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

共 3573 条记录，以下是31-40 订阅

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remote sensing image UNEVEN HAZE REMOVAL BASED ON HAZE DENSITY ESTIMATION AND SALIENCY-DRIVEN DUAL CHANNEL FUSION 31

REMOTE SENSING IMAGE UNEVEN HAZE REMOVAL BASED ON HAZE DENSI...

引用

2024 International conference on image processing

作者： Liu, Yanmeng Zhang, Libao Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China

ISBN: (纸本)9798350349405;9798350349399

remote sensing images (RSIs) are usually degraded by haze, losing the spectral fidelity and texture details. Most previous dehazing works adopted a unified dehazing strategy to process the entire image, ignoring the discrepancies of haze density, spectral information and texture complexity among different regions in one RSI, and thus resulted in the distorted spectral and blurred texture. In this paper, we propose a skip-connected network based on haze density estimation and saliency-driven dual channel fusion, realizing a differentiated defogging method. First, we propose a haze density estimation model, generating the haze density map. Second, we design a saliency-driven dual channel fusion to distinguish the spectral and texture features of different regions in the input, generating the saliency map. The two maps above jointly serve as guidance for the network to perform differentiated dehazing. Finally, an attention-based skip-connected block is introduced, which helps to fuse multi-scale information and obtain more refined dehazing results. A mixed loss function is also constructed to retain more texture details. The efficiency of the proposed method is validated by comparing its performance with other SOTA schemes.

关键词： remote sensing image dehazing haze density estimation saliency detection skip-connected network

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Monitoring of Renewable energy sources with remote sensing, Open data and field data in Bulgaria 29

Monitoring of Renewable energy sources with remote sensing, ...

引用

conference on image and signal processing for remote sensing xxix

作者： Spasova, Temenuzhka Dancheva, Adlin Avetisyan, Daniela Ivanova, Iva Popov, Iliyan Shirov, Boris Bulgarian Acad Sci SRTI BAS Space Res & Technol Inst Acad G Bonchev Strbl 1 Sofia 1113 Bulgaria Res Lab Met Air Syst OXYMET Sofia Bulgaria

ISBN: (纸本)9781510666955;9781510666962

Monitoring through satellite data, in situ (including spectrometer data, GPS, thermal camera), Open data, data from various devices and Unmanned aerial vehicles (UAV) in the selected anthropogenic sites is of extremely high ecological importance for tracking natural processes, the consequences of climate changes and the creation of a useful model for the analysis of spectral characteristics based on machine learning. The timeliness of the data and the spatial extent of the observed objects allow satellite information to be reliable in monitoring and making predictions about the risk and potential risk of natural disasters, rise of average air temperatures and anthropogenic pollution. The sites were pre-marked based on open data from non-governmental organizations (NGOs) and administration. Data from the Multispectral Instrument (MSI) of the Sentinel 2 platform and SAR of the European Space Agency's Copernicus program, spectrometer (380-780nm) and drone data were used. Landsat sensors and data from Sentinel 3 (EUMETSAT) were used to calculate the surface temperature of renewable energy sites such as photovoltaic parks. Data from different years were used in order to track the studied territories according to NUTS2. The result is the development of a useful hybrid model for spectral analysis and tracking of spatial dynamics and surface changes of objects of interest based on satellite and field surveys. Data from the ground mobile and autonomous weather station AWG (1), powered by an environmentally friendly magnesium-air battery was improved specifically for the project. Another important task is the creation of an energy atlas for the benefit of the Earth's Digital Twins. The data is part of an open data catalog of the NGO Eco Global Monitoring TA(2).

关键词： Climate change Digital Twins LST NUTS 2 Sentinel 3 Renewable energy SAR

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PHASE LEARNING BASED ON INTERACTIVE PERCEPTION FOR LIMITED-SAMPLE RESIDENTIAL AREA SEMANTIC SEGMENTATION 49

PHASE LEARNING BASED ON INTERACTIVE PERCEPTION FOR LIMITED-S...

