期刊文献+
共找到1,162篇文章
< 1 2 59 >
每页显示 20 50 100
Weakly Supervised Network with Scribble-Supervised and Edge-Mask for Road Extraction from High-Resolution Remote Sensing Images
1
作者 Supeng Yu Fen Huang Chengcheng Fan 《Computers, Materials & Continua》 SCIE EI 2024年第4期549-562,共14页
Significant advancements have been achieved in road surface extraction based on high-resolution remote sensingimage processing. Most current methods rely on fully supervised learning, which necessitates enormous human... Significant advancements have been achieved in road surface extraction based on high-resolution remote sensingimage processing. Most current methods rely on fully supervised learning, which necessitates enormous humaneffort to label the image. Within this field, other research endeavors utilize weakly supervised methods. Theseapproaches aim to reduce the expenses associated with annotation by leveraging sparsely annotated data, such asscribbles. This paper presents a novel technique called a weakly supervised network using scribble-supervised andedge-mask (WSSE-net). This network is a three-branch network architecture, whereby each branch is equippedwith a distinct decoder module dedicated to road extraction tasks. One of the branches is dedicated to generatingedge masks using edge detection algorithms and optimizing road edge details. The other two branches supervise themodel’s training by employing scribble labels and spreading scribble information throughout the image. To addressthe historical flaw that created pseudo-labels that are not updated with network training, we use mixup to blendprediction results dynamically and continually update new pseudo-labels to steer network training. Our solutiondemonstrates efficient operation by simultaneously considering both edge-mask aid and dynamic pseudo-labelsupport. The studies are conducted on three separate road datasets, which consist primarily of high-resolutionremote-sensing satellite photos and drone images. The experimental findings suggest that our methodologyperforms better than advanced scribble-supervised approaches and specific traditional fully supervised methods. 展开更多
关键词 Semantic segmentation road extraction weakly supervised learning scribble supervision remote sensing image
下载PDF
ConvNeXt-UperNet-Based Deep Learning Model for Road Extraction from High-Resolution Remote Sensing Images
2
作者 Jing Wang Chen Zhang Tianwen Lin 《Computers, Materials & Continua》 SCIE EI 2024年第8期1907-1925,共19页
When existing deep learning models are used for road extraction tasks from high-resolution images,they are easily affected by noise factors such as tree and building occlusion and complex backgrounds,resulting in inco... When existing deep learning models are used for road extraction tasks from high-resolution images,they are easily affected by noise factors such as tree and building occlusion and complex backgrounds,resulting in incomplete road extraction and low accuracy.We propose the introduction of spatial and channel attention modules to the convolutional neural network ConvNeXt.Then,ConvNeXt is used as the backbone network,which cooperates with the perceptual analysis network UPerNet,retains the detection head of the semantic segmentation,and builds a new model ConvNeXt-UPerNet to suppress noise interference.Training on the open-source DeepGlobe and CHN6-CUG datasets and introducing the DiceLoss on the basis of CrossEntropyLoss solves the problem of positive and negative sample imbalance.Experimental results show that the new network model can achieve the following performance on the DeepGlobe dataset:79.40%for precision(Pre),97.93% for accuracy(Acc),69.28% for intersection over union(IoU),and 83.56% for mean intersection over union(MIoU).On the CHN6-CUG dataset,the model achieves the respective values of 78.17%for Pre,97.63%for Acc,65.4% for IoU,and 81.46% for MIoU.Compared with other network models,the fused ConvNeXt-UPerNet model can extract road information better when faced with the influence of noise contained in high-resolution remote sensing images.It also achieves multiscale image feature information with unified perception,ultimately improving the generalization ability of deep learning technology in extracting complex roads from high-resolution remote sensing images. 展开更多
关键词 Deep learning semantic segmentation remote sensing imagery road extraction
下载PDF
A Road Extraction Method for Remote Sensing Image Based on Encoder-Decoder Network 被引量:24
3
作者 Hao HE Shuyang WANG +2 位作者 Shicheng WANG Dongfang YANG Xing LIU 《Journal of Geodesy and Geoinformation Science》 2020年第2期16-25,共10页
