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

共 3575 条记录，以下是81-90 订阅

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SAR2NDVI: PRE-TRAINING FOR SAR-TO-NDVI image TRANSLATION 49

SAR2NDVI: PRE-TRAINING FOR SAR-TO-NDVI IMAGE TRANSLATION

引用

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

作者： Kimura, Daiki Ishikawa, Tatsuya Mitsugi, Masanori Kitakoshi, Yasunori Tanaka, Takahiro Simumba, Naomi Tanaka, Kentaro Wakabayashi, Hiroaki Sampei, Masato Tatsubori, Michiaki IBM Res Yorktown Hts NY 10598 USA IBM Japan Ltd Minato City Tokyo Japan IBM Japan Digital Serv Co Tokyo Japan Space Shift Inc Tokyo Tokyo Japan

ISBN: (纸本)9798350344868;9798350344851

Geospatial machine learning is of growing importance in various global remote-sensing applications, particularly in the realm of vegetation monitoring. However, acquiring accurate ground truth data for geospatial tasks remains a significant challenge, often entailing considerable time and effort. Foundation models, emphasizing pre-training on large-scale data and fine-tuning, show promise but face limitations when applied to geospatial data due to domain differences. Our paper introduces a novel image translation method, combining geospatial-specific pre-training with training and test-time data augmentation. In a case study involving the translation of normalized difference vegetation index (NDVI) values from synthetic aperture radar (SAR) images of cabbage farms, our approach outperformed competitors by 31% in a public competition. It also exceeded the average of the top five teams by 44%. We publish both our image translation method with baseline methods and the geospatial-specific dataset at https://***/IBM/SAR2NDVI.

关键词： Geospatial image translation Pre-training

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Design and Verification of Super-Resolution Reconstruction Algorithm for remote sensing image Based on PSF Estimation 11th

Design and Verification of Super-Resolution Reconstruction A...

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

作者： Qiu, Xinyi Yin, Yanhe Cao, Haiyi Zhang, Xinwei Huang, Jin Mo, Fan Fan, Lijia Institute of Remote Sensing Satellite CAST Beijing100094 China

ISBN: (纸本)9789819721238

To improve the spatial resolution of remote sensing images, an improved Projections onto Convex Sets (POCS) super-resolution algorithm based on Point Spread Function (PSF) estimation is proposed. Obtaining PSF from system design or remote sensing image, we use it as a projection operator to simulate the real degradation process of the satellite imaging system, and improve the reconstruction accuracy. To evaluate its performance, we compared it with the classic POCS algorithm, the PSNR/SSIM of the super-resolution image reconstructed by our algorithm is improved from 24.32dB/0.77 to 25.73dB/0.82, and the visual effect of the image is significantly enhanced. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Optical transfer function

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Jitter error detection and compensation method based on H-infinity filter fusion for remote sensing satellite image 2

Jitter error detection and compensation method based on H-in...

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

作者： Tian, Yingqi Yu, Haojie Zhu, Ying Wuhan Institute of Technology School of Electrical and Information Engineering Wuhan China

ISBN: (纸本)9798331515669

Satellite jitter is a common phenomenon in high-resolution satellite applications, leading to image blurring, geometric distortion, difficulties in image stitching, and reduced spatial resolution. Therefore, accurately detecting satellite jitter and compensating for it in images is crucial. In this paper, we propose a novel framework for multispectral satellite jitter detection and compensation, which integrates the results of two different detection methods to enhance detection accuracy and effectively compensate for the images. First, dense matching is performed between the bands of the multispectral imagery to obtain the relative registration error between two bands;Then, dense control point matching is conducted between the single-band image and the corresponding Ortho-images. Next, a method based on the H-infinity filtering model is proposed to fuse the results of the two detection methods and compensate for the original image. Comparative experimental analysis shows that the compensation results after fusion exhibit significant improvements compared to those obtained using a single detection method. © 2024 IEEE.

关键词： remote sensing

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ROTATION XGBOOST BASED METHOD FOR HYPERSPECTRAL image CLASSIFICATION WITH LIMITED TRAINING SAMPLES 30

ROTATION XGBOOST BASED METHOD FOR HYPERSPECTRAL IMAGE CLASSI...