引用

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

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

ISBN: (纸本)9798350344868;9798350344851

Due to the rich details of residential areas and the characteristics of remote sensing image sharpness vulnerable to haze, it will not only consume a lot of labor costs but also be very difficult to produce a large-scale dataset with strong labels. Therefore, the limited-sample dataset has become a hotspot in recent years. To address this issue, we proposed a semantic segmentation method for residential areas by phase learning. The main task of the first stage is to generate a joint saliency map by reducing the interference of haze noise through the feature comparison similarity sorting algorithm and combine them to generate initial pixel-level pseudo labels for the next stage of training. In the second stage, we proposed to construct a group feature interactive perception module to achieve image group semantic co-segmentation. Comprehensive evaluations with 2 datasets and the comparison with 7 methods validate the superiority of the proposed model.

关键词： remote sensing phase learning weak annotation interactive perception contrast similarity ranking

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Efficient Vision Mamba with Dynamic Feature Fusion for remote sensing image Semantic Segmentation 2

Efficient Vision Mamba with Dynamic Feature Fusion for Remot...

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

作者： Liu, Chenchen Cao, Yice Jiang, Dingkun Wu, Zhenhua Hu, He Huang, Zhixiang Anhui University School of Electronic and Information Engineering Hefei230601 China

ISBN: (纸本)9798331515669

The improvement in imaging resolution and the increase in imaging swath of remote sensing satellites have enabled the acquisition of vast and complex remote sensing image data. Effectively and comprehensively extracting target information to enhance segmentation accuracy and interpretation efficiency has become a research hotspot in the field of remote sensing semantic segmentation. Although current segmentation algorithms based on CNN and Transformer frameworks have made significant progress in performance, achieving a good balance between segmentation accuracy and computational complexity remains challenging, limiting their widespread application in practical tasks to a certain extent. To address this issue, this paper introduces a state-space model and proposes an efficient vision Mamba for remote sensing image semantic segmentation, abbreviated as EVMDNet. By designing an efficient vision state space block (EVSSBlock), the method effectively integrates local and non-local features while improving computational efficiency through efficient 2D scanning (ES2D) strategy. Furthermore, to avoid the insufficient utilization of features and weak discriminative ability caused by the direct addition of skip connections, a dynamic feature fusion block (DFFBlock) is designed to adaptively adjust the weights of different features for efficient feature fusion. Experimental results on classical remote sensing image datasets demonstrate that the proposed method not only improves segmentation accuracy but also reduces computational complexity to a certain extent, outperforming existing state-of-the-art segmentation algorithms. © 2024 IEEE.

关键词： Semantic Segmentation

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MAS-NET:MIXED-FEATURE ATTENTION SIAMESE NETWORK FOR CHANGE DETECTION ON remote sensing imageS 49

MAS-NET:MIXED-FEATURE ATTENTION SIAMESE NETWORK FOR CHANGE D...

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

作者： Ding, Xingyu Wang, Weiqiang Univ Chinese Acad Sci CAS Beijing Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Change detection plays a crucial role in remote sensing tasks. However, current deep learning-based change detection methods suffer from issues such as misclassified pixels and unclear segmentation result on edges. To address these challenges, we propose a novel approach called Mixed-feature Attention Siamese Network (MAS-Net). MAS-Net adopts an encoder-decoder structure, where the encoder part effectively fuses image features from different time points, thereby preserving more cross-time information in the feature map. In the decoder part, we introduce the Feature Global Attention Module (FGAM) to leverage the global attention mechanism for extracting deep semantic information from the fused feature map. By incorporating these proposed modules and strategies, MAS-Net achieves fewer misclassified pixels and clearer edges in the resulting change detection maps. Experimental evaluations on the LEVIR-CD and CDD datasets demonstrate that MAS-Net outperforms state-of-the-art models by 0.15% (91.88% vs. 91.73%) and 1.3% (97.5% vs. 96.2%) in terms of F1-Score, respectively, thus establishing a solid baseline for change detection.

