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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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SSRI-NET: AN ITERATIVE NETWORK FOR SAR SUPER-RESOLTION IMAGING

SSRI-NET: AN ITERATIVE NETWORK FOR SAR SUPER-RESOLTION IMAGI...

引用

2022 International conference on Radar Systems, RADAR 2022

作者： Wang, Ziwen Wei, Yangkai Ding, Zegang School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing China

ISBN: (纸本)9781839537776

SAR imaging is a widely used technique to solve the remote sensing problem. However, the image resolution of traditional imaging methods is limited by signal bandwidth. In order to solve this problem, a new iterative network is proposed to obtain SAR super-resolution images, i.e., SAR Super-Resolution Imaging Network (SSRI-Net). The proposed SSRI-Net is designed to implement SAR super-resolution imaging. In SSRI-Net, the SAR echo is transformed to the SAR reconstruction image, while an additional decoder part does the opposite operation. The SSRI-Net is composed of the unfolded Alternating Direction Method of Multipliers (ADMM), and the decoder part is formulated into a linear mapping to achieve self-supervised imaging. Numerical simulation and real-data experiment in microwave anechoic chamber demonstrate the robustness and effectiveness of the proposed method. © 2022 IET conference Proceedings. All rights reserved.

关键词： image resolution

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image GENERATION BASED ON TEXTURE GUIDED VAE-AGAN FOR REGIONS OF INTEREST DETECTION IN remote sensing imageS

IMAGE GENERATION BASED ON TEXTURE GUIDED VAE-AGAN FOR REGION...

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

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

ISBN: (纸本)9781728176055

Deep learning has shown great strength in regions of interest (ROIs) detection for remote sensing images (RSIs). However, for most of RSIs, the unbalanced distribution of positive and negative samples greatly limits the performance of the deep learning-based methods. To cope with this issue, we propose a novel method based on texture guided variational autoencoder-attention wise generative adversarial network (VAE-AGAN) to augment the training data for ROI detection. First, to generate realistic texture details of RSIs, we propose a texture guidance block to embed texture prior information into encoder and decoder networks. Second, we introduce the channel and spatial-wise attention layers in the discriminator construct to adaptively recalibrate the varying importance of different channels and spatial regions of input RSIs. Finally, we apply the RSI dataset balanced by our proposal to the weakly supervised ROI detection method. Experimental results demonstrate that the proposal can not only improve the performance of ROI detection, but also outperform other competing augmentation methods.

关键词： image generation texture guidance attention generative adversarial networks

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Assessment of multisource remote sensing image fusion by several dissimilarity methods 2

Assessment of multisource remote sensing image fusion by sev...

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2021 2nd International conference on signal processing and Computer Science, SPCS 2021

作者： Li, Feiyan Investigation Department Shandong Police College Shandong Jinan250000 China

Recently, advancements in remote sensing technology have made it easier to obtain various temporal and spatial resolution satellite data. remote sensing techniques can be a useful tool to detect vegetation and soil conditions, monitor crop diseases and natural disaster prevention, etc. Although the same scene taken by different sensors belong to the same ground object, the information that they offered are redundant, complementary and collaborative due to the spatial, spectral and temporal resolution are different. The method of image fusion can integrate an image with rich details and valuable information from multi-source remote sensing images, which aim to obtain more comprehensive and precise observations of the ground object. By using aspects from multi-source image fusion, this review presents the current status and future trends in remote sensing image fusion. First, different image properties and their applications are presented for remote sensing datasets at home and abroad. Second, a general summary and inductive analysis of the challenging difficulty of different types of multisource image fusion methods is conducted. Third, experiments are tested on eight different methodological approaches, and experimental results demonstrate that GSA method is the best alternative in terms of obtaining high spatial resolution and retaining the spectral information. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： image fusion

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Integration of Landsat-8 Multispectral Band and Cartosat-1 Digital Elevation Model using image Fusion Techniques 8

Integration of Landsat-8 Multispectral Band and Cartosat-1 D...

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8th International conference on signal processing and Integrated Networks, SPIN 2021

作者： Bopche, Litesh Rege, Priti P. College of Engineering Pune India

ISBN: (纸本)9781665435642

Significant technical restrictions, such as limited data storage on the satellite platform in space and limited bandwidth for communication with the ground station, restrict satellite sensors from simultaneously recording images with higher spatial and spectral resolutions. image fusion and pan-sharpening have proved to be effective methods in remote sensing studies to handle these restrictions. image fusion combines Panchromatic and Multispectral band images to produce a composite or fused image with better spatial and spectral resolutions. In this paper, five different pan-sharpening approaches viz., the Intensity-Hue-Saturation transformation, the Brovey Transformation, the ESRI pan-sharpening transformation, the Gram-Schmidt method, and Simple Mean transformation are tested on the Landsat-8 MS band and Cartosat-1 Digital Elevation Model (DEM) PAN band. Results are analyzed visually and quantitatively to verify the spectral characteristics of the fused images in order to assess the efficiency of various image fusion techniques. A statistical index such as Root Mean Square Error (RMSE) is used to evaluate the fused image. © 2021 IEEE

关键词： remote sensing

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remote sensing Scene Classification based on Generative Compressed-Domain

Remote Sensing Scene Classification based on Generative Comp...