引用

30th IEEE International conference on image processing (ICIP)

作者： Feng, Wei Gao, Xinting Dauphin, Gabriel Quan, Yinghui Xidian Univ Sch Elect Engn Dept Remote Sensing Sci & Technol Xian 710071 Peoples R China Xian Key Lab Adv Remote Sensing Xian 710071 Peoples R China Xidian Univ Minist Educ Key Lab Collaborat Intelligence Syst Xian 710071 Peoples R China Univ Paris XIII Inst Galilee Lab Informat Proc & Transmiss L2TI F-93430 Paris France

ISBN: (纸本)9781728198354

The classification of hyperspectral image (HSI) has become the focus of the remote sensing field. However, limited training data, which makes the classification task face a major challenge, is inevitable in remote sensing. To eliminate the negative effects of limited labeled samples, an enhanced ensemble method named RoXGBoost, which inherently combines Rotation Forest (RoF) and eXtreme Gradient Boosting (XGBoost) is proposed in this paper. This algorithm could increase the diversity of base classifiers by random feature selection and data transformation. Five ensemble learning methods, Random Forest (RF), AdaBoost, RoF, Rotation Boost and XGBoost, are applied as comparisons. The results on two benchmark datasets, Indian Pines and Pavia University, demonstrate the effectiveness of the RoXGBoost.

关键词： Rotation Forest XGBoost limited training samples hyperspectral image classification ensemble learning

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Study on Hyperspectral remote sensing images of GF-5 De-Blurring Based on Sparse Representation 2023

Study on Hyperspectral Remote Sensing Images of GF-5 De-Blur...

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5th International Symposium on signal processing Systems, SSPS 2023

作者： Wang, Hong School of Electronic Information Sichuan Modern Vocational College Chengdu China

ISBN: (纸本)9798400700040

Deblurring high resolution remote sensing image is a very important problem in remote sensing research. In this paper, we propose a new deblurring algorithm for high-resolution remote sensing images (HSI) based on sparse representation. The purpose of this study is to apply compressed sensing measurement and reconstruction technology to realize the processing of remote sensing image, and discuss the Under what circumstances can CS achieve better results in remote sensing image processing. The algorithm uses fast gradient projection algorithm to achieve deblurring and retain the important ground information of the original image. Experiments on remote sensing images obtained by GF-5 show that the algorithm can filter the blurring of remote sensing images well and improve the peak-to-noise ratio (PSNR) of images. The algorithm has better performance than other sparse representation algorithms. This paper explores the application of dictionary learning theory and sparse decomposition in remote sensing image processing. By further extending the algorithm proposed in this paper and adding new constraints, remote sensing image restoration, target recognition, deblurring, fusion and so on can be carried out. © 2023 ACM.

关键词： image reconstruction

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Research on Detection Algorithm Based on Improved Yolov5

Research on Detection Algorithm Based on Improved Yolov5

引用

2024 International conference on remote sensing, Mapping, and image processing, RSMIP 2024

作者： Liu, Mingsheng Mo, Yurong Wang, Yu Jiangxi Traffic Monitoring Command Center Nanchang China

ISBN: (纸本)9781510680012

In order to improve the effect and quality of video surveillance, we hope to use cutting-edge technology to realize video event recognition and other functions, and enhance the ability to respond to emergencies. Firstly, this paper briefly introduces the network framework, data enhancement processing method and loss function of YOLO series algorithm and YOLOv5 model. Further, it briefly explains the improvement of the improved YOLOv5 in this paper compared with the previous YOLOv5 version, including the enhancement strategy of the data set, the equalization processing strategy of the data set, the processing of the data characteristics, the improvement of the backbone network, and the tuning of the loss function. Finally, the number of classified samples of the training data set is introduced and the experimental results of the trained YOLOv5 are analyzed and visualized. © 2024 SPIE.

关键词： signal detection

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

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

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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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Tensor-Based Chaotic Convolutional Neural Network for remote sensing Data Classification 2

Tensor-Based Chaotic Convolutional Neural Network for Remote...

