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

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WEAKLY SUPERVISED SEMANTIC SEGMENTATION FOR remote sensing HYPERSPECTRAL IMAGING

WEAKLY SUPERVISED SEMANTIC SEGMENTATION FOR REMOTE SENSING H...

引用

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

作者： Moliner, Eloi Romero, Luis Salgueiro Vilaplana, Veronica Univ Politecn Cataluna Dept Signal Theory & Commun Barcelona Spain

ISBN: (纸本)9781509066315

This paper studies the problem of training a semantic segmentation neural network with weak annotations, in order to be applied in aerial vegetation images from Teide National Park. It proposes a Deep Seeded Region Growing system which consists on training a semantic segmentation network from a set of seeds generated by a Support Vector Machine. A region growing algorithm module is applied to the seeds to progressively increase the pixel-level supervision. The proposed method performs better than an SVM, which is one of the most popular segmentation tools in remote sensing image applications.

关键词： Weakly-supervised segmentation remote sensing hyperspectral image

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Performance Improvement of Water Body Segmentation by DeeplabV3+Using Two Dimensional Variational Mode Decomposition

Performance Improvement of Water Body Segmentation by Deepla...

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International conference on signal processing and Integrated Networks (SPIN)

作者： Bichu George Variyar.V.V Sajith V Sowmya. Ramesh Sivanpillai Center for Computational Engineering and Networking(CEN)Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Wyoming GIS Center University of Wyoming Laramie WY USA

Monitoring changes in surface water bodies and other earth surface features uses remote sensing data. The DeeplabV3+ network is an encoder decoder based deep neural network that is widely used to segment images with good precision. To improve the predictions made by the DeeplabV3+ model, a novel technique based on the two-dimensional variational mode decomposition (2D-VMD) is proposed in the present work. Sentinel 2A/B images dataset from Kaggle is used for this study. The images and their corresponding annotations are also available. The masks were obtained using the index known as Normalized Water Difference Index (NDWI). From 2841 images, we cropped 100 x100 subsets resulting in 1,50,204 images. The proposed method is found to be effective in improving the predictions made by DeeplabV3+ model. With respect to the images considered for the study, the average F1 score increased from 0.33 to 0.47. The average Jaccard score increased from 0.21 to 0.34.

关键词： Earth Deep learning image segmentation Neural networks Predictive models signal processing Sensors

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High resolution remote sensing image classification using hybrid ensemble learning 3

High resolution remote sensing image classification using hy...

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3rd International conference on Intelligent Control, Measurement and signal processing and Intelligent Oil Field, ICMSP 2021

作者： Xi, Jiangbo Xie, Dashuai Jiang, Wandong Xiang, Yaobing College of Geological Engineering and Geomatics Chang'An University Xi'an710054 China Key Laboratory of Western China's Mineral Resources and Geological Engineering Ministry of Education Xi'an710054 China School of Geography Nanjing Normal University Nanjing210023 China

ISBN: (纸本)9781665437158

The classification of the high resolution remote sensing image has become very complex as the resolution improves. Recently, deep learning has been used successfully for high resolution image classification. But it is hard to classify different objects with a large number of pixels, and a small number of pixels at the same time with a single learning model. In this paper the hybrid ensemble learning method is proposed, which combines three kinds of networks: fully connected network, convolutional neural network, and fully convolutional network, to obtain high classification performance. Firstly, the fully connected network is carried out using pixel brightness as features for classification. Secondly, the object-oriented segmentation was used, and the convolutional block was extracted from the center of gravity and the convolutional neural network was used for classification. Thirdly, images were clipped into image blocks, and fully convolutional network U-Net was used with one versus all. Finally, the hybrid ensemble learning method is used, in which a fully connected network is trained for probabilistic combination using the classification results of the three base classifiers. The proposed method was tested with a large number of experiments on Vaihingen aerial image dataset, and the results showed the hybrid ensemble learning method has best performance compared with the single classical neural network and the deep learning method. © 2021 IEEE.

关键词： Learning systems

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Estimating Ship Radial Velocity from Single-Look Complex Data of Multi-Channel ScanSAR 12th

Estimating Ship Radial Velocity from Single-Look Complex Dat...

