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

共 3585 条记录，以下是151-160 订阅

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A GPU-accelerated Algorithm for Copy Move Detection in large-scale satellite images 29

A GPU-accelerated Algorithm for Copy Move Detection in large...

引用

conference on image and signal processing for remote sensing XXIX

作者： Barni, Mauro Costanzo, Andrea Dimitri, Giovanna Maria Tondi, Benedetta Univ Siena DIISM Via Roma 56 I-53100 Siena Italy

ISBN: (纸本)9781510666955;9781510666962

With the diffusion of advanced image editing software, image manipulation is becoming an impelling aspect also for satellite images. In a copy-move (CM) forgery, part of the image is copied and pasted elsewhere into the same image. In the satellite domain, CM can be performed with the intent of propagating misleading information on the geography and morphology of the landscapes pictured in the images. The best algorithms for CM detection rely on a multi-step procedure involving extraction of image descriptors (keypoints), keypoint matching and finally clustering, for the localization of the forged area. The large size of many satellite images and their richness of details, often prevent the adoption of off-the-shelf tools developed for multimedia images. Due to the large number of keypoints typically present in satellite images, in fact, the computational complexity and memory requirements for SIFT keypoints extraction, matching, clustering and forgery localisation is prohibitive. In this paper, we propose a CM detection algorithm that can successfully process very high resolution satellite images, where off-the-shelf alternatives are crashing due to system memory exhaustion. The proposed algorithm is based on three main strategies powered by GPU acceleration: i) multi-threaded tile-based SIFT keypoints extraction, ii) optimised batch-based descriptors matching, iii) clustering and localisation of manipulated pixels exploiting tensors instead of a sliding window approach. Experiments carried out on images belonging to the ESA WorldView-2 European Cities dataset and on a set of hand-made copy-move forgeries with resolution above 1 Gigapixel, show the good performance of the proposed algorithm in terms of processing time and memory consumption.

关键词： Copy Move detection Satellite image forgery detection remote sensing image Forgery

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A remote sensing image Object Detection Algorithm Based on a Fusion of Classification and Regression Branches 8

A Remote Sensing Image Object Detection Algorithm Based on a...

引用

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

作者： Li, Zejun Zhou, Danshu Liu, Jian Wu, Nanjian Liu, Liyuan Dou, Runjiang University of Chinese Academy of Sciences Center of Materials Science and Optoelectronics Engineering Beijing100049 China Institute of Semiconductors State Key Laboratory of Superlattices and Microstructures Chinese Academy of Sciences Beijing100083 China University of Chinese Academy of Sciences College of Integrated Circuits Beijing100049 China

ISBN: (纸本)9798350397932

The detection head of most object detection algorithms obtains the category information and location information through classification branch and regression branch, respectively. Usually, the two branches do not influence each other, therefore neither the positional information implied in the classification branch, nor the classification information implied in the regression branch is used. In this paper, we redesign the traditional detection head to solve the above problems. The mAP of our method in the public remote sensing image object detection dataset DOTA-v1.0 reaches 77.27%, while the mAP of baseline (RoI Transformer) is only 75.43% under the same training conditions. The test results suggest that the detection head branch fusion algorithm can improve the performance of the remote sensing image object detection model. © 2023 IEEE.

关键词： Classification (of information)

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Multi-model image matching based on feature map matching method 8

Multi-model image matching based on feature map matching met...

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8th International conference on Intelligent Computing and signal processing, ICSP 2023

作者： Qi, Wei Lang, Wenhui School of Computer and Information Hefei University of Technology Hefei China

ISBN: (纸本)9798350302455

In the field of remote sensing, multimodal data matching is a problem with great challenges, and the matching between SAR and visible light images is tremendously difficult, because there is obvious speckle noise in SAR images, and the imaging methods of SAR images and optical images are very different. At present, although lots of excellent results have been fulfilled in multimodal image matching, it is difficult to meet the rapid and even real-time processing requirements of massive remote sensing data such as disaster emergency and military applications due to the complexity of the matching task. In view of the above problems, this paper proposes a feature map matching method based on the deep convolution neural network model of the pseudo-Siam architecture, which can quickly accomplish the multimodal image matching. © 2023 IEEE.

关键词： image matching

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SMALL OBJECT DETECTION ON THE WATER SURFACE BASED ON RADAR AND CAMERA FUSION 49

SMALL OBJECT DETECTION ON THE WATER SURFACE BASED ON RADAR A...