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2021 IEEE International conference on signal processing, Communications and Computing, ICSPCC 2021

作者： Zhao, Yinan Zhao, Baojun Tang, Linbo Beijing Institute of Technology Radar Research Lab School of Information and Electronics Beijing China

ISBN: (纸本)9781665429184

Compressed-domain image classification refers to the direct feature extraction of image data in the form of compressed codestream to achieve scene classification tasks. In recent years, image compression based on deep learning has become an active research field, and its compressed codestream can retain rich high-level semantic features. Inspired by this, this paper proposes a deep representation model based on generative compressed-domain, and realizes the multi-scene classification of remote sensing images. Specifically, the generative compression model optimizes training for image encoding, decoding and discrimination, so that the compressed bitstream can best retain or restore the image content. For the sparsely compressed bitstream, mapping high-dimensional feature to the input based on the residual structure, training the Soft-Max classifier for multi-scene classifcation. To a certain extent, the model is analogous to the decoder in the previous Generative Compression, imitating the process of reconstructing images to obtain deep features. Experiments show that the proposed method can obtain compressed-domain features with good discriminability and generalization, obtaining 94% classification accuracy results on the UCML scene classification data set. © 2021 IEEE.

关键词： image classification

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Research on GISC image Reconstruction Algorithm Based on Area Array CCD image Sensor

Research on GISC Image Reconstruction Algorithm Based on Are...

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signal processing, Communications and Engineering Management conference (ISPCEM), International

作者： Ziyi Shen Meixuan Li Zhipeng Yang Honglin Wang Xiaofei Fan Feng Yang Jilin Engineering Normal University Changchun China Institute for Interdisciplinary Quantum Information Technology Jilin Engineering Normal University Changchun China

Ghost imaging sparsity constraints (GISC) spectral camera can acquire both spatial and spectral information of surface features, and has significant application prospects in the field of satellite remote sensing. This article uses a planar CCD image sensor for photoelectric detection, and then reconstructs multispectral images through image reconstruction algorithms. The article describes the theoretical foundation, related technologies, and image reconstruction algorithms of GISC. Reconstruct the three-dimensional spectral image of the target through single exposure detection of the speckle field and pre calibrated measurement matrix. The research results have advantages such as high energy utilization rate, fast information acquisition efficiency, and the ability to obtain three-dimensional data information with a single exposure.

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Classification of MRI's images using the Fractional Fourier Transform and the Local Binary Pattern information by Artificial Neural Network 29

Classification of MRI's images using the Fractional Fourier ...

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conference on Infrared remote sensing and Instrumentation xxix

作者： Estrada Marmolejo, Carlos A. Alvarez-Borrego, Josue Garcia-Torales, Guillermo Flores Nunez, Jorge Luis Univ Guadalajara Av Revoluc 1500 Guadalajara 44430 Jalisco Mexico CICESE Carretera Ensenada Tijuana 3918 Zona Playitas Ensenada 22860 Baja California Mexico

ISBN: (数字)9781510644991

ISBN: (纸本)9781510644991;9781510644984

Pattern recognition techniques are widely used in computer vision, classification of radio signals, and voice recognition. The fractional Fourier transform is used to recognize patterns using binary rings masks and segment images. This technique has the characteristic of being invariant to position and rotation and finally obtaining a one-dimensional signature. On the other hand, Neural Networks are used for pattern recognition based on a deep neural network algorithm. It has the characteristic of training large datasets with millions of images. Artificial Neural Networks(ANNs) are used for several applications such as pattern recognition and classification of input data. In particular, the ANN has been used to evaluate medical images from the brain to assess if the image corresponds to Alzheimer's disease. One disadvantage of the neural network is a large amount of time to learn depending on the number of patterns to be identified or classified and the ability to adapt and recognize patterns. Besides, the fractional Fourier transform cannot analyze a large amount of information. In this work, a comparison between the Artificial Neural Network and the Fractional Fourier Transform is presented to determine which will be the best for recognizing a batch of selected medical images. We propose a reconstruction method using both techniques for precise image recognition and the evaluation of their respective metrics such as accuracy, precision, sensitivity, and specificity. The medical images regarding Alzheimer's disease are no dementia, very mild dementia, mild dementia presenting the best perfomance regarding the receiver operating characteristics and moderate dementia was the worst classified related to the number of images of the dataset.

