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检索条件"任意字段=Conference on Image Processing and Pattern Recognition in Remote Sensing II"

共 707 条记录，以下是11-20 订阅

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Improved YOLOv5's remote sensing image detection algorithm

Improved YOLOv5's remote sensing image detection algorithm

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2024 International conference on image, Signal processing, and pattern recognition, ISPP 2024

作者： Chen, Naikang Zhang, Jinmin School of Mechanical Engineering Lanzhou Jiaotong University Gansu Lanzou730070 China

ISBN: (纸本)9781510680425

Addressing the challenges inherent in remote sensing image detection, notably the YOLOv5 detector's subpar accuracy due to limited detection target features, intricate detection backgrounds, and predominantly small targets, this paper presents the RS-YOLOv5 algorithm, an enhancement of the YOLOv5s model. Primarily, the conventional convolution in the backbone network is substituted with full-dimensional dynamic convolution to enrich the information gathered across multiple dimensions, thereby enhancing the extraction of target features. Additionally, a decoupled detection head replaces the traditional detection head, refining classification and positioning tasks while strategically leveraging features from different levels to mitigate background interference on small target detection. Next, adding the Normalized Wasserstein distance (NWD) measurement to the loss function is intended to enhance the accuracy of small target detection. The ablation analysis conducted on the DOTA dataset showcases a noteworthy improvement in the average accuracy of object detection in remote sensing imagery facilitated by the proposed RS-YOLOv5 algorithm, culminating in a 2.5% increase in average accuracy compared to the original algorithm. © 2024 SPIE.

关键词： Optical remote sensing

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Land cover classification of UAV visible remote sensing based on superpixel

Land cover classification of UAV visible remote sensing base...

引用

2024 International conference on image, Signal processing, and pattern recognition, ISPP 2024

作者： Zeng, Yushuang College of Computer and Information Science Chongqing Normal University Chongqing401331 China

ISBN: (纸本)9781510680425

Because of the convenience and low cost of obtaining visible remote sensing images of UAV, it is widely used in agricultural production. In land cover classification, in order to obtain more homogeneous superpixels of UAV remote sensing images and achieve accurate classification, a superpixel segmentation algorithm with improved DPC (density peak clustering) of grid point was proposed, and land cover classification was carried out based on this algorithm. The method is as follows: the paper defined color-space distance measurement to calculate local density and relative distance;then, using the connected graph, the connected distance between each grid point to be allocated and the cluster center point connected to the neighborhood is obtained, and it is assigned to the superpixel cluster with the smallest distance. Finally, pixel blocks are generated according to the super-pixel segmentation image and as data to classification by resnet50. Experiments show that the classification accuracy of the proposed algorithm is 99.6%, which is superior to SLIC and the original superpixel segmentation algorithm with DPC of grid point, and proves that the algorithm is effective. © 2024 SPIE.

关键词： image segmentation

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Application of remote sensing in retrieving combustible fuel load in Guizhou region

Application of remote sensing in retrieving combustible fuel...

引用

2024 International conference on image, Signal processing, and pattern recognition, ISPP 2024

作者： Zhang, Sihang Yang, Zhi Zhu, Kuanjun Liu, Bin Zhou, Lixian Han, Junke Liu, Chang China Electric Power Research Institute Beijing100192 China

ISBN: (纸本)9781510680425

Guizhou Province, situated in the southwest of China, boasts diverse and complex geographical environments and abundant forest resources. However, it faces threats from natural disasters like forest fires. Accurate estimation of combustible fuel load in Guizhou is crucial for assessing fire risks and implementing effective fire management strategies. This paper employs remote sensing technology, utilizing various remote sensing datasets including satellite imagery and ground observation data, combined with geostatistical methods to retrieve combustible fuel load in Guizhou. Furthermore, the application value of the retrieval results in fire prevention management and forest resource protection is discussed. © 2024 SPIE.

关键词： Risk management

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Landslide extraction model for remote sensing images based on improved DeepLabv3+

Landslide extraction model for remote sensing images based o...

引用

2024 International conference on image, Signal processing, and pattern recognition, ISPP 2024

作者： Wu, Yanling Lang, Wenhui School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China

ISBN: (纸本)9781510680425

In order to realize the accurate recognition of landslides in remote sensing images, an improved DeepLabv3+ landslide extraction model is proposed in this paper.(1) Hybrid Module and Attention Module based CSPNet (HA-CSPNet) is constructed as the backbone feature extraction network to enhance the feature information of small landslides and suppress the interference of irrelevant background information.(2) Combining the advantages of Residual Structure and Dense Connected Module, Residual-dense ASPP is designed to focus landslide features at different scales, enhance feature reuse and prevent gradient *** experimental results show that the landslide extraction model proposed in this paper is *** can improve the accuracy of landslide semantic segmentation. © 2024 SPIE.

