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检索条件"任意字段=Conference on Image and Signal Processing for Remote Sensing IX"

共 3614 条记录，以下是161-170 订阅

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Super-Resolution Reconstruction of remote sensing images Based on Improved Residual Dense Network

Super-Resolution Reconstruction of Remote Sensing Images Bas...

引用

2023 IEEE International conference on image processing and Computer Applications, ICIPCA 2023

作者： Pengying, Zhang Ming, Zhang Jianjun, Li Baohua, Zhang Inner Mongolia University of Science and Technology College of Information Engineering Baotou China

ISBN: (纸本)9798350314670

An improved residual dense network (IRDN)-based remote sensing images super-resolution reconstruction technique is suggested in order to address the issues with single feature extraction and equalization of feature processing in the current remote sensing images super-resolution reconstruction algorithm. By strengthening the structure of the original residual dense model, adding a branch to extract more varied and rich features, and establishing the channel and position relationship of features by Coordinate Attention (CA), the algorithm enhances the performance of the model's feature extraction and representation. The strategy can effectively increase the reconstruction performance while significantly lowering the number of model parameters, according to experiments using the publicly accessible remote sensing datasets UC MERCED and NWPU-RESISC45. On the UC MERCED dataset with a scale factor of 4, the objective performance evaluation indexes of peak signal to noise ratio and structure similarity achieve 29.3126dB and 0.7987, respectively, and good visual effects are obtained. It offers a solid foundation for the future remote sensing images' additional analysis and processing. © 2023 IEEE.

关键词： Feature extraction

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MTP-Net: Road extraction from remote sensing images based on multi-type pooling network

MTP-Net: Road extraction from remote sensing images based on...

引用

2023 International conference on image, signal processing, and Pattern Recognition, ISPP 2023

作者： Wei, Zhiheng Zhang, Zhenyu College of Information Science and Engineering Xinjiang University Urumqi830017 China Key Laboratory of Multilingual Information Technology in Xinjiang Uygur Autonomous Region Xinjiang University Urumqi830017 China

ISBN: (纸本)9781510666351

As a popular task in remote sensing, road extraction has been widely concerned and applied by researchers, especially by using deep learning methods. However, many methods ignore the properties of roads in remote sensing images, which have long-range structures or discrete distributions. Therefore, this paper designs a network(MTP-Net) based on strip pooling and multi-scale spatial pooling. This network uses the ResNet50 network as the encoder to achieve feature extraction and ensures the overall connectivity and edge details of roads by the multi-type pooling module including strip pooling and multi-scale spatial pooling. The MTP-Net was tested on the Massachusetts Roads Dataset, the F1-score and IoU(intersection ratio) reached 72.24% and 56.54%, respectively. Compared with the mainstream methods such as UNet and deeplabV3+, the experiment shows that the MTP-Net is superior to the comparison model and has good results in road extraction. © 2023 SPIE.

关键词： remote sensing

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JOINT CLASSIFICATION OF HYPERSPECTRAL AND LIDAR DATA USING CROSS-MODAL HIERARCHICAL FREQUENCY FUSION NETWORK 49

JOINT CLASSIFICATION OF HYPERSPECTRAL AND LIDAR DATA USING C...

引用

49th IEEE International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Zeng, Zheng Song, Tiecheng Ma, Xinran Jiu, Yinghao Sun, Huaiyi Chongqing Univ Posts & Telecommun Sch Commun & Informat Engn Chongqing 400065 Peoples R China Chongqing Acad Sci & Technol Chongqing 401123 Peoples R China

ISBN: (纸本)9798350344868;9798350344851

The fusion of hyperspectral images (HSIs) and LiDAR data can improve land cover classification performance. However, existing fusion methods do not well consider the large discrepancies between two modalities (e.g., brightness, structure, and the possible misalignment), leading to limited improvement. In this paper, we handle this problem in frequency domain and propose a cross-modal hierarchical frequency fusion network (HFNet) for joint classification of HSI and LiDAR data. First, we extract multi-level convolutional features from both modalities. Then, we explore spatial activation maps to adaptively fuse cross-modal frequency features at each level. Since the amplitude and phase information of two modalities are separately fused, the discrepancy problem is alleviated. Finally, we concatenate the fused features at all levels to build a classification loss and an auxiliary frequency consistency loss (FCL). FCL enables the concatenated feature to predict the amplitude and phase information of the input data, which acts as a regularization term and improves the model's discrimination ability. Experimental results on two datasets show the superiority of HFNet over the state-of-the-art methods in terms of classification performance.

