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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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Modulation transfer function compensation for optical satellite image restoration using joint statistical model in curvelet domain 40

Modulation transfer function compensation for optical satell...

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40th Asian conference on remote sensing: Progress of remote sensing Technology for Smart Future, ACRS 2019

作者： Wong, Soo Mee Jessica Ng, Su Wai Tan, Adhwa Amir National Space Agency Ministry of Energy Science Technology Environment and Climate Change Banting42700 Malaysia

The heavy-tailed properties of Modulation Transfer Function (MTF) typically introduces noise and an unacceptable aliasing effect in the MTF compensation (MTFC) effort for spatial image quality improvement. These degradative effects compromise the image's signal to Noise (SNR). Consequently, users must evaluate the relative importance of image sharpness versus noise to their applications in deciding whether MTFC processing is appropriate. In this paper, we present a strategy for high-fidelity MTF compensation framework by characterizing the local smooth and nonlocal self-similarity properties of optical satellite images in hybrid space and frequency domain. To realize the objective of this work, we design regularization terms that reflect the local smoothness and nonlocal self-similarity properties of the image and establish a simple joint statistical model in Curvelet domain to combine these two properties. To make the regularization-based MTFC method tractable and robust, we employ multi-objective bilevel optimization approach to efficiently solve the severely ill-posed inverse problem of MTFC. We conduct extensive experiments to evaluate the proposed regularization-based MTFC method using synthetically blurred images simulated from level 2A product of IKONOS. Quantitative measurements of image quality reveal that the proposed method produces competitive restoration results with minimum computational complexity and exhibit a good convergence property. This experimental result shows that the proposed method can find a compromise between regularity to remove noise and preserving image fidelity. © 2020 40th Asian conference on remote sensing, ACRS 2019: "Progress of remote sensing Technology for Smart Future". All rights reserved.

关键词： Optical transfer function

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COMPARISON BETWEEN TWO RADIOMETRIC CALIBRATION METHODS APPLIED TO UAV MULTISPECTRAL imageS 26

COMPARISON BETWEEN TWO RADIOMETRIC CALIBRATION METHODS APPLI...

引用

conference on image and signal processing for remote sensing XXVI

作者： Porto, L. R. Imai, N. N. Berveglieri, A. Miyoshi, G. T. Moriya, E. A. Tommaselli, A. M. Honkavaara, E. Sao Paulo State Univ UNESP Post Grad Program Cartog Sci Presidente Prudente Brazil Sao Paulo State Univ UNESP Dept Cartog Presidente Prudente SP Brazil Finnish Geodet Inst Masala Finland

ISBN: (纸本)9781510638808

There are many advantages of using unmanned aerial vehicles (UAVs) in remote sensing but when using radiometrically corrected multispectral images. This study focuses on two techniques of obtain a multispectral orthomosaic with suitable radiometric quality considering a day period with minor variations in illumination and clouds. The first technique comprises a radiometric block adjustment combined with empirical line whilst the second technique uses only empirical line. Field measurements with spectrometers were used to assess the techniques. The obtained results show that the radiometric block adjustment presented better results when compared to the radiometric reference targets and its calculated Hemispherical Conical Reflectance Factor (HCRF) from the spectrometer. However, the root mean square error (RMSE), normalized root mean square error (NRMSE) and mean absolute percentage error (MAPE) were similar in both cases, showing that the two proposed workflows can generate multispectral mosaics with acceptable radiometric quality for a period in which illumination conditions are stable. images difference between each band was produced showing that there was a stronger variation of pixels in the higher slope region, which indicates that additional corrections beyond empirical line are needed in these situations.

关键词： Radiometric block adjustment Unmanned aerial vehicles - UAV Multispectral images Empirical line Mosaic generation image processing

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Video Super Resolution Using Temporal Encoding ConvLSTM and Multi-Stage Fusion

Video Super Resolution Using Temporal Encoding ConvLSTM and ...

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IEEE International conference on Visual Communications and image processing (VCIP)

作者： Zhang, Yuhang Chen, Zhenzhong Liu, Shan Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Peoples R China Tencent Media Lab Palo Alto CA USA

ISBN: (纸本)9781728180687

Video super resolution is a challenging task and has attracted the attention of many researchers in recent years. In this paper, we propose a multi-stage spatio-temporal feature fusion network. Different from existing methods that only aggregate features from temporal branch once at a specified stage of network, the proposed network is organized in a multi-stage manner so that the temporal correlation in features at different stages of the network can be fully exploited. Furthermore, we propose the temporal encoding convLSTM to effectively capture the temporal information at the end of each stage. Experiments on vid4 and viemo-90K demonstrate the effectiveness of the proposed method.

关键词： video super resolution temporal correlation convLSTM

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Study on the Influence of image Motion on image Radiation Quality

Study on the Influence of Image Motion on Image Radiation Qu...

