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

共 3584 条记录，以下是31-40 订阅

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INVESTIGATING AND REDUCING THE IMPAIRMENT OF POINT SPREAD EFFECT FOR SPATIOTEMPORAL FUSION OF remote sensing imageRY 31

INVESTIGATING AND REDUCING THE IMPAIRMENT OF POINT SPREAD EF...

引用

2024 International conference on image processing

作者： Li, Yunfei Li, Jun Sun Yat Sen Univ Sch Geog & Planning Guangzhou 510275 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430078 Peoples R China

ISBN: (纸本)9798350349405;9798350349399

Spatiotemporal fusion of remote sensing imagery is a technology aiming to provide the synthetic dense satellite image series with medium spatial resolution. Presently, many spatiotemporal fusion approaches have been proposed. However, for the purpose of easier modeling, most approaches ignore the point spread effect of images with different spatial resolutions in the fusion, which may lead to impairment of the fusion performance. To address this problem, we develop a new method to reduce the impairment for spatiotemporal fusion, called dePSE. Specifically, the dePSE utilizes an adaptive flat gaussian kernel to learnt he point s pread function between the medium and coarse resolution images from the base medium and coarse image pair, which is then used to decompose the coarse resolution surface changes and reconstruct the coarse resolution images with higher quality. Finally, the reconstructed ones will replace the original ones to achieve the fusion. To validate the necessity and effect of the dePSE method, our experiments firstly investigate the impairment of point spread effect for spatiotemporal fusion using the simulated dataset, then test its performance using the real Landsat-MODIS dataset. The experimental results indicate that the point spread effect will lead to serious impairment for spatiotemporal fusion, including spatial distortions and accuracy decrease, which should be taken into account when designing new spatiotemporal fusion approaches. On the other hand, the proposed dePSE method can successfully reinforce the fused images via reducing the impairment of the point spread effect.

关键词： remote sensing Spatiotemporal Fusion Point Spread Effect

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A fusion algorithm of SAR and panchromatic remote sensing images for intertidal extraction

A fusion algorithm of SAR and panchromatic remote sensing im...

引用

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

作者： Li, Wenchao Liu, Sheng Ding, Hao He, Liang Chen, Zhenhua Li, Qingliang Xue, Kaichuang China Wenchang Spacescraft Launch Site 799 Shugang Ave Hainan Wenchang471300 China National University of Defense Technology 109 Deya Road Hunan Changsha410073 China

ISBN: (纸本)9781510680425

Intertidal terrain extraction based on satellite remote sensing images is of great military application and civilian value. In this paper, we propose a fusion algorithm based on SAR and optical remote sensing image to enhance intertidal feature. With the SAR and panchromatic images from different phases, we use an improved shift-invariant discrete wavelet transform algorithm to obtain higher quality images that can not only highlight the shape of the intertidal zone, but also highlight the characteristics of the coastline. The real data experiment and comparison experiments with other algorithms show that the algorithm proposed in this paper is effective in enhancing the features of the intertidal zone. © 2024 SPIE.

关键词： Optical remote sensing

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DiffRS: An Extensible Diffusion Model for remote sensing image Generation

DiffRS: An Extensible Diffusion Model for Remote Sensing Ima...

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2025 IEEE International conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Huang, Xinyue Niu, Xin Jiang, Jingfei Pan, Hengyue National University of Defense and Technology Changsha China

ISBN: (纸本)9798350368741

remote sensing image generation is of great value for virtual environment creation and adversarial learning for fake news detection. It could also address the learning sample shortage in the region of interest. However, most current image generation methods are limited to producing images of fixed sizes, few studies on extensible natural image generation largely focus on the stitching of random contents, lacking effective exploration of contextual information, which weakens the coherence of the extended images. To address this problem, we propose an extensible generation method for remote sensing images with the model DiffRS. This approach allows for sequential extension of arbitrary sizes by exploring the generated neighboring regions. The method is particularly suitable for scenes like remote sensing images where a generation block could cover multiple independent targets, rather than natural image tasks which may stitch across regions to form a completely target. Compared to the state-of-the-art extensible generation methods, DiffRS could improve the large scale image generation with better structure consistency, richer details and higher realism. Experiments showed that DiffRS could improve the FID score by 4.6% and 3.2% respectively in comparison with the MultiDiffusion and Mixture of Diffuser models. © 2025 IEEE.

