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Multi-Frame Super-Resolution Algorithm Based on Attention Mechanism 6

Multi-Frame Super-Resolution Algorithm Based on Attention Me...

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6th International conference on signal and image processing, ICSIP 2021

作者： Guo, Cheng Jiang, Juhao Wang, Qing Chen, Guannan Fujian Normal University College of Photonic and Electronic Engineering Fuzhou China

ISBN: (纸本)9780738133737

In the field of computer vision, image super-resolution is a difficult task, which have many applications in remote sensing, military and so on. In this paper, we introduce the convolutional block attention module (CBAM) into super-resolution problem, proposing a novel method called multi-frame super-resolution (MFSR) algorithm based on attention mechanism. Our proposed MFSR algorithm uses a three-layer CNN as benchmark and cascades CBAM at the end of each CNN block. The proposed algorithm can deliver a high-resolution output corresponding to the center (3rd) input frame. The average PSNR and SSIM of our algorithm are 33.318dB and 0.906 respectively, which outperform other MFSR algorithm. © 2021 IEEE.

关键词： remote sensing

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DIFFERENTIAL CONVOLUTION FEATURE GUIDED DEEP MULTI-SCALE MULTIPLE INSTANCE LEARNING FOR AERIAL SCENE CLASSIFICATION

DIFFERENTIAL CONVOLUTION FEATURE GUIDED DEEP MULTI-SCALE MUL...

引用

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

作者： Zhou, Beichen Yi, Jingjun Bi, Qi Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Peoples R China

ISBN: (纸本)9781728176055

Aerial image classification is challenging for current deep learning models due to the varied geo-spatial object scales and the complicated scene spatial arrangement. Thus, it is necessary to stress the key local feature response from a variety of scales so as to represent discriminative convolutional features. In this paper, we propose a deep multi-scale multiple instance learning (DMSMIL) framework to tackle the above challenges. Firstly, we develop a differential multi-scale dilated convolution feature extractor to exploit the different patterns from different scales. Then, the deep features of each scale are fed into a multiple instance learning module to generate a bag-level probability prediction. Lastly, probability predictions from all the MIL branches are fused to generate the final semantic prediction. Extensive experiments on three widely-utilized aerial scene classification benchmarks demonstrate that our proposed DMSMIL outperforms the state-of-the-art approaches by a large margin.

关键词： Deep multi-scale multiple instance learning differential dilated convolution features semantic prediction fusion scene classification aerial image

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Multi-year Crop Type Mapping using pre-trained deep Long-Short Term Memory and Sentinel 2 image Time Series 27

Multi-year Crop Type Mapping using pre-trained deep Long-Sho...

引用

conference on image and signal processing for remote sensing XXVII

作者： Weikmann, Giulio Paris, Claudia Bruzzone, Lorenzo Univ Trento Dept Informat Engn & Comp Sci Via Sommar 5 I-38123 Trento Italy

ISBN: (数字)9781510645691

ISBN: (纸本)9781510645691;9781510645684

This work presents a system for multi-year crop type mapping based on the multi-temporal Long Short-Term Memory (LSTM) Deep Learning (DL) model and Sentinel 2 image Time Series (TS). The method assumes the availability of a pre-trained LSTM model for a given year and aims to update the corresponding crop type map for a different year considering a small amount of recent reference data. To this end, the proposed approach combines Self-Paced Learning (SPL) and fine-tuning (FT) techniques. While the SPL technique gradually incorporates samples from crop types that can be classified with high-confidence by the pre-trained model, the FT strategy adapts the network to those classes having low-confidence accuracy. This condition allows us to reduce the labeled samples required to achieve accurate classification results. The experimental results obtained on three tiles of the Austrian country on TSs of Sentinel 2 data acquired in 2019 and 2020 (considering a model pre-trained on images of 2018) demonstrate the capability of the LSTM to adapt to TS of images with different temporal and radiometric characteristic with respect to the one used to pre-train the model, with a relatively small number of training samples. As expected, by directly applying the model without performing any adaptation, we obtain a mean F-score (F1%) of 64% and 62% compared to 76% and 70% achieved by the proposed technique with only 1500 samples for 2019 and 2020, respectively.

关键词： Long Short Term Memory (LSTM) automatic classification multi-temporal Deep Learning (DL) multi-temporal analysis multi-year crop type mapping remote sensing

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Video stabilization method corresponding to various imagery for geostationary optical Earth observation satellite 27

Video stabilization method corresponding to various imagery ...

