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9th International conference on signal and image processing (ICSIP)

作者： Li, Dongrui Zhao, Zhichun Shenzhen MSU BIT Univ Fac Engn Shenzhen Peoples R China

ISBN: (纸本)9798350350920

Accurate classification and identification of vessels in remote sensing satellite imagery is critical for ocean monitoring and resource management. The ability to extract information from remote-sensing data is of paramount importance. To exploit the non-stationary characteristics of synthetic aperture radar (SAR) target, a comprehensive SAR ship recognition framework is designed by combing the second-order synchrosqueezing transform (SST), an effective non-stationary signal processing tool, with the histogram of oriented gradient (HOG) feature in this paper. Firstly, the second-order SST is performed on SAR images to describe the non-stationary characteristics of ships at different times and frequencies. Secondly, HOG features are utilized to effectively extract the non-stationary information of SAR ships and provide more discriminative input for the deep learning network. Then, the optimal ResNet model is selected as the convolutional neural network (CNN) classifier to automatically fuse the non-stationary features and abstract features of SAR ships. Experiments on two open SAR ship datasets (OpenSARShip and FUSAR-Ship) show that the proposed method achieves accurate classification and outperforms the state-of-the-art (SOTA) CNN-based methods in terms of robustness and generalization ability. The positive effect of non-stationary characteristics on SAR ship classification is verified.

关键词： Synthetic aperture radar (SAR) ship classification non-stationary signal processing time-frequency analysis (TFA) histogram of oriented gradient (HOG) convolutional neural network (CNN)

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Target point alignment with Gaussian mixture models 16

Target point alignment with Gaussian mixture models

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16th International conference on signal processing Systems, ICSPS 2024

作者： Li, Chuchen Zhao, Bingqing Tang, Shanjun Li, Xing Liao, Xin Shanghai Electro-Mechanical Engineering Institute Shanghai201109 China

ISBN: (纸本)9781510689251

Under the remote sensing perception scenario, the sensor field of view alignment is the key step for global information acquisition. However, the model can only utilize position information for alignment, as the remote sensing image has limited resolution and the size of targets is relatively small. And because of inevitable noise and complex background information, false alarms are difficult to eliminate. Moreover, the number of targets that a remote sensor can capture can be scarce compared with the general point registration task. With all these problems, precisely aligning remote sensing images that contain similar targets is extremely difficult. In this paper, we propose Gaussian mixture models with shape and false alarm sensitive strategy to tackle the limited resolution remote sensing image alignment task. We first apply shape context as a structure description to add intrinsic information to our model. Then we introduce a false alarm sensitive strategy to refine the information from the shape context to further enhance the alignment ability of the model. We simulate different target structures and conditions as our evaluation dataset. Thorough experimental results show the effectiveness of our proposed method. © 2025 SPIE.

关键词： Optical remote sensing

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remote sensing image super-resolution reconstruction using depth-dense residual and sub-pixel convolutional model

Remote sensing image super-resolution reconstruction using d...

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

作者： Liu, Haoguang Fu, Yuwen Wang, Zhoujie Chinese Flight Test Establishment Xian China

ISBN: (纸本)9781510680425

Existing super-resolution reconstruction algorithms for remote sensing images often struggle to fully extract and utilize features in complex scenes, and the reconstruction results are not optimal due to the influence of *** propose a reconstruction network model that combines deep dense residual module and sub-pixel *** model connects multiple dense residual modules through recursive linking and introduces a channel attention mechanism to extract multiscale features from the *** the feature reconstruction process, a sub-pixel convolution structure is introduced to reduce the impact of noise and enhance reconstruction *** model is tested on the UC Merced Land Use public dataset, and the results demonstrate that the reconstruction results of our proposed model algorithm have better peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) at different scales compared to the current mainstream reconstruction algorithm *** is proven that the proposed model can significantly improve the reconstruction quality through comparative analysis experiment, meeting the needs for high-resolution remote sensing image processing. © 2024 SPIE.

关键词： image reconstruction

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A Coarse-to-Fine Change Detection Method for remote sensing Sparse Cultivated Land

A Coarse-to-Fine Change Detection Method for Remote Sensing ...

