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Cloud detection algorithm based on point by point refinement 3

Cloud detection algorithm based on point by point refinement

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3rd International conference on signal processing and Machine Learning, CONF-SPML 2023

作者： Zhang, Jiyin Ocean College Zhejiang University Zhejiang province Zhoushan316021 China

In order to limit the interference of cloud noise on ground scene information, cloud detection has been a hot issue in research on remote sensing image processing. Cloud detection labels the clouds in remote sensing images at the pixel level. The majority of early cloud detection systems rely on manually created feature and threshold segmentation with limited generalizability. remote sensing cloud detection based on deep learning has improved in accuracy and speed thanks to the quick development of convolutional neural networks, but it is still unable to satisfy practical application requirements when dealing with sceneries with variable cloud block size and sparse distribution. To this end, this study proposes a cloud detection algorithm based on point-by-point refinement based on the idea of coarse to fine. Specifically, firstly, the residual module is introduced in the U-Net network to extract more features;secondly, the point-by-point refinement module is designed to filter out the areas in the remote sensing images where the clouds are easily detected wrongly for optimization and re-prediction, and then produce finer-grained and more accurate cloud detection results. The quantitative and qualitative experiments validate the effectiveness of the proposed method. © Published under licence by IOP Publishing Ltd.

关键词： remote sensing

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Multiband remote 3D-ViT data fusion 30

Multiband remote 3D-ViT data fusion

引用

conference on Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX

作者： Kurz, Aidan G. Depoian, Arthur C., II Bailey, Colleen P. Univ North Texas Dept Elect Engn Denton TX 76207 USA

ISBN: (纸本)9781510673816;9781510673809

Satellites are equipped with diverse sensors, capable of capturing detailed information across a multitude of wavelengths. The fusion of multispectral data is pivotal to amplify the visual representation of the area of interest. The improvement of information representation allows for enhanced processing, analysis, and other crucial tasks for numerous fields of study, including remote sensing, defense, and material characterization. Previous solutions often utilize traditional signal processing techniques, including principal component analysis (PCA), to accomplish data fusion. By performing fusion on a feature level, extracted information about the area of interest texture and boundaries are combined. The introduction of neural network techniques improved the reconstruction of data similar to the results obtained by conventional inference of humans. For example, the use of deep learning algorithms in conjunction with PCA allowed for refined reduction of redundancy and distortion of spectral data, in comparison to traditional methods alone. The introduction of the Vision Transformer (ViT) architecture, originally developed for two-dimensional image data, has revolutionized image processing tasks, vastly improving performance at the cost of a large quantity of trainable parameters. Recent experimentation has proven that optimizing ViT for efficiency allows for comparable or even superior performance while lessening the computational cost. The transition from 2D to 3D information via utilization of additional depth and spatial data has also led to superior results as the added information allows for better representation of terrain features, making it invaluable for satellite imagery analysis. Combining the principles of ViT and 3D information to process complex satellite data can result in more effective data fusion to achieve a superior level of data visualization of multispectral satellite imagery in an efficient manner.

关键词： Data Fusion Multispectral Fusion remote sensing Deep Learning

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Multi-scale Siamese Dual Decoding Network for remote sensing Tank image Segmentation 23

Multi-scale Siamese Dual Decoding Network for Remote Sensing...

引用

6th International conference on signal processing and Machine Learning, SPML 2023

作者： Liu, Jiashan Zhang, Shanwen Wang, Xuqi Ren, Zhiwen Ding, Zili The School of Electronic Information Xijing University China

ISBN: (纸本)9798400707575

To address the multi-scale problem and interference of differences between data in remote sensing image segmentation, a multi-scale Siamese dual decoding network is proposed. The twin network is used as the backbone network for parallel processing and weight sharing. The dilated stratification attention module is designed to extract effective multi-scale features. Different branches can obtain receptive fields of different sizes in order to learn more partition features and suppress background interference. The global attention module is designed to extract common characteristics between datasets and to focus on the characteristics of the datasets, thus extracting connections between datasets. The residual fusion module is also designed for adaptive learning of feature weights of different encoding layers. Experimental results demonstrate that the proposed method has better efficacy and robustness compared to classical networks, and has stronger recognition efficacy for different complexity scenarios, meeting the expectations of this study. © 2023 ACM.

关键词： image segmentation

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Research and application of remote sensing image-based extraction technology for fire trails 3

Research and application of remote sensing image-based extra...

