behaviors found in video scenes. Several methods have appeared in the l...
Space-based, photon-counting lidar instruments are effective tools for observing cloud and aerosol layers in the atmosphere. Cloud phases and several different kinds of aerosols are presently identified and typed using sophisticated, fine-tuned class...
To address the issues of low detection accuracy and large model parameters in crop pest detection in natural scenes, this study improves the deep learning object detection model and proposes a lightweight and accurate method RTMDet++ for crop pest de...
Fire and smoke detection technologies face challenges in complex and dynamic environments. Traditional detectors are vulnerable to background noise, lighting changes, and similar objects (e.g., clouds, steam, dust), leading to high false alarm rates....
At present, millimeter wave radar imaging technology has become a recognized human security solution in the field. The millimeter wave radar imaging system can be used to detect a concealed object; multiple-input multiple-output radar antennas and sy...
Autonomous driving is a cornerstone of intelligent transportation systems, where visual elements such as traffic signs, lights, and pedestrians are critical for safety and decision-making. Yet, existing datasets often lack diversity, underrepresent r...
Sitting posture recognition, defined as automatically localizing and categorizing seated human postures, has become essential for large-scale ergonomics assessment and longitudinal health-risk monitoring in classrooms and offices. However, in real-wo...
Synthetic Aperture Radar (SAR) has the advantage of continuous observation throughout the day and in all weather conditions, and is used in a wide range of military and civil applications. Among these, the detection of ships at sea is an important re...
Inspired by the human visual system to concentrate on the important region of a scene, attention modules recalibrate the weights of either the channel features alone or along with spatial features to prioritize informative regions while suppressing u...
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