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2024 Asia Pacific signal and Information processing Association Annual Summit and conference

作者： Hu, Yuan Zhang, Yifan Ma, Mingyang Mei, Shaohui Northwestern Polytech Univ Xian 710072 Peoples R China

ISBN: (纸本)9798350367331;9798350367348

remote sensing (RS) images contain rich geographic information. For specific application scenarios like cultivated land, it is necessary to select areas of interest to reduce data scale and focus on detailed features. In this article, an innovative coarse-to-fine change detection method (CFCD) for sparse cultivated land is proposed to address these problems. Coarse screening module (CSM) first removes irrelevant low-difference image pairs, and then fine detection module (FDM) accurately locate change areas in remaining images. Experimental results show that two coarse screening methods can take out many disturbed images, and provide strong support for subsequent fine detection methods to achieve performance improvement.

关键词： Change detection

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Research on SAR image and Optical image Fusion Classification Based on Deep Learning 2

Research on SAR Image and Optical Image Fusion Classificatio...

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

作者： Wu, H. Li, C.H. Tong, G.L. Mnr School of Aerospace Engineering Land Satellite Remote Sensing Application Center Beijing China Mnr Land Satellite Remote Sensing Application Center Satellite Planning Department Beijing China Mnr The Fourth Topographic Surveying Team Technical Quality Management Department Harbin China

ISBN: (纸本)9798331515669

In order to better integrate and classify multi-source image features, we constructed a multi-source remote sensing data feature extraction and fusion classification framework U-DSDNet based on a combination of dual channel convolutional neural networks and self attention. Build two independent channel networks to extract feature information. Introducing self attention mechanism to fuse multi-source image feature information, improving classification accuracy and stability. Meanwhile, we use Sentinel series satellite data to demonstrate the classification accuracy of U-DSDNet. Through comparative analysis with UNet, Deeplabv3+, and PSPNet, the experimental results show that in the SEN-OPT-SAR dataset, the overall accuracy of the U-DSDNet model is 3.8%, 0.2%, and 1.6% higher than the three models, respectively, verifying the superiority and effectiveness of the U-DSDNet model in land classification tasks. © 2024 IEEE.

关键词： Semantic Segmentation

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Language-Enhanced Dual-Level Contrastive Learning Network for Open-Set Hyperspectral image Classification

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2025年 63卷

作者： Qin, Boao Feng, Shou Zhao, Chunhui Li, Wei Tao, Ran Zhou, Jun Harbin Engn Univ Minist Ind & Informat Technol Coll Informat & Commun Engn Harbin 150001 Peoples R China Harbin Engn Univ Minist Ind & Informat Technol Key Lab Adv Marine Commun & Informat Technol Harbin 150001 Peoples R China Minist Nat Resources Technol Innovat Ctr South China Sea Remote Sensing Guangzhou 510300 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Griffith Univ Sch Informat & Commun Technol Nathan Qld 4111 Australia

In recent years, language-supervised vision models have demonstrated impressive potential in learning open-world concepts. Some research has introduced this learning paradigm to the hyperspectral image (HSI) processing domain;however, there has been limited work integrating textual information into the hyperspectral open-set recognition task. To fill this gap, we leverage textual supervision information in open-set HSI classification (HSIC) and propose a language-enhanced dual-level contrastive learning network (LDCLNet). Specifically, we introduce a linguistic mode with prior knowledge as a supervised signal to enhance the metric distances between closed-set samples and provide supplementary semantic information for open-set samples. Second, a dual-level visual-language (v-L) contrastive learning (CL) approach, which can align visual and language embeddings separately at the instance level and manifold level, is proposed to establish a more accurate link between visual and language representations. Finally, a distance-refined open-set recognition method is proposed, which aims to effectively discover unknown class samples during testing by refining predictions of known and unknown classes. Extensive experiments and analysis on three public HSI datasets validate the effectiveness of LDCLNet.

关键词： visualization Training Testing Feature extraction Hyperspectral imaging Contrastive learning Artificial intelligence Semantics image classification Computational modeling Dual-level contrastive learning (CL) hyperspectral image (HSI) language-supervised vision model open-set classification

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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...

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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...

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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...

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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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