According to the characteristics of the road features,an Encoder-Decoder deep semantic segmentation network is designed for the road extraction of remote sensing images.Firstly,as the features of the road target are r... According to the characteristics of the road features,an Encoder-Decoder deep semantic segmentation network is designed for the road extraction of remote sensing images.Firstly,as the features of the road target are rich in local details and simple in semantic features,an Encoder-Decoder network with shallow layers and high resolution is designed to improve the ability to represent detail information.Secondly,as the road area is a small proportion in remote sensing images,the cross-entropy loss function is improved,which solves the imbalance between positive and negative samples in the training process.Experiments on large road extraction datasets show that the proposed method gets the recall rate 83.9%,precision 82.5%and F1-score 82.9%,which can extract the road targets in remote sensing images completely and accurately.The Encoder-Decoder network designed in this paper performs well in the road extraction task and needs less artificial participation,so it has a good application prospect. 展开更多
关键词 remote sensing road extraction deep learning semantic segmentation Encoder-Decoder network
下载PDF
Methods and Prospects of Road and Linear Structure Automatic Extraction from Remote Sensing Images
4
作者 LIU Zhengrong LIU Zhengrong, Research Center of Spatial Information and Digital Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, China. 《Geo-Spatial Information Science》 2003年第2期63-68,共6页
Automatic extraction of road and linear structure from remote sensing images is a very important problem. This paper analyses several existing methods of the automatic road and linear structure extraction by using som... Automatic extraction of road and linear structure from remote sensing images is a very important problem. This paper analyses several existing methods of the automatic road and linear structure extraction by using some multi-spectral remote sensing images acquired from different spatial resolutions, districts and road characteristics. Their advantages and disadvantages have been generalized. 展开更多
关键词 extraction linear structures remote sensing image PHOTOGRAMMETRY
下载PDF
A Road Extraction Method Based on Region Growing and Mathematical Morphology from Remote Sensing Images
5
作者 Yunhe Liu Chi Ma +4 位作者 Li Li Xiaoyan Xing Yong Zhang Zhigang Wang Jiuwei Xu 《Journal of Computer and Communications》 2018年第11期91-97,共7页
Road traffic is the important driving factor for economic and social development. With the rapid increase of vehicle population, road traffic problems such as traffic jam and traffic accident have become the bottlenec... Road traffic is the important driving factor for economic and social development. With the rapid increase of vehicle population, road traffic problems such as traffic jam and traffic accident have become the bottleneck which restricts economic development. In recent years, natural disasters frequently occur in China. Therefore, it is essential to extract road information to compute the degree of road damage for traffic emergency management. A road extraction method based on region growing and mathematical morphology from remote sensing images is proposed in this paper. According to the road features, the remote sensing image is preprocessed to separate road regions from non-road regions preliminarily. After image thresholding, region growing algorithm is used to extract connected regions. Then we sort connected regions by area to exclude the small regions which are probably non-road objects. Finally, the mathematical morphology algorithm is used to fill the holes inside the road regions. The experimental results show that the method proposed can effectively extract roads from remote sensing images. This research also has broad prospects in dealing with traffic emergency management by the government. 展开更多
关键词 Region GROWING MATHEMATICAL MORPHOLOGY road extraction remote sensing images
下载PDF
A Dense Feature Iterative Fusion Network for Extracting Building Contours from Remote Sensing Imagery
6
作者 WU Jiangyan WANG Tong 《Journal of Donghua University(English Edition)》 CAS 2024年第6期654-661,共8页