关键词： Hyperspectral image light detection and ranging (LiDAR) data frequency image fusion remote sensing land cover classification

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Difference Guided VHR remote sensing image Change Detection 48

Difference Guided VHR Remote Sensing Image Change Detection

引用

48th IEEE International conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Sun, Jiukai Liu, Ganchao Li, Xuelong Yuan, Yuan School of Computer Science China China Ministry of Industry and Information Technology Xi'an710072 China Northwestern Polytechnical University Shaanxi Xi'an710072 China

ISBN: (纸本)9781728163277

VHR remote sensing images have abundant ground features and details, but it is a great challenge for machine understanding. The "same object with different spectral"problem caused by environment changes, such as seasonal alternation, bad weather and shadow, is the biggest challenge in multitemporal image change detection, which is more prominent in VHR images. For this problem, a novel difference guided VHR image change detection (DGCD) method is proposed in this paper. In the feature learning stage of DGCD model, difference features are used to guide the feature extraction to suit with the change detection task. In order to make the model focus on the change features, both of the spatial and channel attention mechanism are introduced. Finally, for the edge region of VHR image which is hard to be discriminated, a new edge enhanced loss function based on BCL loss is designed. Experiments on public datasets show the superiority of proposed DGCD method. It has good generalization ability in different classical challenging scenarios. Compared with the representative methods in recent years, the proposed DGCD method performs better on VHR image change detection. © 2023 IEEE.

关键词： Change detection very-high resolution(VHR) remote sensing

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Multimodal Unsupervised Domain Adaptation for remote sensing image Segmentation 13

Multimodal Unsupervised Domain Adaptation for Remote Sensing...

引用

13th Workshop on Hyperspectral Imaging and signal processing: Evolution in remote sensing, WHISPERS 2023

作者： Zou, Jiaqi Li, Zhuohong Lu, Fangxiao He, Wei Zhang, Hongyan Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan430079 China China University of Geosciences School of Computer Science Wuhan430078 China

ISBN: (纸本)9798350395570

We propose a multimodal domain-adaptive approach for remote sensing image segmentation, which consists of a multimodal generator network together with a discriminator and adversarial strategy. The multimodal generator employs HR-Net48 as the backbone to construct three specific networks and one shared network, in order to extract domain-specific from individual remote sensing images and shared information from concatenated multimodal images. We adopt AdvEnt as our base scheme for the discriminator and adversarial strategy, leveraging the prior knowledge that source-trained models produce low-entropy (over-confident) predictions for source-like images and high-entropy (under-confident) predictions for target-like images. The discriminator evaluates the entropy map of the generator's segmentation result to guide the adversarial process. As training progresses, the entropy of the segmentation result reduces for the target domain, achieving domain adaptation from source to target. Finally, the trained generator is applied to unlabeled target domain images to effectively obtain segmentation results. © 2023 IEEE.

关键词： Semantic Segmentation

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A Dual Network Approach with Enhanced Feature Guidance for SAR-to-Optical image Translation 2

A Dual Network Approach with Enhanced Feature Guidance for S...

引用

2nd IEEE International conference on signal, Information and Data processing, ICSIDP 2024

作者： He, Jingfei Mao, Yongfei Zhao, Liangbo Cui, Zihan Wang, Yu Shi, Hao Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China China Academy of Space Technology Institute of Remote Sensing Satellite Beijing China China Academy of Space Technology Beijing Institute of Space Mechanics and Electricity Beijing China

ISBN: (纸本)9798331515669

Synthetic Aperture Radar (SAR) images own dominant merits, such as all-weather and all-day working conditions, but it is difficult for unprofessional people to interpret SAR images. Translating SAR images into optical images to assist interpretation can facilitate the transformation of SAR data into usable information. Therefore, an advanced SAR to Optical (S2O) image translation method utilizing a parallel Generative Adversarial Network (GAN) framework was proposed. This method incorporates an optical image reconstruction network alongside the S2O translation network, enhancing the fidelity and color consistency of the translated images. By employing a domain alignment module and a deep supervision feature loss module, the network effectively utilizes abundant optical image features to overcome the scarcity of SAR-optical image pairs. Experiments conducted on the SEN1-2 dataset demonstrate superior performance over existing methods, particularly in preserving detailed structural information in images. © 2024 IEEE.

关键词： Generative adversarial networks

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Land Surface Temperature Classification Using Machine Learning Algorithms for the region of Kolli Hills, Tamil Nadu, India 10

Land Surface Temperature Classification Using Machine Learni...