关键词： Pattern recognition one-dimensional signature fractional Fourier transform binary rings masks Artificial Neural Network computer vision image processing local binary pattern neural network

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Contrast Enhancement and ROI detection in satellite images Application to the environment and sustainable development

Contrast Enhancement and ROI detection in satellite images A...

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International conference on image and signal processing and their Applications (ISPA)

作者： Aicha Mehidi Malika Mimi Abdallah Mehidi Soraya Zehani Electrical Engineering Department Laboratory of Signals and Systems Faculty of Sciences and Technology University of Abdelhamid Ibn Badis Mostaganem Algeria Laboratory of Signals and Systems Faculty of Sciences and Technology University of Abdelhamid Ibn Badis Mostaganem Algeria Electrical Engineering Department Laboratory of Vision and Communication Systems Faculty of Sciences and Technology University of Biskra

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

During the last few years, remote sensing is considerably used for Earth observation for the environment and sustainable development. The temporal classes of satellite images provide better information for monitoring the earth's surface at different scales; therefore these images are becoming a very relevant opportunity of investigating. The numerical analyses of image processing are based on the artificial intelligence (AI) tool rather than the traditional methods. The environmental indicators from these images offer very important statistics, the carried out work of dynamic phenomena, the observation event and interpretation of evolving circumstances like: atmospheric conditions, crises and natural disasters taking the opportunity to be studied, several problems come out of deforestation and monitoring of water resources (water bodies). The purpose of this study is focused on drawing out details from the database used and then to give an offer for a texture analysis strategy. Firstly, using specific development and then developing the existing software in the second hand. This work is to establish an algorithm for the detection of region of interest (water body) which is based on the theory of fuzzy logic and the hypothesis of Fuzzy C-Means.

关键词： Earth image segmentation Software algorithms signal processing algorithms Satellite images Sustainable development Artificial intelligence

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Difference Guided VHR remote sensing image Change Detection

Difference Guided VHR Remote Sensing Image Change Detection

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

作者： Jiukai Sun Ganchao Liu Xuelong Li Yuan Yuan School of Computer Science Ministry of Industry and Information Technology Xi’an P. R. China School of Artificial Intelligence OPtics and ElectroNics (iOPEN) Ministry of Industry and Information Technology Xi’an P. R. China Northwestern Polytechnical University Xi’an P.R. China Key Laboratory of Intelligent Interaction and Applications (Northwestern Polytechnical University) Ministry of Industry and Information Technology Xi’an P. R. China

VHR remote sensing images have abundant ground features and details, but it is a great challenge for machine understanding. The "same object with different spectral" problem caused by environment changes, such as seasonal alternation, bad weather and shadow, is the biggest challenge in multitemporal image change detection, which is more prominent in VHR images. For this problem, a novel difference guided VHR image change detection (DGCD) method is proposed in this paper. In the feature learning stage of DGCD model, difference features are used to guide the feature extraction to suit with the change detection task. In order to make the model focus on the change features, both of the spatial and channel attention mechanism are introduced. Finally, for the edge region of VHR image which is hard to be discriminated, a new edge enhanced loss function based on BCL loss is designed. Experiments on public datasets show the superiority of proposed DGCD method. It has good generalization ability in different classical challenging scenarios. Compared with the representative methods in recent years, the proposed DGCD method performs better on VHR image change detection.

关键词： Representation learning image edge detection Feature extraction Encoding Decoding Task analysis Speech processing

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Multispectral image Compression Algorithm Based on Sliced Convolutional LSTM 10th

Multispectral Image Compression Algorithm Based on Sliced Co...

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10th International conference on Communications, signal processing, and Systems, CSPS 2021

作者： Wang, Kang Zhang, Ning Hu, Kedi Cao, Tongbo College of Astronautics Nanjing University of Aeronautics and Astronautics Nanjing210016 China Xian710065 China

ISBN: (纸本)9789811903854

The multispectral imaging which are used for remote sensing imaging has a large amount of data, so this paper proposed a deep learning method which is based on sliced convolutional LSTM for multispectral image compression. Compared with other algorithms, the proposed algorithm further compresses the multispectral images by considering the similarity between the spectra and removing the inter-spectral redundancy. The proposed algorithm is based on end to end framework which is consist of encoder, decoder, entropy coding and quantizer. In experiments, the PSNR of proposed model is compared with that of JPEG2000 to evaluate the performance of our algorithm at several different bit rates. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolution

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