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12th International conference on signal and Information processing, Networking and Computers, ICSINC 2024

作者： Ma, Jian Wang, Kuan Wang, Xiaoqing Yin, Haixiao Guo, Yunlong Xing, Jian Institute of Remote Sensing Satellite China Academy of Space Technology Beijing China School of Electronics and Communication Engineering Sun Yat-Sen University Shenzhen518107 China School of Electronics and Communication Engineering Sun Yat-Sen University Shenzhen518107 China Peng Cheng Laboratory Shenzhen518055 China

ISBN: (纸本)9789819649662

The radial velocity of moving ships during the beam irradiation time causes a Doppler shift in the multi-channel scanning synthetic aperture radar (MC-ScanSAR) system, leading to positioning mistake and the appearance of azimuth false targets in the focused image. Due to the varying Doppler spectrum range occupied by different targets in the MC-ScanSAR, and the unknown azimuth positions of ships, traditional Doppler analysis methods are not applicable to the MC-ScanSAR mode. The false targets observed in the focused SAR image are primarily caused by mismatches between the moving ships’ signals and the reconstruction processing. Based on this, this paper proposes a highly accurate moving ship velocity estimation algorithm for MC-ScanSAR system, which leverages the correlation between the main and false targets of ships in the imagery single-look complex (SLC) data. The effectiveness and robustness this algorithm have been validated through simulated ship echo data, demonstrating its application potential for high-resolution and ultrawide-swath sea surface in future MC-ScanSAR systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： image reconstruction

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5th EAI International conference on Advanced Hybrid Information processing, ADHIP 2021

5th EAI International Conference on Advanced Hybrid Informat...

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5th EAI International conference on Advanced Hybrid Information processing, ADHIP 2021

ISBN: (纸本)9783030945503

The proceedings contain 94 papers. The special focus in this conference is on Advanced Hybrid Information processing. The topics include: A Dynamic Monitoring Method for Marathon Athletes Based on Wireless Sensor Network;research on Key Technologies of Analysis of User Emotion Fluctuation Characteristics in Wireless Network Based on Social Information processing;Variable Bandwidth Receiving Method of Civil Aircraft Radar signal Based on FPGA Technology;recognition of Aerobics Movement Posture Based on Multisensor Movement Monitoring;a Real-Time Detection Algorithm for Abnormal Users in Multi Relationship Social Networks Based on Deep Neural Network;research on Personalized Recommendation Algorithm Based on Mobile Social Network Data;building Construction Information Real-Time Sharing Method Based on 3D Scanning Technology and Social Network Analysis;a Mining Algorithm for Relevance of Business Administration Based on Complex Social Information Network;classification Method of Regional Differentiation Characteristics of Enterprise Management;design of Fractal image Coding Compression and Transmission Model Based on Wavelet Transform;research on Price Stickiness of Consumer Goods Based on Real-Time Social Information Flow;an Algorithm of Employment Resource Allocation for College Students Based on Social Network Mining;an Improved Mobilenetv3-Yolov5 Infrared Target Detection Algorithm Based on Attention Distillation;A Novel Method of Combined Window Function Construction Filter Applied to F-OFDM System;Passive Electromagnetic Field Positioning Method Based on BP Neural Network in Underwater 3-D Space;optimization of Cross-border E-commerce Marketing Strategy During the Pandemic;deep Residual Network with Transfer Learning for High Spatial Resolution remote sensing Scenes Classification;research on E-commerce Logistics Transportation Route Planning Method Based on Recurrent Neural Network.

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5th EAI International conference on Advanced Hybrid Information processing, ADHIP 2021

5th EAI International Conference on Advanced Hybrid Informat...

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5th EAI International conference on Advanced Hybrid Information processing, ADHIP 2021

ISBN: (纸本)9783030945534

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A SCIL-based transformer for PolSAR image classification

A SCIL-based transformer for PolSAR image classification

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IET International Radar conference (IRC 2023)

作者： Xinyue Xin Ming Li Yan Wu Peng Zhang Dazhi Xu Jia Zheng National Laboratory of Radar Signal Processing School of Electronic Engineering Xidian University Xi'an People's Republic of China Remote Sensing Image Processing and Fusion Group School of Electronic Engineering Xidian University Xi'an People's Republic of China

ISBN: (数字)9781837240982

Transformer has been applied for polarimetric synthetic aperture radar (PolSAR) image processing due to its ability to construct long-range dependency. However, Transformer lacks the learning of local spatial information within samples and correlation information among samples, which limits its performance in practical tasks. To address these issues, this paper proposes a novel Transformer structure based on spatial information learning and correlation information learning (SCIL-based Transformer) for PolSAR image classification. Firstly, a global-local spatial information learning (GL-SIL) module is constructed to fully extract the global-local spatial information within samples. Secondly, a correlation information learning (CIL) module is designed to achieve the learning of correlation information among samples by modeling the dependency across samples. Compared to the self-attention network in Transformer, the above two modules not only add the learning of local spatial information within samples and the correlation information among samples, but also greatly reduce the network complexity. Experiments on two real PolSAR datasets have shown that the proposed SCIL-based Transformer has better performance than other state-of-the-art algorithms.