Extracting building contours from aerial images is a fundamental task in remote sensing.Current building extraction methods cannot accurately extract building contour information and have errors in extracting small-sc... Extracting building contours from aerial images is a fundamental task in remote sensing.Current building extraction methods cannot accurately extract building contour information and have errors in extracting small-scale buildings.This paper introduces a novel dense feature iterative(DFI)fusion network,denoted as DFINet,for extracting building contours.The network uses a DFI decoder to fuse semantic information at different scales and learns the building contour knowledge,producing the last features through iterative fusion.The dense feature fusion(DFF)module combines features at multiple scales.We employ the contour reconstruction(CR)module to access the final predictions.Extensive experiments validate the effectiveness of the DFINet on two different remote sensing datasets,INRIA aerial image dataset and Wuhan University(WHU)building dataset.On the INRIA aerial image dataset,our method achieves the highest intersection over union(IoU),overall accuracy(OA)and F 1 scores compared to other state-of-the-art methods. 展开更多
关键词 remote sensing image building contour extraction feature iteration
下载PDF
Road Vector Map Change Monitoring Based on High Resolution Remote Sensing Images 被引量:3
7
作者 Ting Yang Lulin Zhang +1 位作者 Haitao Wang Yong Zhang 《Advances in Remote Sensing》 2014年第4期272-279,共8页
Some studies about road vector map change detection were done in this paper. Firstly, on the basis of old road vector data, the original high resolution remote sensing image was cut into segments. Then, gray analysis ... Some studies about road vector map change detection were done in this paper. Firstly, on the basis of old road vector data, the original high resolution remote sensing image was cut into segments. Then, gray analysis and edge extraction of those segments were done so that changes of roads could be detected. Finally, according to the vector data and gray information of roads which were not changed, road templates were extracted and saved automatically. This method was performed on the World View high resolution image of certain parts in the country. The detection result shows that detection correctness is 79.56% and completeness can reach 97.72%. Moreover, the extracted road templates are essentials for the template matching method of road extraction. 展开更多
关键词 road VECTOR High RESOLUTION remote sensing image EDGE extraction CHANGE Monitoring
下载PDF
Extraction of coastline in high-resolution remote sensing images based on the active contour model
8
作者 邢坤 付宜利 +1 位作者 王树国 韩现伟 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2011年第4期13-18,共6页
While executing tasks such as ocean pollution monitoring,maritime rescue,geographic mapping,and automatic navigation utilizing remote sensing images,the coastline feature should be determined.Traditional methods are n... While executing tasks such as ocean pollution monitoring,maritime rescue,geographic mapping,and automatic navigation utilizing remote sensing images,the coastline feature should be determined.Traditional methods are not satisfactory to extract coastline in high-resolution panchromatic remote sensing image.Active contour model,also called snakes,have proven useful for interactive specification of image contours,so it is used as an effective coastlines extraction technique.Firstly,coastlines are detected by water segmentation and boundary tracking,which are considered initial contours to be optimized through active contour model.As better energy functions are developed,the power assist of snakes becomes effective.New internal energy has been done to reduce problems caused by convergence to local minima,and new external energy can greatly enlarge the capture region around features of interest.After normalization processing,energies are iterated using greedy algorithm to accelerate convergence rate.The experimental results encompassed examples in images and demonstrated the capabilities and efficiencies of the improvement. 展开更多
关键词 remote sensing images coastline extraction active contour model greedy algorithm
下载PDF
Automatic Extraction of Typical River Vegetation Elements Based on Low-altitude Remote Sensing Images
9
作者 Xiaomeng ZHANG Hao WU 《Meteorological and Environmental Research》 CAS 2023年第1期70-75,共6页
Based on low-altitude remote sensing images,this paper established sample set of typical river vegetation elements and proposed river vegetation extraction technical solution to adaptively extract typical vegetation e... Based on low-altitude remote sensing images,this paper established sample set of typical river vegetation elements and proposed river vegetation extraction technical solution to adaptively extract typical vegetation elements of river basins.The main research of this paper were as follows:(1)a typical vegetation extraction sample set based on low-altitude remote sensing images was established.(2)A low-altitude remote sensing image vegetation extraction model based on the focus perception module was designed to realize the end-to-end automatic extraction of different types of vegetation areas of low-altitude remote sensing images to fully learn the spectral spatial texture information and deep semantic information of the images.(3)By comparison with the baseline method,baseline method with embedded focus perception module showed an improvement in the precision by 7.37%and mIoU by 49.49%.Through visual interpretation and quantitative calculation analysis,the typical river vegetation adaptive extraction network has effectiveness and generalization ability,consistent with the needs of practical applications of vegetation extraction. 展开更多