引用

3rd International conference on signal image processing and Communication, ICSIPC 2023

作者： Huang, Huan Fan, Qiang Wu, Jianrong Liu, Kang Mao, Xianyin Zhang, Yizhao Zhao, Chao Electric Power Research Institute of Guizhou Power Grid Co. Ltd. Guiyang550002 China Joint Laboratory of Ice Prevention & Disaster Reducing China Southern Power Grid Co. Ltd. Guiyang550002 China School of Electrical Engineering Guizhou University Guiyang550025 China

ISBN: (纸本)9781510670945

This paper proposes a remote sensing image-based method for the extraction of fire trails. Firstly, by acquiring multispectral images of the forest in the study area and pre-processing the multispectral images. A vegetation correlation index is calculated, the vegetation correlation index is normalized, read into the vector range of the study area, and the vector range is cropped. Finally, the cropped area is extracted based on a first threshold, the extracted area is transformed, and the transformed area is filtered based on a second threshold to obtain the final fire-trail area. The invention is able to automate the pre-processing and extraction, using only one vegetation index, and combining with area thresholds, the process is more simplified. © 2023 SPIE.

关键词： Fires

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Multi-feature Based Sea–land Segmentation for Multi-spectral and Panchromatic remote-sensing imagery 7th

Multi-feature Based Sea–land Segmentation for Multi-spectra...

引用

7th International conference on Computer, Communication and signal processing, ICCCSP 2023

作者： Savitha, D.K. Kethavath, Rajkumar Jamal, Arshad Bangalore093 India

ISBN: (纸本)9783031398100

Robust sea–land segmentation in optical remote-sensing images is difficult task because of complex sea-land environment. Low contrast difference between sea and land in the case of panchromatic images and water color variation in case of multispectral images makes the segmentation process highly challenging. In this paper, we propose a multi-feature sea–land segmentation approach for optical remote sensing images where features such as color information in CMYK, intensity in HSV, statistical information and texture features are used to segment the image into sea and land. Morphological operations are applied to fine tune the segmentation results. In the proposed approach, these features are extracted from resized image and take very less computation time. The proposed approach was tested on Google-Earth images and yields robust, accurate sea–land segmentation results and it works very well on both panchromatic as well as multi-spectral images. © IFIP International Federation for Information processing 2023.

关键词： image segmentation

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DYNAMIC MUTUAL ENHANCEMENT NETWORK FOR SINGLE remote sensing image DEHAZING 29

DYNAMIC MUTUAL ENHANCEMENT NETWORK FOR SINGLE REMOTE SENSING...

引用

IEEE International conference on image processing (ICIP)

作者： Wang, Shan Zhang, Libao Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

In this paper, we propose a dynamic mutual enhancement network (DMENet) for haze removal in remote sensing images. It has three major advantages compared with other dehazing algorithms: 1) The proposed DMENet is based on the U-Net architecture to extract features effectively, which is composed of three components, i.e., a multi-scale encoder, a middle transmission layer (MTL), and a dynamic mutual decoder. 2) The dynamic mutual enhancement (DME) module is designed to dynamically integrate multi-level feature maps in a mutual way, which contains the low-level detail information and high-level semantic information respectively. 3) To improve the robustness and generalization performance of the DMENet, the hybrid supervision is built for network training between the restored results and their ground-truth labels, which consists of the pixel-level supervision, patch-level supervision and image-level supervision. Experimental results on both synthetic datasets and real remote sensing hazy images demonstrate that the proposed DMENet can gain significant progresses over the competing methods.

关键词： image enhancement remote sensing image dehazing dynamic mutual enhancement CNN

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Fusion of Multiple Models with Multi-Modal Datasets: Land Cover Mapping in the Yangtze River Economic Belt 14

Fusion of Multiple Models with Multi-Modal Datasets: Land Co...