关键词： Semantic Segmentation

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A novel CNN-Transformer network for cloud detection in remote sensing image

A novel CNN-Transformer network for cloud detection in remot...

引用

2024 International conference on image, Signal processing, and pattern recognition, ISPP 2024

作者： Ai, Xinkai Sun, Lin College of Geodesy and Geomatics Shandong University of Science and Technology Shandong Province Qingdao266590 China

ISBN: (纸本)9781510680425

Convolutional neural networks (CNNs) are the mainstream model for extracting rich features in deep learning-driven studies on cloud detection for remote sensing images. However, due to the limitation of receptive fields in convolutional operations, a single CNN has limitations in mining global information and long-range dependencies, which affects the precision of cloud identification in intricate environments. To address the insufficient global feature and long-range dependency modeling of CNNs, a new CNN-Transformer network for cloud detection (CNN-TFCD) in remote sensing images is proposed in this paper. This method comprehensively utilizes strengths of both CNN and transformer to fully mine local spatial details, global contextual information, and long-range dependencies, to obtain comprehensive feature representations. The feature cascade mechanism is applied for multi-scale feature fusion, further enhancing the model's feature representation capability. Evaluations conducted on the Landsat-8 dataset demonstrate that the CNN-TFCD method achieves accurate and stable cloud detection. © 2024 SPIE.

关键词： Convolutional neural networks

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SPECTRAL-AWARE DEEP NETWORKS FOR OBJECT DETECTION IN HYPERSPECTRAL imageS WITH CLOUD INTERFERENCE 24

SPECTRAL-AWARE DEEP NETWORKS FOR OBJECT DETECTION IN HYPERSP...

引用

1st International conference on image processing Machine Learning and pattern recognition

作者： Zhang, Ying Zeng, Xia Zhang, Hongtao Xu, Meng Shenzhen Univ Shenzhen Peoples R China Beijing Inst Astronaut Syst Engn Beijing Peoples R China

ISBN: (纸本)9798400707032

Hyperspectral object detection (HOD) aims to identify and locate multiple objects in a scene using hyperspectral images (HSIs). While much research has focused on hyperspectral target detection (HTD) at the pixel level, HOD remains underexplored. Traditional HTD methods rely heavily on prior spectral information of the target and simple pixel neighborhood relationships, leading to accuracy issues when targets are occluded. Inspired by advances in RGB image detection, we propose a compact and efficient cloud-robust hyperspectral object detection network (CR-HODNet) using 3D convolution to extract spatial and spectral features jointly. We further enhance these features with channel and spatial attention mechanisms and address cloud occlusion challenges using transformer-based multi-head attention. Our method is validated on real airborne hyperspectral images with synthetic cloud occlusion, showing robust performance in challenging scenarios.

关键词： Hyperspectral image Object detection Cloud interference remote sensing

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D-CANet: Diverse class-aware coding and decoding structure network for semantic segmentation of high-resolution remote sensing images

D-CANet: Diverse class-aware coding and decoding structure n...

引用

2024 International conference on image, Signal processing, and pattern recognition, ISPP 2024

作者： Yuan, Zhengwu Shao, Wen Chen, Qiang Ke, Yingqi College of Computer Science and Technology Chongqing University of Posts and Telecommunication Chongqing China

ISBN: (纸本)9781510680425

The substantial scale variation and intra-class diversity within remote sensing imagery pose significant challenges for semantic segmentation, rendering methods developed for natural images inapplicable. These challenges, we introduce a novel semantic segmentation model named D-CANet, which primarily comprises three modules: the Global Class Center Awareness (GCCA), the Local Class Awareness Module (LCAM), and the Global Class Generation Module (GCG). Specifically, the GCCA module is dedicated to modeling the global representation of class context to mitigate the interference from image backgrounds;the LCAM module generates a local class representation, serving as an intermediary perceptual element that facilitates an implicit linkage between pixels and global class representations, minimizing the variance within classes;following the processing by the LCAM module, the GCG module enhances the global class representation. This encoder-decoder structure equipped with GCCA, LCAM, and GCG modules achieves precise segmentation of objects of varying scales within remote sensing imagery through the interactive perception and fusion of global and local features. Experimental assessments conducted on the Potsdam dataset and the Vaihingen dataset illustrate that D-CANet surpasses the current state-of-the-art semantic segmentation techniques in terms of efficacy. © 2024 SPIE.

