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

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Evaluation of Very-high Resolution Satellite image Compression to Support Agriculture Monitoring

Evaluation of Very-high Resolution Satellite Image Compressi...

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IEEE International conference on Aerospace Electronics and remote sensing Technology (ICARES)

作者： Danang S. Candra Kurnia Ulfa Fadillah H. Rasyidy Yohanes Fridolin Hestrio Yudhi Prabowo Mulia I. Rahayu Research Center for Remote Sensing National Research and Innovation Agency (BRIN) Cibinong Indonesia Research Center for Hydrodynamic Technology National Research and Innovation Agency (BRIN) Surabaya Indonesia Center for Data and Information National Research and Innovation Agency (BRIN) Cibinong Indonesia

Recently, the need for detailed images such as very high-resolution satellite imagery has been increasing, but there is an issue of heavy processing for the image as the size of the file is very big. To address this issue, this study aims to evaluate the image compression method in terms of decreasing the size of the imagery. To assess the reliability of the method in preserving the quality of the compressed image, Structural Similarity Index, Peak signal-To-Noise Ratio, and Universal Quality Index were applied to the compressed image. The results show that the compressed image successfully decreased up to 95% in the quality value of 5 and the method can preserve the quality of the compressed images very well.

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A CONSENSUAL COLLABORATIVE LEARNING METHOD FOR remote sensing image CLASSIFICATION UNDER NOISY MULTI-LABELS

A CONSENSUAL COLLABORATIVE LEARNING METHOD FOR REMOTE SENSIN...

引用

IEEE International conference on image processing (ICIP)

作者： Aksoy, Ahmet Kerem Ravanbakhsh, Mahdyar Kreuziger, Tristan Demir, Beguem Tech Univ Berlin Fac Elect Engn & Comp Sci Berlin Germany

ISBN: (纸本)9781665441155

Collecting a large number of reliable training images annotated by multiple land-cover class labels in the framework of multi-label classification is time-consuming and costly in remote sensing (RS). To address this problem, publicly available thematic products are often used for annotating RS images with zero-labeling-cost. However, such an approach may result in constructing a training set with noisy multi-labels, distorting the learning process. To address this problem, we propose a Consensual Collaborative Multi-Label Learning (CCML) method. The proposed CCML identifies, ranks and corrects training images with noisy multi-labels through four main modules: 1) discrepancy module;2) group lasso module;3) flipping module;and 4) swap module. The discrepancy module ensures that the two networks learn diverse features, while obtaining the same predictions. The group lasso module detects the potentially noisy labels by estimating the label uncertainty based on the aggregation of two collaborative networks. The flipping module corrects the identified noisy labels, whereas the swap module exchanges the ranking information between the two networks. The experimental results confirm the success of the proposed CCML under high (synthetically added) multi-label noise rates. The code of the proposed method is publicly available at https://***.

关键词： Multi-label noise collaborative learning multilabel image classification remote sensing

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U-Shaped Network Based on Deformable Convolutional Encoder for Semantic Segmentation of Winter Wheat

U-Shaped Network Based on Deformable Convolutional Encoder f...

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6th International conference on Intelligent Computing and signal processing (ICSP)

作者： Qiao Hu Nan Wang Haining Zhang Beijing Institute of Technology Beijing China Neijiang Normal Uninversity Sichuan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

The distribution of winter wheat in mountainous regions is fragmented and exhibits irregular shapes. Conventional networks tend to lose the fragmented distribution of winter wheat and struggle to accurately predict the distribution edges. To address this issue, this study improves the encoder part of the UNet network by replacing ordinary convolutions with deformable convolutions to achieve flexible extraction of local features. This improvement is beneficial for extracting features from fragmented and irregularly distributed winter wheat. To leverage the advantages of multi-spectral imagery, a bi-temporal remote sensing dataset of winter wheat was created. The bi-temporal data provides more spectral information for the model. In order to model the correlation between the extracted feature channels and the importance of each channel for the segmentation task, a channel attention module called SEblock was added at the bottom of the network. Finally, comparative experiments demonstrate that DUNet effectively locates winter wheat under complex terrain conditions.

关键词： Correlation Convolution Shape Semantic segmentation image edge detection Semantics Feature extraction Data models Sensors remote sensing

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A Simple and Effective Deep Registration Algorithm for SAR images

A Simple and Effective Deep Registration Algorithm for SAR I...