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Research on Building Extraction from High-Resolution remote sensing image Based on Improved U-Net 2nd

Research on Building Extraction from High-Resolution Remote ...

引用

2nd International conference on 3D Imaging Technologies-Multidimensional signal processing and Deep Learning, 3DIT-MSPandDL 2020

作者： Liang, Shaohui Wang, Meile Qiao, Baoming College of Science Xi’an University of Science and Technology Xi’an China

ISBN: (纸本)9789811633904

Buildings extracted from remote sensing images play a crucial part in resource development and urban planning. With the development of convolutional neural networks for the past several years, the use of deep learning to automatically extract feature information has become a current research hotspot. However, due to the diverse shapes of buildings and complex backgrounds, the segmentation result map faces the problems of low accuracy and blurred edge contours. At the same time, computer hardware also affects the segmentation performance of the model. To resolve these problems, this article puts forward an algorithm to improve the U-Net model after many experiments. The results of the high-scoring remote sensing image test data set show that compared with the basic U-Net, the F1-Score, and IoU of the proposed algorithm are improved by 2.2% and 2.4%, respectively, and the training time of the model is shortened by 5 h. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computer hardware

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DeCloud GAN: An Advanced Generative Adversarial Network for Removing Cloud Cover in Optical remote sensing imagery 2021

DeCloud GAN: An Advanced Generative Adversarial Network for ...

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2021 The 4th International conference on Computational Intelligence and Intelligent Systems

作者： Krisha Bhambani Mukta Takalikar Computer Engineering Pune Institute of Computer Technology India

ISBN: (纸本)9781450385930

Optical remote sensing imagery has several applications in monitoring the states of natural and man-made features around the globe. However, due to clouds and other climatic conditions, information extracted from the imagery retrieved is very limited. Deep learning has often been used in several image processing and remote sensing tasks. In this work, we propose the usage of generative adversarial networks to remove clouds and other climatic interference from high-resolution remote sensing imagery. We have trained and tested upon the remote sensing image Cloud rEmoving dataset (RICE). The novel network(DeCloud GAN) we propose, makes use of residual UNets and pixel shuffle layers in the generator, which yield high quality cloudless satellite images. We have tested 4 methods for comparison, and have found that DeCloudGAN achieves the best performance on two main metrics, peak signal to noise ratio (PSNR) and structural similarity index (SSIM), to measure similarity in visual perception of the produced and target images.

关键词： image generation Optical imagery remote sensing Generative Adversarial Networks Deep Learning

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A Novel Reconstruction Filter for the Azimuth Multi-Channel SAR

A Novel Reconstruction Filter for the Azimuth Multi-Channel ...

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2021 CIE International conference on Radar, Radar 2021

作者： Shang, Mingyang Qiu, Xiaolan Yang, Junying Zhong, Lihua Xin, Yu Ding, Chibiao Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems Aerospace Information Research Institute Chinese Academic of Sciences Beijing China Beijing Institute of Remote Sensing Information Beijing China

ISBN: (纸本)9781665498142

The compensation of channel imbalances plays a vital role in signal processing of the azimuth multi-channel (AMC) synthetic aperture radar (SAR). In the operational AMC SAR system, the channel imbalance is usually considered to be a constant in both the time and frequency domain for one image. However, since the antenna patterns of all channels cannot be strictly consistent, the channel imbalances may vary with the Doppler frequency. This paper establishes the signal model of AMC SAR with channel imbalances changing with Doppler frequency and proposes a novel compensation method during the reconstruction for the echo signals received by all channels. This method has been verified by simulations and real data acquired by Gaofen-3 (GF-3). The result shows that this method has excellent potential for improving the image quality for AMC SAR. © 2021 IEEE.

关键词： Azimuth Multichannel SAR channel imbalances Doppler-varying GF-3 reconstruction

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