关键词： Semantic Segmentation

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Atmospheric Correction of High Resolution remote sensing images with Automatic Data Acquisition by Network 24

Atmospheric Correction of High Resolution Remote Sensing Ima...

引用

1st International conference on image processing Machine Learning and pattern recognition

作者： Wan, Xingyu Chen, Xingfeng Li, Kaitao Wang, Yanping Xiao, Qian Wan, Wei Liu, Shumin Zhao, Limin Jiangxi Univ Sci & Technol Sch Software Engn Ganzhou Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing Peoples R China Space Engn Univ Sch Space Informat Beijing Peoples R China Inst Disaster Prevent Sanhe Peoples R China Space Star Technol Co Ltd Beijing Peoples R China Peking Univ Inst Remote Sensing & GIS Sch Earth & Space Sci Beijing Peoples R China

ISBN: (纸本)9798400707032

Atmospheric parameters are necessary inputs for atmospheric correction, but obtaining these parameters is difficult. To address this challenge, a solution for atmospheric parameter acquisition based on NNAeroG and networked automatic matching was proposed. This solution, combined with QUAAC, enables the atmospheric correction of GF images, thereby achieving full process automation of atmospheric correction. This scheme effectively simplifies the tedious process of obtaining AOD in existing methods and greatly improves the efficiency of atmospheric correction. The atmospheric parameters provided by this program can support multiple atmospheric correction methods, reduce labor-intensive operations, and offer efficient tools for large-scale atmospheric radiation production and research.

关键词： Atmospheric correction Automated program Aerosol optical depth Geostationary satellite

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Rotated Multi-Scale Interaction Network for Referring remote sensing image Segmentation

Rotated Multi-Scale Interaction Network for Referring Remote...

引用

IEEE/CVF conference on Computer Vision and pattern recognition (CVPR)

作者： Liu, Sihan Ma, Yiwei Zhang, Xiaoqing Wang, Haowei Ji, Jiayi Sun, Xiaoshuai Ji, Rongrong Xiamen Univ Key Lab Multimedia Trusted Percept & Efficient Co Minist Educ China Xiamen 361005 Peoples R China

ISBN: (纸本)9798350353006

Referring remote sensing image Segmentation (RRSIS) is a new challenge that combines computer vision and natural language processing. Traditional Referring image Segmentation (RIS) approaches have been impeded by the complex spatial scales and orientations found in aerial imagery, leading to suboptimal segmentation results. To address these challenges, we introduce the Rotated Multi-Scale Interaction Network (RMSIN), an innovative approach designed for the unique demands of RRSIS. RMSIN incorporates an Intra-scale Interaction Module (iiM) to effectively address the fine-grained detail required at multiple scales and a Cross-scale Interaction Module (CIM) for integrating these details coherently across the network. Furthermore, RMSIN employs an Adaptive Rotated Convolution ARC) to account for the diverse orientations of objects, a novel (contribution that significantly enhances segmentation accuracy. To assess the efficacy of RMSIN, we have curated an expansive dataset comprising 17,402 image-caption-mask triplets, which is unparalleled in terms of scale and variety. This dataset not only presents the model with a wide range of spatial and rotational scenarios but also establishes a stringent benchmark for the RRSIS task, ensuring a rigorous evaluation of performance. Experimental evaluations demonstrate the exceptional performance of RM-SIN, surpassing existing state-of-the-art models by a significant margin. Datasets and code are available at https://***/Lsan2401/RMSIN.

关键词： image segmentation

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Detection and Classification of Satellite remote sensing images Using Hybrid Segmentation and Feature Extraction with Effective Algorithms 2

Detection and Classification of Satellite Remote Sensing Ima...

引用

2nd International conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Vinuja, G. Devi, N. Bharatha Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Cse Chennai India

ISBN: (纸本)9798350382952

The remote sensing image analysis, classification, and pattern recognition processes all depend on image segmentation. In this research, a search-based convolutional neural network (SBCNN) is used to identification method for remote sensing images. Prior to applying the image data to the SKFCM with PeSOA segmentation step, the image data must first undergo pre-processing. When pre-processing satellite images for road networks, noise is removed using an improved median filtering technique. The image is then segmented using the SKFCM with PeSOA Segmentation technique to have inverse shape determination with lowest energy usage. Using an intensity constraint, it is possible to identify the segments of a building and vegetation, a road, and a barren area of land. Following segmentation, MLBP with DWT feature extraction is performed on the road satellite images, and the SBCNN is then used to categorize the images. After associated with obtainable methods, the findings of the suggested technique display excellent precision of 98.6%. © 2024 IEEE.

关键词： Feature extraction

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