关键词： Diffusion Model image Extensible Generation remote sensing image Sequence Generation

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Patch-based Cross-domain Adaptation for Few-shot remote sensing Scene Recognition 2

Patch-based Cross-domain Adaptation for Few-shot Remote Sens...

引用

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

作者： He, Chengzhi Liu, Yu Jiang, Zhizhuo Li, Yaowen Yan, Chenggang Zheng, Bolun Tsinghua University Shenzhen International Graduate School Shenzhen China Tsinghua University Department of Electronic Engineering Beijing China Hangzhou Dianzi University Intelligent Information Processing Laboratory Hangzhou China

ISBN: (纸本)9798331515669

Recognizing new classes from a few labeled remote sensing images presents a significant challenge. Existing methodologies assume that training and testing datasets come from identical domains. Collecting training datasets of remote sensing images from each domain is practically unfeasible. To tackle this challenge, we propose a strategy that leverages image patch feature embedding and cross-adaptation mechanism for cross-domain few-shot classification. Initially, the supplied image is segmented into patches of the same size. Subsequently, feature embeddings are extracted and reinforced with prototypical triplet loss to enhance robustness. A unified module then facilitates cross-adaptation between the support and query sets, producing classification features that effectively represent the relationship between them, thus better adapting to few-shot remote sensing classification challenges with domain shifts. Our proposed method's excellence and generality are confirmed by comprehensive experiments. © 2024 IEEE.

关键词： Jurassic

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Few-Shot Optical remote sensing Object Detection Based on Shape Matching 2

Few-Shot Optical Remote Sensing Object Detection Based on Sh...

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

作者： Wu, Zhihui Yuan, Hong-Fang Ma, Fei Zhang, Fan Beijing University of Chemical Technology College of Information Science and Technology Beijing China

ISBN: (纸本)9798331515669

Traditional object detection methods suffer from excessively high false alarm rates in scenarios with scarce training samples. To address this issue, this paper proposes a few-shot optical remote sensing object detection method based on shape matching. This method first obtains the sliced mask image of the object within the predicted bounding box in the pre-detection process. Then, the shape context algorithm is utilized to perform template matching between the template image and the sliced mask image, thereby eliminating false alarm objects and enhancing the object detection performance. To examine the capability of the proposed method, two public datasets, namely FAIR1M datasets and MAR20 datasets, are selected for experimentation. The experimental results show that our method achieves exceptional effectiveness, yielding F1 scores of 0.705 and 0.866 on the FAIR1M and MAR20 datasets, respectively. © 2024 IEEE.

关键词： Optical remote sensing

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PROGRESSIVE REFINEMENT LEARNING BASED ON FEATURE INTERACTIVE FUSION FOR SEMANTIC SEGMENTATION OF remote sensing LIMITED DATASET 30

PROGRESSIVE REFINEMENT LEARNING BASED ON FEATURE INTERACTIVE...

引用

30th IEEE International conference on image processing (ICIP)

作者： Lyu, Xinran Zhang, Libao Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China

ISBN: (纸本)9781728198354

Due to the labor cost and the accuracy of manual identification, it is very difficult to make a strong label dataset of remote sensing images with a large amount of data. Therefore, the limited remote sensing dataset has become a research hotspot in recent years. However, due to insufficient precision and the lack of label accuracy, these methods often have insufficient expression ability. In this paper, we proposed a semantic segmentation method for remote sensing images by progressive refinement learning. Firstly, we construct multiple classification networks to vote for label noise cleaning, and select a network to retrain. Then, the method based on hierarchical feature learning is used to realize the pixel-level pseudo label calculation. Secondly, we proposed to construct feature interactive fusion module in the multi-level codec to achieve image group semantic segmentation. Comprehensive evaluations and the comparison with 7 methods validate the superiority of the proposed model.

关键词： remote sensing limited dataset progressive refinement learning feature interactive fusion

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Application Research of On-Board Satellite remote sensing image Terrain Classification Based on Deep Learning 11th

Application Research of On-Board Satellite Remote Sensing Im...