关键词： change dectection remote sensing attention mechanism

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signal-Centric remote sensing via Alternative Preprocessing and Acoustic processing for ML-Driven Applications

Signal-Centric Remote Sensing via Alternative Preprocessing ...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Luna, Logan Jansen-Sánchez, Sirio Demirkiran, Ilteris Ghelarducci, Leo Embry-Riddle Aeronautical University Daytona Beach United States

ISBN: (纸本)9798331504847

The dominant method of processing sonar data is using image-based representations, requiring the preprocessing of image data on autonomous systems. We propose an alternative data processing method for remote sensing applications via the use of data in Comma-Seperated Value format. Experimentation on our alternative approach shows a reduction of processing time by 91.18%, an improvement in accurate object detection by Machine Learning, and an increase in SNR (signal-to-noise ratio), PSNR (Peak signal-to-noise ratio), and other evaluation metrics. © 2025 IEEE.

关键词： Data curation

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MULTIMODAL TRANSFORMER USING CROSS-CHANNEL ATTENTION FOR OBJECT DETECTION IN remote sensing imageS 31

MULTIMODAL TRANSFORMER USING CROSS-CHANNEL ATTENTION FOR OBJ...

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2024 International conference on image processing

作者： Bahaduri, Bissmella Ming, Zuheng Feng, Fangchen Mokraoui, Anissa Univ Sorbonne Paris Nord Lab L2TI Villetaneuse France

ISBN: (纸本)9798350349405;9798350349399

Object detection in remote sensing images (RSI) is a critical task for numerous applications in Earth Observation (EO). Differing from object detection in natural images, object detection in remote sensing images faces challenges of scarcity of annotated data and the presence of small objects represented by only a few pixels. Multi-modal fusion has been determined to enhance the accuracy by fusing data from multiple modalities such as RGB, infrared (IR), lidar, and synthetic aperture radar (SAR). To this end, the fusion of representations at the mid or late stage, produced by parallel subnetworks, is dominant, with the disadvantages of increasing computational complexity in the order of the number of modalities and the creation of additional engineering obstacles. Using the cross-attention mechanism, we propose a novel multi-modal fusion strategy for mapping relationships between different channels at the early stage, enabling the construction of a coherent input by aligning the different modalities. By addressing fusion in the early stage, as opposed to mid or late-stage methods, our method achieves competitive and even superior performance compared to existing techniques. Additionally, we enhance the SWIN transformer by integrating convolution layers into the feed-forward of non-shifting blocks. This augmentation strengthens the model's capacity to merge separated windows through local attention, thereby improving small object detection. Extensive experiments prove the effectiveness of the proposed multimodal fusion module and the architecture, demonstrating their applicability to object detection in multimodal aerial imagery. Our code is available at here.

关键词： Multimodal fusion cross-channel attention convolutional shifting window object detection remote sensing imagery

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Koopman Ensembles for Probabilistic Time Series Forecasting 32

Koopman Ensembles for Probabilistic Time Series Forecasting

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32nd European signal processing conference (EUSIPCO)

作者： Frion, Anthony Drumetz, Lucas Tochon, Guillaume Dalla Mura, Mauro El Bey, Abdeldjalil Aissa IMT Atlantique Lab STICC Brest France LRE EPITA Le Kremlin Bicetre France Univ Grenoble Alpes CNRS Grenoble INP Inst Univ FranceGIPSA Lab Grenoble France

ISBN: (纸本)9789464593617;9798331519773

In the context of an increasing popularity of data-driven models to represent dynamical systems, many machine learning-based implementations of the Koopman operator have recently been proposed. However, the vast majority of those works are limited to deterministic predictions, while the knowledge of uncertainty is critical in fields like meteorology and climatology. In this work, we investigate the training of ensembles of models to produce stochastic outputs. We show through experiments on real remote sensing image time series that ensembles of independently trained models are highly overconfident and that using a training criterion that explicitly encourages the members to produce predictions with high inter-model variances greatly improves the uncertainty quantification of the ensembles.