引用

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

作者： Wei, Qiancheng Jiang, Xiaoping Liu, Ying Su, Qiya Yu, Muyao Univ Chinese Acad Sci Beijing Peoples R China Beijing Inst Remote Sensing Equipment Beijing Peoples R China

ISBN: (纸本)9798350344868;9798350344851

With the growing applications of water operations, water surface object detection tasks are facing new challenges. In this paper, we focus on improving the performance of water surface small object detection. Due to the limitations of singlesensor in water environments, we propose RCFNet, a novel small object detection method based on radar-vision fusion. RCFNet fuses features captured by radar and camera in multiple stages to generate more effective target feature representations for small object detection on water surfaces. In particular, we propose a multi-frame radar feature fusion module and an image-guided radar feature enhancement module to enhance the radar features. This method fully utilizes radar and camera data information, improving the performance of small object detection on water surfaces. RCFNet was evaluated on the publicly released water surface floating dataset Flow, achieving an Average Precision (AP50) of 0.9316, which is a state-of-the-art result.

关键词： object detection sensor fusion attention transformer

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Research on Blind Restoration Algorithm of Motion Blurred remote sensing image 10th

Research on Blind Restoration Algorithm of Motion Blurred Re...

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10th International conference on signal and Information processing, Network and Computers, ICSINC 2022

作者： Yang, Hanwen Liu, Yanran Luan, Li Wang, Yunsen Wang, Haoxuan Shanghai Jiao Tong University Shanghai200240 China Shandong Institute of Space Electronic Technology Shandong Yantai264670 China

ISBN: (纸本)9789811999673

The reconstruction of motion blur is a significant subject in remote sensing image processing. It has a great effect on the follow-up processes of target detection and recognition. To meet the needs of on-board intelligent processing, a new blind restoration method based on Radon transform and autocorrelation function is proposed to process remote sensing image blurred by motion. The method adopt frequency spectrum diagram and Radon transform to calculate the direction of motion blur image. Autocorrelation function is quoted to calculate the blur scale. After the point spread function is estimated, Wiener filter is applied to realize the restoration of motion blurred images. By rotating blurred image to the horizontal axis direction, the two-dimensional restoration problem is converted into one dimension. Test results indicate that the method can obtain satisfactory recovering results of remote sensing image blurred by linear motion. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image reconstruction

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Road Detection from remote sensing images using Machine Learning 9

Road Detection from Remote Sensing Images using Machine Lear...

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9th International conference on signal processing and Communication, ICSC 2023

作者： Kathiya, Pruthvirajsinh A. Sindhav, Kashyap Gajjar, Ruchi Dept of Electronics and Communication Engineering Institute of Technology Nirma University Gujarat Ahmedabad India

ISBN: (纸本)9798350383201

Road detection from remote sensing images is vital in various applications, such as urban planning, transportation management, and navigation systems. In this work, we propose a methodology for road detection using deep learning architectures, including ResNet, U-Net, DenseNet, and MobileNet, with the DeepGlobe dataset. This work aims to accurately identify and delineate road pixels within remote sensing imagery. The objective is to compare the performance of different models and determine the most influential architecture for road detection. The proposed methodology involves data preprocessing, implementing the deep learning architectures, training the models, and evaluating their performance using metrics such as Intersection over Union (IoU), Structural Similarity Index (SSIM), and Peak signal-to-Noise Ratio(PSNR). Experimental results show that ResNet achieves the highest accuracy, followed by U-Net, DenseNet, and MobileNet. Future work includes exploring multi-segmentation for detecting other features in remote sensing imagery, ensemble models, transfer learning, addressing occlusions and environmental variability, and efficient inference. By leveraging deep learning architectures and the DeepGlobe dataset, this work contributes to accurate road detection in remote sensing images, enabling improved urban and transportation planning decision-making. © 2023 IEEE.

关键词： image segmentation

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Single Band NIR-to-RGB image Colorization using Attention Guided Conditional GAN

Single Band NIR-to-RGB Image Colorization using Attention Gu...

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IEEE Space, Aerospace and Defence conference (SPACE)

作者： Songade, Moordhan Arya, Sunita Moorthi, S. Manthira Malaviya Natl Inst Technol Dept Comp Sci & Engn Jaipur Rajasthan India ISRO Space Applicat Ctr Signal & Image Proc Area Ahmadabad Gujarat India

ISBN: (纸本)9798350367393;9798350367386

NIR images, capturing light beyond visible spectrum, are useful in remote sensing, astronomy, etc. Converting them to RGB format makes them better to interpret for humans. This paper introduces a novel approach for NIR to RGB image colorization using GAN. Our primary contribution is a Conditional Generative Adversarial Network (cGAN) with an enhanced Attention UNet serving as its generator. With this model, we have achieved an Angular Error of 15.55, PSNR of 16.41 and SSIM of 0.69 which is comparable to the other prepared models' like pix2pix & Attention UNet as well as more visually appealing than all of them.