引用

14th Workshop on Hyperspectral Imaging and signal processing: Evolution in remote sensing, WHISPERS 2024

作者： Li, Jiepan Huang, He Xia, Yu Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan430079 China Pengcheng Laboratory Shenzhen518066 China

ISBN: (纸本)9798331513139

Land cover is essential for understanding Earth's environment, playing a vital role in disaster monitoring and infrastructure management. The Yangtze River Economic Belt, a pivotal region for economic growth in China, requires comprehensive monitoring and analysis of land cover changes to support effective policy-making and environmental protection. The 2024 IEEE Whisper contest, MMSeg-YREB, aims to integrate multi-modal data, specifically Sentinel-1 and Sentinel-2, to conduct a detailed analysis of land cover types and their changes within the Yangtze River Economic Belt. To overcome this challenge, the MMSeg-YREB dataset was integrated with Google Earth Engine (GEE) tools and publicly available global land cover products for data preparation. With this enhanced dataset, multiple models were developed, including convolutional neural networks (CNN) and Transformer networks, which enables the learning of features from both local and global perspectives, significantly enhancing the accuracy of segmentation. To fuse these results, a variety of post-processing techniques were proposed to refine the outcomes of the different models, resulting in a highly accurate final land cover maps. This innovative approach allowed our team to achieve a commendable first-place in the competition, underscoring our contributions and effectiveness in advancing methodologies for land cover mapping. © 2024 IEEE.

关键词： Convolutional neural networks

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Domain-Adaptive Deep Transfer Learning for Cross-Modal Heterogeneous image Matching under Label-Scarce Constraints 2

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

引用

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

作者： Gong, Guoqing Yang, PeiZhen Yang, Chao Han, Zhiqiang Ye, Yuanxin Southwest Jiaotong University Faculty of Geosciences and Engineering Sichuan Chengdu611576 China

ISBN: (纸本)9798331515669

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. © 2024 IEEE.

关键词： Contrastive Learning

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Multi-scale Frequency Attention Fusion Network for Optical and SAR image Registration 17

Multi-scale Frequency Attention Fusion Network for Optical a...

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17th IEEE International conference on signal processing, ICSP 2024

作者： Hu, Xin Wu, Yan Li, Zhikang Yang, Zhifei Li, Ming Xidian University School of Electronic Engineering Xi'an China Xidian University National Key Laboratory of Radar Signal Processing Xi'an China

ISBN: (纸本)9798350387384

image registration is a prerequisite for remote sensing image fusion and classification, and registration accuracy affects the performance of these tasks. However, there are significant nonlinear radiation differences between optical and synthetic aperture radar (SAR) images and speckle noise in SAR, which limits the ability of pure convolutional neural networks (CNNs) to obtain modality-shared features. To solve these problems, this paper proposes a Multi-scale Frequency Attention Fusion Network for Optical and SAR image Registration (MFAF-OS-RegNet), which includes a Multi-scale Frequency Channel Attention Fusion (MFCAF) module and a Momentum Contrastive Learning (MCL) strategy. Firstly, we extract repeatable keypoints distributed on the edges and develop a novel MFCAF module to obtain modality-shared features. In the MFCAF module, multi-scale feature fusion is used to improve the positioning accuracy of matching points, and frequency channel attention is used to mine more favorable features. Secondly, we design a MCL strategy to reduce the interference of speckle noise in network training. The momentum teacher corrects handcrafted labels by learning the essential characteristics of the data for robust matching. Finally, we filter outliers and estimate transformation parameters based on the initial matching correspondence output by MFAF-OS-RegNet. Experimental results on a publicly available OS-dataset show that the proposed method outperforms other state-of-the-art methods. © 2024 IEEE.

关键词： image fusion

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MPFFNet:Semantic Segmentation of remote sensing image Buildings Based on Multipath Feature Fusion 8

MPFFNet:Semantic Segmentation of Remote Sensing Image Buildi...

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8th International conference on Communication, image and signal processing, CCISP 2023

作者： Dong, Jie Mao, Jingkun Liu, Kun Cheng, Liangyong School of Electrical Engineering and Automation Tianjin University of Technology Tianjin China School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China Interplanetary Space Technology Development Co. LTD Tianjin China

ISBN: (纸本)9798350305838

The buildings with variable scales and complex background information in remote sensing images often result in missed and misclassified segmentation of buildings. To address this issue, this paper designs an multipath feature fusion network (MPFFNet). MPFFNet has an overall encoding and decoding structure, embedded with an residual depth Inception block(RDI) module to extract multi-scale features. At the same time, MPFFNet uses the multipath residual channel attention (MPRCA) module to expand the Receptive field of the feature map to obtain rich multi-scale context information. The validity of the proposed algorithm is proved on the WHU Buildings data set, with F1 reaching 94.70%, IOU reaching 89.93%, and Recall reaching 94.98%. © 2023 IEEE.

关键词： Buildings

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