引用

11th International conference on signal and Information processing, Network and Computers, ICSINC 2023

作者： Tian, Hexiang Zhang, Jingya Zhao, Wenrui Institute of Remote Sensing Satellite CAST Beijing100094 China

ISBN: (纸本)9789819721238

Aiming at the real-time on-board intelligent decoding/translating need of the satellite remote sensing image, the article raises up a system structure of satellite remote sensing image terrain classification based on the combination of terrain servers and on-board AI analyzing platform, and designs an on-board satellite remote sensing image terrain classification algorithm based on deepabv3+. Then, the optimizing solution satisfies the constraints of the performance and the storage of the on-board intelligent information system is offered. Tested by on-board AI analyzing platform built on land, the solution is proved to be able to satisfy the on-board real-time processing need, the on-board image decoding/translating speed and accuracy requirements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Satellites

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TRANSFORMATION CONSISTENCY FOR remote sensing image SUPER-RESOLUTION 30

TRANSFORMATION CONSISTENCY FOR REMOTE SENSING IMAGE SUPER-RE...

引用

30th IEEE International conference on image processing (ICIP)

作者： Deng, Kai Yao, Ping Cheng, Siyuan Bi, Junyu Zhang, Kun Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China INRIA Saclay Ile De France Palaiseau France Inst Polytech Paris Paris France

ISBN: (纸本)9781728198354

Single image Super-Resolution (SISR) based on deep learning methods has been widely studied for applications on remote sensing images. With limited remote sensing images, most of the existing SISR methods simply adopt the regular data augmentation approaches (such as flip) in natural images to improve model performance. Considering the fact that remote sensing images are all taken from a bird's-eye view and objects appear in multiple directions, we first introduce rotation augmentation method in remote sensing images to promote diversity of samples dramatically, as rotation does not cause semantic problems like people standing upside down in natural images. However, image rotation at various angles implemented by interpolation will cause the inconsistent pixel distribution problem for the pixel level task. Thus, we propose Transformation Consistency Loss Function (TCLF) to narrow the gap between the augmented and original distribution, while expanding the feature space with rotation augmentation method. Extensive experiments are performed on UC-Merced Land-use dataset of 21 remote sensing scenes, and the results as well as ablation studies demonstrate our proposed method outperforms mainstream methods.

关键词： image processing Super-resolution Transformation consistency

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The application of Gaussian-Rayleigh mixture model in remote sensing image segmentation 9

The application of Gaussian-Rayleigh mixture model in remote...

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9th International conference on Intelligent Computing and signal processing, ICSP 2024

作者： Sun, Xiaoxue Yang, Yue Li, Yanhao Gao, Guoren Jilin Agriculture Science and Technology University Mechanical Engineering Department Jilin Jilin China State Grid Liaoning Extra High Voltage Company Liaoning Anshan China

ISBN: (纸本)9798350376548

This paper employs the Entropy-Max method to determine the optimal number of classes in an image, utilizes the Markov Random Field (MRF) method to complete the image segmentation, and addresses the computation of maximum a posteriori probability during the segmentation process using the Iterative Conditional Model (ICM). Three remote sensing images are used in the experiments, comparing the segmentation and fitting results using Gaussian Mixture Mode (GMM) and Gaussian-Rayleigh Mixture Model (GRMM). © 2024 IEEE.

关键词： image segmentation

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Compressive sensing based sparse representation analysis for remote sensing SAR imagery

Compressive sensing based sparse representation analysis for...

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2024 International conference on remote sensing, Mapping, and Geographic Information Systems, RSMG 2024

作者： Yin, Hang Wang, Jue Wen, Xinyi Xie, Xinran Space Engineering University Beijing101407 China

ISBN: (纸本)9781510685826

The signal processing technology of Compressive sensing (CS) overturns the limitation of the traditional sensing method that only high sampling rate can achieve high resolution. The compressive sensing method saves internal storage and resources of information processing due to the small amount of data processed. Applying CS to spaceborne Synthetic Aperture Radar (SAR) system can effectively reduce the pressure of hardware platform. Sparse representation of SAR image is a prerequisite for CS application in remote sensing SAR imagery, which is analyzed quantitatively under different orthogonal bases and representation approach. The effect analysis is measured by the evaluation indexes such as peak signal-to-noise ratio, structural similarity and edge retention index. The simulation results show that the effect of sparse representation can meet the quality and structure requirements of SAR images in most cases. The conclusion verifies that SAR images satisfy CS requirement for sparsity, which supports the further study of CS reconstruction application in spaceborne SAR image. © 2024 SPIE.

关键词： Digital storage

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