关键词： image Colorization Near-Infrared image Red-Green-Blue image Attention UNet Generative Adversarial Network

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Learning Deep Frequency Degradation Prior for remote sensing Spatio-temporal Fusion

Learning Deep Frequency Degradation Prior for Remote Sensing...

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

作者： Bian, Yiting Hu, Shenglong Song, Huihui Zhang, Kaihua Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9798350368741

Existing deep learning-based remote sensing spatiotemporal fusion (STF) relies on a data-driven paradigm without considering the degradation prior modeling from the coarse-to fine-resolution images. This makes the learned model easy to overfit to the training dataset, resulting in poor domain generalization across different datasets. To this end, this paper presents a deep frequency degradation prior to STF, dubbed as DeepFDP. The DeepFDP is based on a statistical observation that the frequency feature distributions of the tokens from the coarse- and fine-resolution images in different datasets have a low intra-resolution variance and a high inter-resolution variance with compact well-separated clusters. Therefore, the DeepFDP first designs a frequency proximity module to learn the mapping function between the token frequency representations of the coarse- and fine-resolution images, which is to narrow their feature distribution difference. Since the statistical properties of the token frequency representations are independent of the land-cover classes, the DeepFDP can be learned on a training set of limited image pairs without extra-supervision signals, which has a favorable zero-shot generalization capability across different datasets. Then, to faithfully recover the land-surface details, a high-frequency feature modulation module is designed that uses the fine-resolution image as guidance to progressively learn the multi-scale residual features in a coarse-to-fine fashion, yielding the fused features with rich high-frequency details. Finally, the progressively-fused features at each stage are fed into a hybrid fusion module, yielding the fine-resolution image prediction. Extensive evaluations on LGC and CIA datasets demonstrate favorable performance of the DeepFDP over state-of-the-art methods. Especially, the DeepFDP also shows a good zero-shot generalization performance when training on LGC and testing on CIA. © 2025 IEEE.

关键词： domain generalization frequency degradation prior remote sensing Spatio-temporal fusion

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Diffusion Posterior Sampling for remote sensing image Fusion 7

Diffusion Posterior Sampling for Remote Sensing Image Fusion

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7th International conference on Vision, image and signal processing, ICVISP 2023

作者： Zhang, Chenlin Wang, Zelong Han, Jialing College of science National University of Defense Technology Changsha China

ISBN: (纸本)9781839539848

remote sensing image fusion, i.e., fusing remote sensing images from different sensors or different time into a comprehensive image, can integrate image information for all kinds of image tasks, such as object detection, pattern recognition, and so on. In this paper, we focus on the image fusion of optical image and synthetic aperture radar (SAR) image, where the traditional methods, such as sparse representation and image decomposition, usually fail to improve the image quality and enhance object features’ level. Inspired by the powerful generation model named diffusion denoising probabilistic model (DDPM), we propose a novel method based on diffusion posterior sampling for image fusion of optical image and SAR image. Starting from the variational model of image fusion and the total variation constraint, we approximate the posterior sampling process of image fusion by a closed-form analytic solution. After that, DDPM can be used to generate the fused image with high image quality and enhanced object features. The feasibility and the superiority of the proposed method is validated in numerical experiments. © The Institution of Engineering & Technology 2023.

关键词： image fusion

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An Unsupervised Domain Adversarial Wavelet Learning Method for registration of Optical and SAR Heterogeneous remote sensing images 2

An Unsupervised Domain Adversarial Wavelet Learning Method f...

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

作者： Ye, Hongze Zhang, Shenfu Li, Liangzhi Meng, Xiangchao Ningbo University School of Information Science and Engineering Ningbo China Chang'an University College of Geological Engineering and Geomatics Xi'an China

ISBN: (纸本)9798331515669

Optical images and synthetic aperture radar (SAR) remote sensing images are highly complementary information for Earth observation and applications. Accurate registration of these two data types is essential for subsequent collaboration processing and analysis. However, traditional methods struggle to achieve effective registration due to nonlinear radiometric disparities and geometric distortions. Meanwhile, supervised deep learning approaches require costly manual annotations. To address these issues, this paper proposes an unsupervised registration method based on domain adversarial wavelet learning. We innovatively design an inter-domain adversarial mutual transfer (IAT) module to alleviate cross-modal differences while preserving structural features. Additionally, wavelet transform is employed at different scales for feature matching, to mitigate radiometric disparities and enhance feature recognizability. By incorporating an attention mechanism, the network structure is optimized through spatial alignment and image similarity. The proposed method outperforms other state-of-the-art methods in both quantitative and qualitative aspects of benchmark datasets. © 2024 IEEE.

关键词： image registration

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