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7th International conference on Communications, signal processing, and Systems (CSPS)

作者： Yang, Fan Wu, Zhaocong Zeng, Jisheng Wei, Zheng SOA South China Sea Inst Planning & Environm Res Guangzhou Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Peoples R China

ISBN: (纸本)9789811365041;9789811365034

This paper conducted depth and systematic research on the influence of image motion on radiation quality. The exploration of the regular image radiation quality could be a great significance for improving the image quality. The source of abnormal image motion was analyzed in terms of platform motion and flutter, attitude control error, and image motion compensation error. The model of point spread function under abnormal image motion was established based on the analysis of superposition model between adjacent pixels and PSF variation model. The radiation information and MTF caused by PSF changes were analyzed. The experiment shows that the authenticity of the radiation information expressing the imaging target is weakened. The larger the abnormal image motion, the faster the MTF curve declines, indicates the overall image quality decreases, the spatial resolution decreases, and the sharpness decreases.

关键词： Abnormal image motion Point spread function Radiation quality

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Fast processing of UAV remote sensing image Based on DSP Hardware Simulation Platform 4

Fast Processing of UAV Remote Sensing Image Based on DSP Har...

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2019 4th International Seminar on Computer Technology, Mechanical and Electrical Engineering, ISCME 2019

作者： Liu, Fan Guan, Guixia Zhao, Haimeng Sun, Peng Yan, Lei Capital Normal Universityt Information Engineering College Beijing100048 China Guilin University of Aerospace Technology Key Laboratory of Remote Sensing Surveying and Mapping UAV in Universities of Guangxi Guangxi GuiLin541004 China Peking University Space Information Integration and 3S Engineering Application Key Laboratory of Beijing BeiJing100871 China

The traditional unmanned aerial vehicle (UAV) remote sensing image processing algorithm is difficult to guarantee both accuracy and speed when dealing with massive data of UAV network. This paper designs an on-the-spot processing system based on portable digital signal processor (DSP) hardware simulation platform. The bilinear interpolation algorithm is optimized and improved for the adaptability of the DSP platform with the reference of the bicubic interpolation algorithm, which is long time-consuming but high accuracy. The nearest neighbor interpolation is merged in the bilinear interpolation algorithm, which improves the edge blurring problem of bilinear interpolation algorithm. In the loop calculation of the multiplication and addition iteration calculation segment of the algorithm, the parallelism of the instruction level is increased by increasing the instruction level parallelism between the loop bodies. Experiments show that, on the basis of ensuring the accuracy of the bilinear interpolation algorithm, the execution speed of the processing algorithm is improved, which can meet the requirements of fast real-time processing of remote sensing images of complex unmanned aerial vehicles. © Published under licence by IOP Publishing Ltd.

关键词： Digital signal processors

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RATE CONTROL FOR VERSATILE VIDEO CODING

RATE CONTROL FOR VERSATILE VIDEO CODING

引用

IEEE International conference on image processing (ICIP)

作者： Li, Yiming Liu, Zizheng Chen, Zhenzhong Liu, Shan Wuhan Univ Sch Comp Sci Wuhan Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Peoples R China Tencent Media Lab Palo Alto CA USA

ISBN: (纸本)9781728163956

Rate control plays an important role in practical video coding. In this paper, we present a rate control method for Versatile Video Coding (VVC). We conduct some statistical analysis to investigate the influence of the skip block upon the rate-distortion parameter estimation and quality dependency among frames. Based on the analysis, we propose the rate-distortion parameter updating strategy and establish a quality dependency factor based frame-level bit allocation scheme. Experimental results show that the proposed method can achieve up to 7.31%/5.44% BD-rate improvements in Low-Delay/Random-Access configuration upon previous rate control method in VVC respectively. The presented rate control algorithm has already been adopted into VVC VTM platform by Joint Video Experts Team (JVET).

关键词： Video coding Rate control VVC Skip block

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Analysis of Influencing Factors of Marine Polarized Reflectivity Based on Marine Polarization Characteristics Modeling 5

Analysis of Influencing Factors of Marine Polarized Reflecti...

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5th IEEE International conference on signal and image processing, ICSIP 2020

作者： Du, Jun Wang, Biao Zhu, Lingxuan Lin, Jiaxuan Xu, Zhenyu Shanghai Institute of Radio Equipment Key Lab of Electromagnetic Scattering Shanghai China Shanghai Institute of Satellite Engineering Lab of Satellite Load Shanghai China

ISBN: (纸本)9781728168968

With the development of optical remote sensing technology, single spectral imaging cannot meet people's requirements for image information collection. Polarization imaging technology has very good achievements in target detection and recognition because it can effectively distinguish the target from the background. Based on the marine background, this paper builds a marine surface reflectance model based on the Cox-Munk model, introduces the Stokes vector to model the polarization characteristics of the marine surface, and conducts a certain degree of simulation work for different influencing factors. According to the analysis, it is found that the wind speed has a large influence on the polarized reflectance of the ocean, while the wind direction has little effect on it. Under a fixed observation angle, the polarization information received from the observation point is mainly related to the solar zenith angle. © 2020 IEEE.