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IEEE International conference on Information Communication and signal processing (ICICSP)

作者： Hui Fang Qingping Meng Guisheng Liao Yongjun Liu Cao Zeng Zhuochen Chen National Key Laboratory of Radar Signal Processing Xidian University Xi'an China Institute of Remote Sensing Satellite China Academy of Space Technology Beijing China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

Synthetic aperture radar (SAR) image registration contributes significantly to various applications of SAR images. Conventional scale invariant feature transform (SIFT) alignment methods possess abysmal robustness for SAR image registration resulting from speckle noise in SAR images. Attributing to the excellent feature representation ability of convolutional neural networks (CNN), a simple and effective deep registration algorithm for SAR images is proposed in this paper. Specifically, we construct a CNN to extract the features of SAR images and to accomplish the feature matching, while outputting the transformation matrix for SAR image registration directly. Furthermore, a data generation strategy is adopted to solve the problem of insufficient training data during the training process. Experiment results on the real data verify the effectiveness of the proposed method.

关键词： Training Training data signal processing algorithms Transforms Data collection Speckle Feature extraction Radar polarimetry Convolutional neural networks Synthetic aperture radar

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Information supplement based on Tip-adapter

Information supplement based on Tip-adapter

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signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： Shengzhou Lian Xu Tang School of Artificial Intelligence Xidian University Xian China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The Contrast Language-image Pre-training (CLIP) model learns to associate image and text content by pre-training on a large number of image and text pairs. The CLIP model can understand the relationship between image content and text description, thereby achieving functions such as image recognition, classification, and retrieval in various tasks. And due to its zero sample learning ability, it can perform effective task inference even without a large amount of labeled data. The proposal of CLIP model has brought new research ideas and application possibilities to the field of multimodal learning, and has shown excellent performance in the zero-shot domain. When we want to apply CLIP to the field of few-shot remote sensing image classification, the main challenge is how to fine-tune the knowledge stored in the clip so that the entire model can better adapt to downstream tasks and how to solve the problem of overfitting in training models under the situation of few shot samples. The commonly used CLIP-based fine-tuning methods have poor performance under few-shot conditions. In this paper, we propose a semi-supervised learning based cached model method, which classifies the test set for the first time based on a small amount of labeled data in the training set as the cached model. The high confidence pseudo labeled data in the test set is used as a supplement to jointly classify the remaining low confidence test samples. The experiment shows that our proposed innovative method has significantly improved accuracy compared to previous methods on two benchmark datasets.

关键词： Training Adaptation models image recognition Semisupervised learning Data models Sensors Proposals remote sensing Overfitting image classification

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A Robust Methodology for Reducing Noise and Artifacts in Digital images using image Denoising with Filtering Strategy

A Robust Methodology for Reducing Noise and Artifacts in Dig...

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International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： J. Gayathri D Prerna Yuvaraj S V. Sivani Saveetha School of Law Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

image denoising is a critical task in various fields, including medical imaging, remote sensing, and photography, where noise and artifacts can significantly degrade image quality. This study proposes a robust methodology for reducing noise and artifacts in digital images using a hybrid image denoising strategy that integrates Median, Gaussian, and Bilateral filtering techniques. The proposed model was evaluated using the Berkeley Segmentation Dataset and Benchmark (BSDS500) and compared against nine existing models. The results demonstrate that the proposed methodology outperforms existing models across multiple metrics, achieving a Peak signal-to-Noise Ratio (PSNR) of 32.10 dB, a Structural Similarity Index (SSIM) of 0.890, a Feature Similarity Index (FSIM) of 0.948, and an Edge Preservation Index (EPI) of 0.889. The model also achieved the lowest Mean Squared Error (MSE) of 24.90 and the fastest processing time of 1.20 seconds, highlighting its effectiveness in reducing noise while preserving image details. These findings suggest that the proposed methodology is highly suitable for applications requiring precise and efficient image denoising. The study’s approach offers a comprehensive solution that balances noise reduction with the preservation of critical image features, making it a valuable contribution to the field of image processing.

关键词： Photography Measurement PSNR Filtering Digital images Noise Noise reduction Indexes remote sensing image denoising

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Efficient Atmospheric Correction for Onboard processing Using Knowledge Distillation and Model Compression

Efficient Atmospheric Correction for Onboard Processing Usin...