关键词 altitude remote sensing images Deep learning Semantic segmentation Typical river vegetation extraction Attention
下载PDF
Privacy‐preserving remote sensing images recognition based on limited visual cryptography 被引量:3
10
作者 Denghui Zhang Muhammad Shafiq +2 位作者 Liguo Wang Gautam Srivastava Shoulin Yin 《CAAI Transactions on Intelligence Technology》 SCIE EI 2023年第4期1166-1177,共12页
With the arrival of new data acquisition platforms derived from the Internet of Things(IoT),this paper goes beyond the understanding of traditional remote sensing technologies.Deep fusion of remote sensing and compute... With the arrival of new data acquisition platforms derived from the Internet of Things(IoT),this paper goes beyond the understanding of traditional remote sensing technologies.Deep fusion of remote sensing and computer vision has hit the industrial world and makes it possible to apply Artificial intelligence to solve problems such as automatic extraction of information and image interpretation.However,due to the complex architecture of IoT and the lack of a unified security protection mechanism,devices in remote sensing are vulnerable to privacy leaks when sharing data.It is necessary to design a security scheme suitable for computation‐limited devices in IoT,since traditional encryption methods are based on computational complexity.Visual Cryptography(VC)is a threshold scheme for images that can be decoded directly by the human visual system when superimposing encrypted images.The stacking‐to‐see feature and simple Boolean decryption operation make VC an ideal solution for privacy‐preserving recognition for large‐scale remote sensing images in IoT.In this study,the secure and efficient transmission of high‐resolution remote sensing images by meaningful VC is achieved.By diffusing the error between the encryption block and the original block to adjacent blocks,the degradation of quality in recovery images is mitigated.By fine‐tuning the pre‐trained model from large‐scale datasets,we improve the recognition performance of small encryption datasets for remote sensing images.The experimental results show that the proposed lightweight privacy‐preserving recognition framework maintains high recognition performance while enhancing security. 展开更多
关键词 activity recognition feature extraction image classification KNN privacy protection remote monitoring remote sensing
下载PDF
Automatic road extraction framework based on codec network
11
作者 WANG Lin SHEN Yu +2 位作者 ZHANG Hongguo LIANG Dong NIU Dongxing 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2024年第3期318-327,共10页
Road extraction based on deep learning is one of hot spots of semantic segmentation in the past decade.In this work,we proposed a framework based on codec network for automatic road extraction from remote sensing imag... Road extraction based on deep learning is one of hot spots of semantic segmentation in the past decade.In this work,we proposed a framework based on codec network for automatic road extraction from remote sensing images.Firstly,a pre-trained ResNet34 was migrated to U-Net and its encoding structure was replaced to deepen the number of network layers,which reduces the error rate of road segmentation and the loss of details.Secondly,dilated convolution was used to connect the encoder and the decoder of network to expand the receptive field and retain more low-dimensional information of the image.Afterwards,the channel attention mechanism was used to select the information of the feature image obtained by up-sampling of the encoder,the weights of target features were optimized to enhance the features of target region and suppress the features of background and noise regions,and thus the feature extraction effect of the remote sensing image with complex background was optimized.Finally,an adaptive sigmoid loss function was proposed,which optimizes the imbalance between the road and the background,and makes the model reach the optimal solution.Experimental results show that compared with several semantic segmentation networks,the proposed method can greatly reduce the error rate of road segmentation and effectively improve the accuracy of road extraction from remote sensing images. 展开更多