关键词： Dynamical systems Koopman operator Uncertainty quantification remote sensing Sentinel-2

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Triplet Network with Autoencoder for remote sensing image scene recognition and classification

Triplet Network with Autoencoder for Remote sensing image sc...

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

作者： Hu, Fangchun Dai, Yifeng Wu, Lie Anhui University Anhui China Chuzhou University Anhui China

ISBN: (纸本)9781510680425

remote sensing image scene categorization is a crucial process that involves assigning semantic labels to images based on their content,which has wide-ranging applications in a number of *** inherent potential of deep neural networks to learn attractive features has substantially sped up progress in the field, resulting in considerable breakthroughs in remote sensing and scene classification powered by deep ***, the existing literature lacks a comprehensive evaluation of research findings in deep learning for remote sensing image scene categorization, despite the notable advancements in this *** CNN has shown remarkable efficacy in extracting image features, ranging there are persistent challenges that hinder its optimal performance in the domain of categorization of images from data collected from remote *** intrinsic attributes linked to remote sensing image data, such as intraclass variability and inter-class similarities, result in a notable decrease in the classification accuracy of the ***, the discernible impact of noise and interference further exacerbates limitations in CNN performance within this specific *** response to these challenges, this study introduces an innovative classification methodology that seamlessly integrates a triplet network and ***, the paper systematically conducts a rigorous comparative assessment, evaluating the performance of diverse image classification models across various remote sensing *** holistic approach aims to contribute to the evolving landscape of remote sensing image scene classification by addressing existing gaps and advancing our understanding of effective model architectures. © 2024 SPIE.

关键词： Deep neural networks

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Computational methods to retrieve soil moisture using remote sensing data: A review 7

Computational methods to retrieve soil moisture using remote...

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IEEE 7th International conference on Advanced Technologies, signal and image processing (ATSIP)

作者： Inoubli, Raja Constantino-Recillas, Daniel Enrique Monsivais-Huertero, Alejandro Farah, Lilia Bennaceur Farah, Imed Riadh Natl Sch Comp Sci Labratoire RIADI Manouba 1001 Tunisia Tecnol Nacl Mexico TES Ecatepec Mexico City DF Mexico Inst Politecn Nacl ESIME Ticoman Mexico City DF Mexico Preparatory Inst Engn Studies El Manar LTSIRS Lab Manar Tunisia Natl Inst Engn Sci Tunis Manar Tunisia

ISBN: (纸本)9798350351491;9798350351484

Recently, soil moisture (SM) retrieval has become the first interest of the scientific community due to the increasing demand for water resources in several fields, especially the agricultural sector. A lack of such resources has been detected along with the increasing climate changes and disasters occurring such as drought. It has become very essential to search for efficient approaches and data sources that can serve effectively the SM retrieval goals. This paper is a literature review set to identify the strengths and weaknesses of the most relevant works in the area of SM retrieval and remote sensing (RS) published from 2017 to 2024. Summarizing the selected studies serves to detect the relevant information that tackles the SM retrieval challenges. Thus, creating new horizons for new research. One of the main fields of interest is SM retrieval over agriculture based on the synergistic use of Sentinel-1 (S-1) and Sentinel-2 (S-2) images. High accuracy has been achieved by combining different approaches such as backscattering models and Machine Learning (ML) algorithms (e.g., AIEM and NN algorithms). The application of such a combination is highly recommended due to its promising potential.

关键词： Soil moisture arid and semi-arid regions backscattering models Machine learning Change detection hybrid approaches remote sensing retrieval

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