引用

conference on image and signal processing for remote sensing XXVII

作者： Takeo, Y. Sekiguchi, T. Mitani, S. Mizutani, T. Shirasawa, Y. Kimura, T. Japan Aerosp Explorat Agcy JAXA Res & Dev Directorate 2-1-1 Sengen Tsukuba Ibaraki 3058505 Japan

ISBN: (数字)9781510645691

ISBN: (纸本)9781510645691;9781510645684

In geostationary Earth observation satellite system with high-resolution optical system under conceptual study phase developed by Japan Aerospace Exploration Agency (JAXA), one of its main products for users is a video with one fps captured by the telescope and the optical sensor;however, there have been few examples of satellite video with high-rate fps, especially in geostationary satellite. A satellite video has instability of the video sequences caused by an undesired disturbance of the satellite. In this study, we propose the video stabilization method corresponding to various satellite imagery characteristics, using feature point matching and bundle adjustment with eliminating outliers by using RANdom SAmple Consensus (RANSAC). Moreover, we adopt a polynomial approximation to smooth global motions of all video frames. We demonstrate the video stabilization using the experimental video captured by the helicopter. Moreover, the performance test result shows that the proposed method can stabilize the satellite video less than one pixel accuracy. In addition, we discuss various issues specific to a satellite imagery, for example, moving cloud.

关键词： Earth observation video stabilization image matching bundle adjustment

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An object-based fuzzy prior knowledge sparse coding algorithm for image fusion 27

An object-based fuzzy prior knowledge sparse coding algorith...

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conference on image and signal processing for remote sensing XXVII

作者： Vakilian, A. Asefpour Saradjian, M. R. Univ Tehran Univ Coll Engn Sch Surveying & Geospatial Engn Tehran *** Iran

ISBN: (数字)9781510645691

ISBN: (纸本)9781510645691;9781510645684

image fusion methods are designed to combine multiple input images with complementary information from a scene that can increase the interpretation capabilities of the objects. The spatial or spectral quality of a fusion method highly depends on the employed fusion method and prior information exploited from input data. Spectral distortion and spatial artifacts are common drawbacks in many image fusion methods. In this study, we propose a model-based image fusion approach using sparse representation (SR) with fuzzy prior knowledge, which incorporates three constraints as prior information for better modeling of the spatial and spectral characteristics of the fusion results. These prior constraints include sparsity of coefficients, patch-wise spectral similarity, and improvement of edge details extracted from a precise unsupervised classification based on a fuzzy inference system (FIS). Edge pixels are vulnerable to spectral distortions due to the spectral mixture of neighboring pixels. Therefore, an objects-based fuzzy classification was used to separate edge pixels from background information and smooth fusion results over edge pixels to prevent spectral distortion. Moreover, in the optimization step of sparse coding, the spectral basis was estimated along with sparse coefficients in an iterative procedure. Results from the proposed fusion approach over real satellite data revealed the promising performance of the proposed SR approach based on its quantitative and qualitative results. Objective and subjective enhancement of the proposed approach compared to other well-known fusion methods included pixel-wise detail preservation and object-based spectral injection to the fused image.

关键词： sparse representation object-based optimization pan-sharpening

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An Overview on remote sensing image Classification Methods with a Focus on Support Vector Machine

An Overview on Remote Sensing Image Classification Methods w...

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2021 International conference on signal processing and Machine Learning, CONF-SPML 2021

作者： Li, Haodong Lanzhou University School of Information Science and Engineering Lanzhou China

ISBN: (纸本)9781665417341

With the growing demand for better performance of remote sensing (RS) image classification, a variety of methods have been proposed in RS image classification field in recent years. In general, there are two categories of RS image classification methods: pixel-based (PB) approach and object-based (OB) approach. In this paper, RS image classification methods are reviewed from the perspective of PB approach and OB approach and, specifically, the development and characteristics of a promising methodology for RS image classification named support vector machine (SVM) are surveyed. SVM is particularly popular in the RS field since it can deal with small-sized training dataset and provide higher classification accuracy than some traditional methods like maximum likelihood classifier. Besides, SVM has advantages of high memory-efficiency and strong generalization. However, SVM-based approaches also suffer from some problems. For instance, SVM-based methods tend to overfit due to inappropriate choice of kernel functions and it is inefficient for them to determine the optimum kernel function parameters as well as to process hyperspectral images. This paper also proposes the improvement of SVM-based methods aiming to address the limitations and improve the performance of SVM in RS image classification field. Moreover, future directions for SVM in RS image classification field are presented, expecting to help researchers to find possible research focuses in the future. © 2021 IEEE.

关键词： Support vector machines

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Learned image Compression with Quantization Error Compensator

Learned Image Compression with Quantization Error Compensato...

引用

IEEE Visual Communications and image processing (VCIP)

作者： Nianxiang Fu Zhenzhong Chen Huairui Wang Shan Liu Hubei Luojia Laboratory Wuhan University Wuhan China School of Remote Sensing and Information Engineering Wuhan University Wuhan China Tencent Shenzhen China Tencent America Palo Alto CA USA

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Recent advancements in learned image compression methods have demonstrated superior rate-distortion performance and remarkable potential compared to traditional compression techniques. However, the core operation of quantization, inherent to lossy image compression, introduces errors that can degrade the quality of the reconstructed image. To address this challenge, we propose a novel Quantization Error Compensator (QEC), which leverages spatial context within latent representations and hyperprior information to effectively mitigate the impact of quantization error. Moreover, we propose a tailored quantization error optimization training strategy to further improve rate-distortion performance. Notably, QEC serves as a lightweight, plug-and-play module, offering high flexibility and seamless integration into various learned image compression methods. Extensive experimental results consistently demonstrate significant coding efficiency improvements achievable by incorporating the proposed QEC into state-of-the-art methods, with a slight increase in runtime.