关键词 remote sensing image road extraction ResNet34 U-Net channel attention mechanism sigmoid loss function
下载PDF
Design of Content-Based Retrieval System in Remote Sensing Image Database 被引量:1
12
作者 LI Feng ZENG Zhiming HU Yanfeng FU Kun 《Geo-Spatial Information Science》 2006年第3期191-195,共5页
To retrieve the object region efficaciously from massive remote sensing image database, a model for content-based retrieval of remote sensing image is given according to the characters of remote sensing image applicat... To retrieve the object region efficaciously from massive remote sensing image database, a model for content-based retrieval of remote sensing image is given according to the characters of remote sensing image application firstly, and then the algorithm adopted for feature extraction and multidimensional indexing, and relevance feedback by this model are analyzed in detail. Finally, the contents intending to be researched about this model are proposed. 展开更多
关键词 content-based retrieval remote sensing image image database feature extraction object region
下载PDF
Land-Cover Classification and its Impact on Peshawar’s Land Surface Temperature Using Remote Sensing 被引量:4
13
作者 Shahab Ul Islam Saifullah Jan +3 位作者 Abdul Waheed Gulzar Mehmood Mahdi Zareei Faisal Alanazi 《Computers, Materials & Continua》 SCIE EI 2022年第2期4123-4145,共23页
Spatial and temporal informationon urban infrastructure is essential and requires various land-cover/land-use planning and management applications.Besides,a change in infrastructure has a direct impact on other land-c... Spatial and temporal informationon urban infrastructure is essential and requires various land-cover/land-use planning and management applications.Besides,a change in infrastructure has a direct impact on other land-cover and climatic conditions.This study assessed changes in the rate and spatial distribution of Peshawar district’s infrastructure and its effects on Land Surface Temperature(LST)during the years 1996 and 2019.For this purpose,firstly,satellite images of bands7 and 8 ETM+(Enhanced Thematic Mapper)plus and OLI(Operational Land Imager)of 30 m resolution were taken.Secondly,for classification and image processing,remote sensing(RS)applications ENVI(Environment for Visualising Images)and GIS(Geographic Information System)were used.Thirdly,for better visualization and more in-depth analysis of land sat images,pre-processing techniques were employed.For Land use and Land cover(LU/LC)four types of land cover areas were identified-vegetation area,water cover,urbanized area,and infertile land for the years under research.The composition of red,green,and near infra-red bands was used for supervised classification.Classified images were extracted for analyzing the relative infrastructure change.A comparative analysis for the classification of images is performed for SVM(Support Vector Machine)and ANN(Artificial Neural Network).Based on analyzing these images,the result shows the rise in the average temperature from 30.04℃ to 45.25℃.This only possible reason is the increase in the built-up area from 78.73 to 332.78 Area km^(2) from 1996 to 2019.It has also been witnessed that the city’s sides are hotter than the city’s center due to the barren land on the borders. 展开更多
关键词 remote sensing temperature extraction URBANIZATION satellite image classification artificial neural network support vector machine LU/LC land surface temperature
下载PDF
Applying Digital Image Processing to Evaluate a Extraction Method of Cartographic Features in Digital Images
14
作者 Erivaldo Antonio da Silva Guilherme Pina Cardim 《Journal of Earth Science and Engineering》 2012年第4期241-246,共6页
A topic studied in cartography is to make the extraction of cartographic features that provide the update of cartographic maps more easily. For this reason many automatic routines were created with the intent to perfo... A topic studied in cartography is to make the extraction of cartographic features that provide the update of cartographic maps more easily. For this reason many automatic routines were created with the intent to perform the features extraction. Despite of all studies about this, some features cannot be found by the algorithm or it can extract some pixels unduly. So the current article aims to show the results with the software development that uses the original and reference image to calculate some statistics about the extraction process. Furthermore, the calculated statistics can be used to evaluate the extraction process. 展开更多
关键词 remote sensing cartographic features extraction evaluate process digital image processing.