关键词： Optical remote sensing

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Lightweight Multilevel Feature Fusion Network for Hyperspectral image Classification

Lightweight Multilevel Feature Fusion Network for Hyperspect...

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IEEE International conference on Information Communication and signal processing (ICICSP)

作者： Quanyu Huang Haojin Tang Weixin Xie Yanshan Li ATR National Key Laboratory of Defense Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

Hyperspectral image (HSI) classification is the key technology of remote sensing image processing. In recent years, convolutional neural network (CNN), which is a powerful feature extractor, has been introduced into the field of HSI classification. Since the features of HSI are the basis of HSI classification, how to effectively extract the spectral-spatial features from HSI with CNN has become a research hotspot. The HSI feature extraction network, based on two-dimensional (2D) and three-dimensional (3D) CNN which can extract both spectral and spatial information, may lead to the increase of parameters and computational cost. Compared with 2D CNN and 3D CNN, the number of parameters and computational cost of one-dimensional (1D) CNN will be greatly reduced. However, 1D CNN based algorithms can only extract the spectral information without considering the spatial information. Therefore, in this paper, a lightweight multilevel feature fusion network (LMFFN) is proposed for HSI classification, which aims to achieve efficient extraction of spectral-spatial features and to minimize the number of parameters. The main contributions of this paper are divided into the following two points: First, we design a hybrid spectral-spatial feature extraction network (HSSFEN) to combine the advantages of 1D, 2D and 3D CNN. It introduces the idea of depthwise separable convolution method, which effectively reduces the complexity of the proposed HSSFEN. Then, a multilevel spectral-spatial feature fusion network (MSSFFN) is proposed to further obtain more effective spectral-spatial features, which effectively fuses the bottom spectral-spatial features and the top spectral-spatial features. To demonstrate the performance of our proposed method, a series of experiments are conducted on three HSI datasets, including Indian Pine, University of Pavia, and Salinas Scene datasets. The experimental results indicate that our proposed LMFFN is able to achieve better performance than the manual f

关键词： Three-dimensional displays Convolution Two dimensional displays Manuals Feature extraction Computational efficiency Complexity theory

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TARGETED INCORPORATING SPATIAL INFORMATION IN SPARSE SUBSPACE CLUSTERING OF HYPERSPECTRAL remote sensing imageS

TARGETED INCORPORATING SPATIAL INFORMATION IN SPARSE SUBSPAC...

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IEEE International conference on image processing (ICIP)

作者： Zhan, Jiaqiyu Zhu, Yuesheng Bai, Zhiqiang Peking Univ Shenzhen Grad Sch Commun & Informat Secur Lab Beijing Peoples R China

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163956

Methods based on sparse subspace clustering (SSC) have shown great potential for hyperspectral image (HSI) clustering. However their performance is limited due to the complex spatial-spectral structure in HSIs. In this paper, a spatial best-fit direction (SBFD) algorithm is proposed to update the coefficients obtained from sparse representation to more discriminant features by integrating the spatial-contextual information given by the best-fit pixel of each target pixel. Also, SBFD is more targeted by searching for the best-fit direction than directly using the local window to do max pooling. The proposed SBFD was tested on two widely used hyperspectral dataset, the experimental results indicate its improvement in the clustering accuracy and spatial homogeneity.

关键词： Hyperspectral image sparse subspace clustering spatial information sparse representation

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FUSED RECURRENT NETWORK VIA CHANNEL ATTENTION FOR remote sensing SATELLITE image SUPER-RESOLUTION

FUSED RECURRENT NETWORK VIA CHANNEL ATTENTION FOR REMOTE SEN...

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IEEE International conference on Multimedia and Expo (ICME)

作者： Li, Xinyao Zhang, Dongyang Liang, Zhenwen Ouyang, Deqiang Shao, Jie Univ Elect Sci & Technol China Chengdu 611731 Peoples R China Sichuan Artificial Intelligence Res Inst Yibin 644000 Peoples R China

ISBN: (纸本)9781728113319

remote sensing satellite images often suffer from low spatial resolution. image super-resolution plays an important role in remote sensing image processing. However, existing methods show that increasing network depth will inevitably lead to the dramatic increase of model parameters and the over-fitting problem. Besides, most methods treat different types of information (low-frequency and high-frequency) equally. Motivated by these observations, we propose a fused recurrent network via channel attention (CA-FRN) in this paper. The basic module, recursive channel attention block (RCAB), pays enough attention to the high-frequency information and diminishes the low-frequency information adaptively through channel attention. Based on RCAB, we render our model effective by retaining and fusing hierarchical local information of both low-resolution and high-resolution, and we enhance the network performance simply by increasing the number of RCABs without adding extra parameters. We evaluate the proposed model on satellite images from different datasets, and the proposed CA-FRN is superior to the state-of-the-art methods. Code is available at https://***/lxy0922/CAFRN.

关键词： Satellite image super-resolution fused recurrent network channel attention

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