关键词： Training image coding Quantization (signal) Runtime Codecs Visual communication Rate-distortion Distortion Optimization image reconstruction

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Efficient Chip Design of Penta Linear Array Charge Coupled Device (CCD)

Efficient Chip Design of Penta Linear Array Charge Coupled D...

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Electronics, Communication and signal processing (ICECSP), International conference on

作者： Kreena Desai Ayush Kumar Amit Kumar Department of Electronics and Communication Engineering Pandit Deendayal Energy University Gandhinagar India Space Applications Centre Indian Space Research Organization Ahmedabad India

ISBN: (数字)9798350364590

ISBN: (纸本)9798350375381

Charge coupled devices (CCDs) have historically played a crucial role in a variety of imaging and sensing tasks owing to their notable sensitivity, minimal noise, and impressive dynamic range. With technological progress, there is an increasing demand for CCDs with improved functionalities and efficiency to meet demands of various applications. One notable progression in CCD technology involves emergence of Penta Linear Array CCDs. This paper presents the design of a silicon-based CCD for space-based remote sensing in visible spectrum using Cadence Virtuoso software. The image sensor comprises linear five arrays, each linear array consisting of 6000 pixels. The pixel structure incorporates a 16×16 μm pinned photodiode, along with a storage gate, bias gate, transfer gate, and anti-blooming area. Each linear array is equipped with four readouts for charge-to-voltage conversion and amplification. The pixel array has been designed according to four poly and two metal layers. The clock fidelity simulations are performed using resistances and capacitances derived from the design layout in Cadence Virtuoso. The significant contributions of this paper lie in full chip CCD layout, encompassing subfield metal routing, device pad placement, and shield metal routing. The entire CCD chip design is thoroughly verified using design rule checks (DRCs), and subsequently generated a GDS file for fabrication.

关键词： Charge coupled devices Performance evaluation Fabrication CCD image sensors Layout Metals Logic gates Routing Chip scale packaging Sensor arrays

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Research on the Relative Radiation Consistency Correction Method for Dual-Line Array Infrared images 12th

Research on the Relative Radiation Consistency Correction Me...

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

作者： Gao, Hongxia Shi, Jiawei Wang, Shitao Institute of Remote Sensing Satellite China Academy of Space Technology Beijing100094 China

ISBN: (纸本)9789819649662

The relative radiation consistency of dual-line array images is crucial for the efficiency of in-orbit applications. This paper proposes a method that utilizes blackbody and scene information to correct the relative radiation consistency of line array images. Initially, the blackbody is used to perform a two-point correction on the two line array images to preliminarily calibrate the inconsistent responses of the pixels. Subsequently, scene-based correction is applied to one of the line array images, using the scanned image of this line array as the reference image and the scanned image of the other line array as the correction image for pixel-by-pixel fine-tuning, thereby calibrating the radiation intensity of the two line array images to the same level. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Dual-Line Array Infrared image Relative Radiation Consistency Correction

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Tele-Rheumatology-Advanced Neural Network Model for remote Assessment and Management of Rheumatic Conditions

Tele-Rheumatology-Advanced Neural Network Model for Remote A...

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International conference on Communications and signal processing

作者： S. Aghalya R. Sarasu Nilamadhab Mishra G. Arunachalam D. Gobinath Suresh Babu Thandullu Naganathan Department of Electronics and Communication Engineering St. Joseph’s College of Engineering OMR Chennai Tamil Nadu India Department of Spatial Informatics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Department of Electronics and Communication Engineering Gnanamani College of Technology (Autonomous) Namakkal Tamil Nadu India Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Adhiparasakthi Engineering College Melmaruvathur Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

The study presents an advanced neural network model for real-time remote evaluation and treatment of rheumatic diseases using sensor fusion. The model was developed for use in this investigation. The suggested system uses a wide variety of sensing modalities, such as image, motion, and biometric sensors. This is accomplished via artificial intelligence (AI) and the data gleaned from sensors. The neural network, which was trained on a dataset that was both varied and labeled, performs autonomous analyses of input data in order to accurately predict and categorize rheumatic diseases. The integration of the model into remote medical treatment is made easier by the fact that it is run on a Raspberry Pi inside a tele-rheumatology framework. This unique strategy not only improves diagnostic accuracy but also expands rheumatology services to geographically inaccessible or disadvantaged groups. As a result, the landscape of rheumatic healthcare delivery is completely transformed as a result of this new approach. For the purpose of ensuring the ethical use of this revolutionary technology, the procedures for data security and ethical concerns have been given priority.

关键词： Ethics Technological innovation Biological system modeling Neural networks Medical treatment signal processing Sensor fusion

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