下载PDF
Semiautomatic Algorithm to Extraction of Cartographic Features in Digital Images
15
作者 Erivaldo Antonio da Silva Guilherme Pina Cardim Ruben de Best 《通讯和计算机(中英文版)》 2012年第11期1247-1251,共5页
关键词 自动提取算法 制图 数码影像 特征提取 研究人员 自动程序 算法选择 半自动
下载PDF
A review of road extraction from remote sensing images 被引量:17
16
作者 Weixing Wang Nan Yang +3 位作者 Yi Zhang Fengping Wang Ting Cao Patrik Eklund 《Journal of Traffic and Transportation Engineering(English Edition)》 2016年第3期271-282,共12页
As a significant role for traffic management, city planning, road monitoring, GPS navigation and map updating, the technology of road extraction from a remote sensing (RS) image has been a hot research topic in rece... As a significant role for traffic management, city planning, road monitoring, GPS navigation and map updating, the technology of road extraction from a remote sensing (RS) image has been a hot research topic in recent years. In this paper, after analyzing different road features and road models, the road extraction methods were classified into the classification-based methods, knowledge-based methods, mathematical morphology, active contour model, and dynamic programming. Firstly, the road features, road model, existing difficulties and interference factors for road extraction were analyzed. Secondly, the principle of road extraction, the advantages and disadvantages of various methods and research achievements were briefly highlighted. Then, the comparisons of the different road extraction algorithms were performed, including road features, test samples and shortcomings. Finally, the research results in recent years were summarized emphatically. It is obvious that only using one kind of road features is hard to get an excellent extraction effect. Hence, in order to get good results, the road extraction should combine multiple methods according to the real applications. In the future, how to realize the complete road extraction from a RS image is still an essential but challenging and important research topic. 展开更多
关键词 remote sensing image road extraction road feature CLASSIFICATION
原文传递
Fast Remote-Sensing Image Registration Using Priori Information and Robust Feature Extraction 被引量:5
17
作者 Xijia Liu Xiaoming Tao Ning Ge 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2016年第5期552-560,共9页
In this paper, we propose a fast registration scheme for remote-sensing images for use as a fundamental technique in large-scale online remote-sensing data processing tasks. First, we introduce priori-information imag... In this paper, we propose a fast registration scheme for remote-sensing images for use as a fundamental technique in large-scale online remote-sensing data processing tasks. First, we introduce priori-information images,and use machine learning techniques to identify robust remote-sensing image features from state-of-the-art ScaleInvariant Feature Transform(SIFT) features. Next, we apply a hierarchical coarse-to-fine feature matching and image registration scheme on the basis of additional priori information, including a robust feature location map and platform imaging parameters. Numerical simulation results show that the proposed scheme increases position repetitiveness by 34%, and can speed up the overall image registration procedure by a factor of 7:47 while maintaining the accuracy of the image registration performance. 展开更多