引用

10th International conference on Communication and signal processing, ICCSP 2024

作者： Kumar, K. Sathish Navin, M. Sam Karunya Institute of Technology and Sciences School of Computer Science and Technology Coimbatore India

ISBN: (纸本)9798350353068

Land Use Land Cover change is considered an exciting topic in earth sciences and information technology. The real-time multispectral satellite images captured for finding the variations in the Earth's surface can be explored through remote sensing and Geographic Information Systems. In our work, the Land Surface Temperature image was extracted using the thermal bands of the Landsat-9/8 satellite, and the temperature values were computed for the Kolli Hills, India spanning the years 2021, and 2024. This work provides novel information about the Land Surface Temperature classification using the Clustering-based maximum likelihood machine learning model. The accuracy of the work was calculated with the assistance of Google Earth Engine time series images. The random sampling method was applied to validate the correctness of the acquired maps and the result shows an overall accuracy of 96.484%. The significance of this work lies in providing Land Use Land Cover information for time series data, supporting land resource organizers in preserving forest-covered areas in hilly regions. © 2024 IEEE.

关键词： remote sensing

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image Restoration Based on Blur Kernel Estimation Using Vibration Data 8

Image Restoration Based on Blur Kernel Estimation Using Vibr...

引用

8th International conference on signal and image processing, ICSIP 2023

作者： Xing, Weida Tuo, Weixiao Li, Xingfei Yang, Chenxi Tianjin University State Key Laboratory of Precision Measuring Technology and Instruments Tianjin China Taihu Laboratory of Deepsea Technological Science Wuxi China

ISBN: (纸本)9798350397932

remote sensing images are usually blurred due to platform vibrations and camera defocus, which seriously limit its application. To solve the problem of blurring remote sensing images caused by in-orbit angular vibration, an image restoration approach is proposed. Magnetohydrodynamics (MHD) angular rate sensor is used to measure the angular vibration experiencing by the camera. The blur kernel is estimated with detected vibration and defocusing radius. A regularized image deblurring algorithm with total variation (TV) regularization is used to restore the blurred image. The modulation transfer function (MTF) is used to evaluate quality of the restored image. Simulations and experiments are conducted to validate the proposed approach. Results indicate that the proposed method can improve the image quality while preserving the detail information. © 2023 IEEE.

关键词： image reconstruction

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SSTNet: spatial-spectral transformer network for panchromatic sharpening

SSTNet: spatial-spectral transformer network for panchromati...

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2024 International conference on image, signal processing, and Pattern Recognition, ISPP 2024

作者： Wang, Shuxiang Jin, Fei Rui, Jie Lin, Yuzhun Niu, Yueqin Information Engineering University China Shan Dong Agriculture and Engineering University China

ISBN: (纸本)9781510680425

In the field of remote sensing, panchromatic sharpening technology integrates spatial data from panchromatic images with spectral data from multispectral images to generate high-resolution multispectral *** mapping from multispectral to single-band panchromatic images greatly impacts the quality of fusion *** paper introduces the spatial-spectral transformer network (SSTNet).SSTNet combines the spatial constraints of the difference model in the gradient domain and the intensity constraints in the spectral domain to quantitatively express multispectral imagery into panchromatic imagery in a many-on-one form, laying the groundwork for designing the loss function in image *** to the original method, SSTNet is applied to *** on the Quickbird dataset demonstrate that the evaluation indexes of the SSTNet-based P2Sharpen method are improved in both reduced and full resolution resolution, requiring fewer training data and epoch. © 2024 SPIE.

关键词： image fusion

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Multi-scale Rotating Target Detection in High Resolution remote sensing image Based on Improved YOLOv5

Multi-scale Rotating Target Detection in High Resolution Rem...

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2023 International conference on image, signal processing, and Pattern Recognition, ISPP 2023

作者： Li, Jiapeng Wu, Jiaqi Liu, Qi Zhang, Zheng North China University of Technology Beijing100144 China China Institute of Water Resources and Hydropower Research Beijing100038 China

ISBN: (纸本)9781510666351

As the remote sensing image information rapidly becomes abundant, it is a challenge for the detection of tiny targets with dense distribution. Therefore, a multi-scale rotating object detection model based on the improved YOLOv5 is proposed in this paper. Firstly, because of adding a prediction feature layer to the network, the detection precision of tiny targets has substantially increased. Secondly, the loss between rotation anchors can be fitted at a high precision due to the compressed loss function aiming at the calculation of the IoU loss of rotation is proposed. Finally, a hybrid prior bounding box strategy is applied in the feature prediction layer to suit the targets to be detected in different sizes. Experiments conducted on the DOTA dataset indicates that this method significantly exceeded the original YOLOv5. It has extraordinary performances for the task of object detection in optical remote sensing image fields. © 2023 SPIE.

关键词： Object detection

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