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

作者： Chen, Luobing Yin, Junjun Yang, Jian University of Science and Technology Beijing Beijing China Tsinghua University Beijing China

ISBN: (纸本)9798331515669

With the advancement of deep learning techniques, the classification of remote sensing data using artificial neural networks has emerged as a prominent research area. Despite this progress, the emulation of brain structures by traditional artificial neural networks remains at a relatively low level. Therefore, there is an urgent need to explore novel methods that can more effectively simulate human brain functions and enhance algorithmic performance. At the same time, artificial neural networks in image processing require the transformation of raw data into vector form. However, this conversion disrupts the inherent relationships between adjacent positional data in the raw data, resulting in the loss of crucial spatial structural information. To address these issues, we propose a tensor-based chaotic convolutional neural network model for the classification of remote sensing data. Firstly, we constructed a 3D discrete chaotic system, utilizing both Logistic mapping and Tent mapping, to achieve a more uniform iterative distribution and a broader full mapping range. This system was then integrated with convolutional neural networks to devise a novel chaotic convolutional neural network algorithm. By introducing chaotic mechanisms, this algorithm addresses the drawback of neural networks being prone to local minima. Secondly, we established a tensor model for remote sensing data. In contrast to existing methods that convert raw data into vector form, our approach represents raw data in tensor form, thereby preserving the spatial structural information among the three channels of the raw data. Finally, the effectiveness of the algorithm was validated using the NWPU-RESISC45 remote sensing dataset © 2024 IEEE.

关键词： Convolutional neural networks

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Design and implementation of algorithm execution time predictor for remote sensing product workflow 3

Design and implementation of algorithm execution time predic...

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3rd International conference on Advanced Algorithms and signal image processing, AASIP 2023

作者： Zhou, Bing Wu, Qiong Li, Jiaguo Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475001 China School of Computer and Information Engineering Henan University Kaifeng475001 China Institute of Remote Sensing Applications Chinese Academy of Sciences Beijing100101 China

ISBN: (纸本)9781510668522

remote sensing product production is an important task in the field of remote sensing engineering. Compared with the production process of remote sensing products, the implementation process of remote sensing product production is a multi-Task serial and highly concurrent process that integrates a large number of remote sensing digital image processing algorithms and various processes. remote sensing product workflows transform complex computing tasks into processes that can be described, managed, and automated. In the workflow, algorithms are the smallest execution unit of tasks, and their execution time has a significant impact on the performance and efficiency of the entire workflow. Therefore, accurate prediction of algorithm execution time is crucial for optimizing workflow performance. This paper proposes and designs an algorithm execution time predictor for remote sensing product workflows. Firstly, feature extraction algorithms are used to extract features from algorithm execution logs, and the prediction model suitable for this scenario is determined by analyzing the characteristics of operators in remote sensing product workflows. Then, the model is trained using historical data sets, and the test set results are compared with the actual execution time to verify the accuracy and practicality of the model. Experimental results show that the proposed algorithm execution time predictor outperforms the baseline model in terms of prediction accuracy and time efficiency © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： remote sensing

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L-MFFN: Lightweight Multiscale Feature Fusion Network with Limited Samples for HSI Classification

L-MFFN: Lightweight Multiscale Feature Fusion Network with L...

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

作者： Ali, Aamir Mu, Caihong Wang, Yafeng Liu, Yi Xidian Univ Sch Artificial Intelligence Xian 710071 Peoples R China Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

ISBN: (纸本)9798350350920

Hyperspectral image (HSI) classification is valuable in remote sensing due to its rich spectral and spatial information. In the last decade, deep learning methods, especially Convolutional Neural Networks (CNNs), have revolutionized HSI classification by extracting intangible semantic features and maintaining the spatial structure during feature extraction. However, the efficacy of these techniques can be constrained by the limited availability of labeled samples in HSI data. To address the issue of small-sample HSI classification, a Lightweight Multiscale Feature Fusion Network (L-MFFN) is introduced. The Multiscale Feature Extraction Module (MFEM) and the enhanced Spectral-Spatial Attention Module (SSAM) are designed and combined in L-MFFN, optimizing the use of deep and shallow features. This integration improves the extraction and fusion of multiscale spectral-spatial features, enhancing classification performance. The proposed model demonstrates state-of-the-art performance across two HSI datasets and stands out in situations with limited labeled samples, highlighting its capability to effectively tackle the challenge of small-sample HSI classification.

关键词： hyperspectral image classification small sample multiscale feature fusion spectral-spatial attention

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