关键词 remote sensing image registration priori information feature extraction
原文传递
A landslide extraction method of channel attention mechanismU-Net network based on Sentinel-2A remote sensing images
18
作者 Hesheng Chen Yi He +5 位作者 Lifeng Zhang Sheng Yao Wang Yang Yumin Fang Yaoxiang Liu Binghai Gao 《International Journal of Digital Earth》 SCIE EI 2023年第1期552-577,共26页
Accurate landslide extraction is significant for landslide disaster prevention and control.Remote sensing images have been widely used in landslide investigation,and landslide extraction methods based on deep learning... Accurate landslide extraction is significant for landslide disaster prevention and control.Remote sensing images have been widely used in landslide investigation,and landslide extraction methods based on deep learning combined with remote sensing images(such as U-Net)have received a lot of attention.However,because of the variable shape and texture features of landslides in remote sensing images,the rich spectral features,and the complexity of their surrounding features,landslide extraction using U-Net can lead to problems such as false detection and missed detection.Therefore,this study introduces the channel attention mechanism called the squeeze-and-excitation network(SENet)in the feature fusion part of U-Net;the study also constructs an attention U-Net landside extraction model combining SENet and U-Net,and uses Sentinel-2A remote sensing images for model training and validation.The extraction results are evaluated through different evaluation metrics and compared with those of two models:U-Net and U-Net Backbone(U-Net Without Skip Connection).The results show that proposed the model can effectively extract landslides based on Sentinel-2A remote sensing images with an F1 value of 87.94%,which is about 2%and 3%higher than U-Net and U-Net Backbone,respectively,with less false detection and more accurate extraction results. 展开更多
关键词 Sentinel-2A remote sensing image landslide extraction U-Net attention mechanism deep learning
原文传递
基于改进DeeplabV3+的遥感图像道路分割模型 被引量:1
19
作者 张银胜 单梦姣 +3 位作者 钟思远 陈戈 童俊毅 单慧琳 《国外电子测量技术》 2024年第1期189-198,共10页
针对遥感图像道路分割边界模糊和遮挡难以区分的问题,提出了基于改进DeeplabV3+的遥感图像道路分割模型。该模型在主干网络中引入MobileNetV3和高效通道注意力机制(ECA),减少了参数量并关注连续的道路特征信息。在解码过程中采用多级上... 针对遥感图像道路分割边界模糊和遮挡难以区分的问题,提出了基于改进DeeplabV3+的遥感图像道路分割模型。该模型在主干网络中引入MobileNetV3和高效通道注意力机制(ECA),减少了参数量并关注连续的道路特征信息。在解码过程中采用多级上采样,增强了编码器和解码器之间的紧密连接,全面保留了细节信息。同时,在ASPP模块中采用深度可分离膨胀卷积DS-ASPP,显著减少了参数量。实验结果表明,该模型在Massachusetts Roads数据集上的交并比达到了83.71%,准确率达到了93.71%,分割精度最优,模型参数量为55.57×10^(6),能够有效地避免边界模糊和遮挡导致的错漏检问题,在遥感道路分割中提高了精度和速度。 展开更多
关键词 遥感图像 道路分割 DeeplabV3+模型 MobileNetV3模型 多级上采样
下载PDF
改进YOLOX的遥感图像目标检测算法 被引量:1
20
作者 梁燕 饶星晨 《计算机工程与应用》 CSCD 北大核心 2024年第12期181-188,共8页
针对遥感图像目标检测算法复杂背景下目标检测精度低、小目标特征丢失的问题,提出一种改进YOLOX的遥感图像目标检测算法MYOLOX(modified YOLOX)。该算法在主干网络引入残差金字塔卷积模块(residual pyramid convolution module,RPCM)增... 针对遥感图像目标检测算法复杂背景下目标检测精度低、小目标特征丢失的问题,提出一种改进YOLOX的遥感图像目标检测算法MYOLOX(modified YOLOX)。该算法在主干网络引入残差金字塔卷积模块(residual pyramid convolution module,RPCM)增强浅层特征图中的空间位置等细节信息,缓解下采样过程中的特征丢失。引入增强跨阶段局部块(improved cross stage partial block,ICSP)提取丰富的上下文信息并抑制噪声干扰,减少复杂背景及噪声干扰带来误检。将改进算法应用于使用DIOR数据集对NWPU VHR-10数据集扩充后数据集和SSDD数据集,MYOLOX算法检测平均精度均值(mean average precision,mAP)分别达到了80.8%和94.4%,较原算法提升了4.1和4.5个百分点。实验结果证明,改进后的算法能够明显提高遥感图像目标检测精度。 展开更多
关键词 目标检测 遥感图像 多尺度特征提取 浅层特征增强
下载PDF
上一页 1 2 59 下一页 到第
使用帮助 返回顶部