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Proceedings of the Digital image Computing: Technqiues and Applications (DICTA)

作者： Miaohua Zhang Ali Cheraghian Yi Qin David Benn Therese Rollan Nariman Habili Data61 CSIRO Australia Environment CSIRO Australia IM&T CSIRO Australia Manufacturing CSIRO Australia

ISBN: (数字)9798350379037

ISBN: (纸本)9798350379044

This paper introduces an innovative deep learning-based solution for atmospheric correction in remote sensing, specifically designed for resource-constrained spaceborne systems. We train the UNet deep learning model with 3D kernels on a synthetic dataset derived from Hyperion imagery over Australia and then utilize knowledge distillation to develop a lightweight model that maintains accuracy while minimizing computational demands. The lightweight model undergoes static quantization and is then deployed on the Raspberry Pi 4 platform. The Raspberry Pi 4 is known for its computational efficiency and aligns with spaceborne computational constraints, offering a practical solution for onboard processing. Major contributions include utilizing available remote sensing data and a physics-based method to create an extensive synthetic dataset resembling typical atmospheric conditions and geographic terrains, as well as outlining an AI processing workflow encompassing synthetic dataset generation, optimal model training, knowledge distillation for lightweight model training, and model compression through static quantization for resource-constrained environments. The results demonstrate the successful deployment of atmospheric correction on the Raspberry Pi 4.

关键词： Training Solid modeling Quantization (signal) Three-dimensional displays Atmospheric modeling Computational modeling Data models Computational efficiency remote sensing Synthetic data

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Domain-Adaptive Deep Transfer Learning for Cross-Modal Heterogeneous image Matching under Label-Scarce Constraints

Domain-Adaptive Deep Transfer Learning for Cross-Modal Heter...

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signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： Guoqing Gong PeiZhen Yang Chao Yang Zhiqiang Han Yuanxin Ye Faculty of Geosciences and Engineering Southwest Jiaotong University Chengdu China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Deep learning has made significant strides in cross-modal heterogeneous remote sensing image matching, yet it remains heavily dependent on annotated target scene data for training models. Under label-scarce constraints, domain discrepancies between actual test data and training data often result in a decline in the performance of trained models. To tackle this challenge, a domain adaptation method is put forth for consideration in the paper for cross-modal heterogeneous image matching. The method achieves the transformation between the images of the existing scene and the target scene through style transfer, and narrows the domain offset between the images of the target scene generated by the transformation of images of the existing scene and the original target scene through adversarial learning. It aligns the target domain with unlabeled information and the source domain with labeled information from both the image and feature perspectives, thereby solving the matching tasks inside the target domain through the application of knowledge from the source domain. Experiments were conducted using two different optical and synthetic aperture radar (SAR) cross-modal datasets, alternating as both target and source domains in training and testing. Results confirmed that compared to current traditional matching methods and deep learning matching methods, our method demonstrates feasibility and effectiveness in cross-dataset transfer, effectively reducing the dependency on annotated data for matching tasks in the target scene.

关键词： Training Deep learning Adaptation models image matching Transfer learning Training data Data models Synthetic aperture radar remote sensing Testing

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Road extraction from remote sensing images based on residual dense blocks and mixed attention mechanism

Road extraction from remote sensing images based on residual...

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6th International conference on Intelligent Computing and signal processing (ICSP)

作者： Xiaohui He Yinjie Jiang The School of Geo-Science & Technology Zhengzhou University Zhengzhou China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

In order to solve the problem of road edge blur in remote sensing image road extraction, a road edge feature information enhancement algorithm based on residual dense blocks and mixed attention mechanism is designed in this paper. At present, semantic segmentation algorithm based on deep learning has been able to extract relatively complete and accurate road network, but the segmentation of road edge is not accurate enough, and the extraction of road edge details is often incomplete. To this end, the ADRU-net road extraction network is designed in this paper, and experimental verification is conducted on data set Zimbabwe_Data_Roads.

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HYPERSPECTRAL image CHANGE DETECTION BASED ON INTRINSIC image DECOMPOSITION FEATURE EXTRACTION 27

HYPERSPECTRAL IMAGE CHANGE DETECTION BASED ON INTRINSIC IMAG...

引用

conference on image and signal processing for remote sensing XXVII

作者： Du, Kecheng Liu, Sicong Tongji Univ Coll Surveying & Geoinformat Shanghai Peoples R China

ISBN: (数字)9781510645691

ISBN: (纸本)9781510645691;9781510645684

Hyperspectral images (HSIs) provides abundant spectral information through hundreds of bands with continuous spectral information that can be used in land cover fine change detection (CD). HSIs make it possible for hyperspectral CD performance with higher discrimination on changes but provides a challenge to the conventional CD techniques due to its high dimensionality and dense spectral representation. In this paper, we implemented intrinsic image decomposition (IID) model to decompose the hyperspectral temporal difference image into two parts: real change and pseudo change information. In particular, the spectral reflecting component is selected as a kind of pure spectral feature used to enhance the CD performance in multitemporal HSIs. Experimental results illustrate the effectiveness of IID features extraction in addressing a supervised CD task.

关键词： Hyperspectral image change detection intrinsic image decomposition multiple changes

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