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A Modified Sparse Reconstruction Method for Three-Dimensional Synthetic Aperture Radar image 10

A Modified Sparse Reconstruction Method for Three-Dimensiona...

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10th international symposium on multispectral image processing and pattern recognition (MIPPR) - Remote Sensing image processing, Geographic Information Systems, and Other Applications

作者： Zhang, Ziqiang Ji, Kefeng Song, Haibo Zou, Huanxin Natl Univ Def Technol Coll Elect Sci & Engn Changsha 410073 Hunan Peoples R China

ISBN: (数字)9781510617261

ISBN: (纸本)9781510617261;9781510617254

There is an increasing interest in three-dimensional Synthetic Aperture Radar (3-D SAR) imaging from observed sparse scattering data. However, the existing 3-D sparse imaging method requires large computing times and storage capacity. In this paper, we propose a modified method for the sparse 3-D SAR imaging. The method processes the collection of noisy SAR measurements, usually collected over nonlinear flight paths, and outputs 3-D SAR imagery. Firstly, the 3-D sparse reconstruction problem is transformed into a series of 2-D slices reconstruction problem by range compression. Then the slices are reconstructed by the modified SL0 (smoothed l(0) norm) reconstruction algorithm. The improved algorithm uses hyperbolic tangent function instead of the Gaussian function to approximate the l(0) norm and uses the Newton direction instead of the steepest descent direction, which can speed up the convergence rate of the SLO algorithm. Finally, numerical simulation results are given to demonstrate the effectiveness of the proposed algorithm. It is shown that our method, compared with existing 3-D sparse imaging method, performs better in reconstruction quality and the reconstruction time.

关键词： Synthetic Aperture Radar three-dimensional imaging sparse reconstruction smoothed l(0) norm

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Ship detection using STFT sea background statistical modeling for largescale oceansat remote sensing images 10

Ship detection using STFT sea background statistical modelin...

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10th international symposium on multispectral image processing and pattern recognition (MIPPR) - Remote Sensing image processing, Geographic Information Systems, and Other Applications

作者： Wang, Lixia Pei, Jihong Xie, Weixin Liu, Jinyuan Shenzhen Univ ATR Key Lab Natl Def Technol Shenzhen 518060 Peoples R China Shenzhen Univ Coll Optoelect Engn Minist Educ & Guangdong Prov Key Lab Optoelect Devices & Syst Shenzhen 518060 Peoples R China

ISBN: (数字)9781510617261

ISBN: (纸本)9781510617261;9781510617254

Large-scale oceansat remote sensing images cover a big area sea surface, which fluctuation can be considered as a non-stationary process. Short-Time Fourier Transform (STFT) is a suitable analysis tool for the time varying non stationary signal. In this paper, a novel ship detection method using 2-D STFT sea background statistical modeling for large-scale oceansat remote sensing images is proposed. First, the paper divides the large-scale oceansat remote sensing image into small sub-blocks, and 2-D STFT is applied to each sub-block individually. Second, the 2-D STFT spectrum of sub-blocks is studied and the obvious different characteristic between sea background and non-sea background is found. Finally, the statistical model for all valid frequency points in the STFT spectrum of sea background is given, and the ship detection method based on the 2-D STFT spectrum modeling is proposed. The experimental result shows that the proposed algorithm can detect ship targets with high recall rate and low missing rate.

关键词： oceansat remote sensing image STFT sea background statistical modeling ship detection

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Proceedings of SPIE - The international Society for Optical Engineering

Proceedings of SPIE - The International Society for Optical ...

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10th international symposium on multispectral image processing and pattern recognition, MIPPR 2017

ISBN: (纸本)9781510617179

The proceedings contain 32 papers. The topics discussed include: analysis of periodically patterned metallic nanostructures for infrared absorber;mapping soil total nitrogen of cultivated land at county scale by using hyperspectral image;flexible electronic control system based on FPGA for liquid-crystal microlens;a high-transmission liquid-crystal Fabry-Perot infrared filter for electrically tunable spectral imaging detection;a prosail-based spectral unmixing algorithm for solving vegetation spectral variability problem;a blind deconvolution method based on L1/L2 regularization priors in the gradient space;nano-focusing effect simulation of metal film with subwavelength pattern structures;panchromatic cooperative hyperspectral adaptive wide band deletion repair method;infrared and visible images fusion based on visual saliency map and NSCT;three-dimensional imaging through turbid media based on polarization-difference liquid-crystal microlens array;liquid-crystal microlens array with swing and adjusting focus and constructed by dual patterned ITO-electrodes;subsidence monitoring and prediction of high-speed railway in Beijing with multitemporal TerraSAR-X data;fisheye image rectification using spherical and digital distortion models;and water vapor retrieval from near-IR measurements of polarized scanning atmospheric corrector.

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Infrared video based gas leak detection method using modified FAST features 10

Infrared video based gas leak detection method using modifie...

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10th international symposium on multispectral image processing and pattern recognition (MIPPR) - Remote Sensing image processing, Geographic Information Systems, and Other Applications

作者： Wang, Min Hong, Hanyu Huang, Likun Wuhan Inst Technol Lab Image Proc & Intelligent Control Wuhan 430074 Hubei Peoples R China

ISBN: (数字)9781510617261

ISBN: (纸本)9781510617261;9781510617254

In order to detect the invisible leaking gas that is usually dangerous and easily leads to fire or explosion in time, many new technologies have arisen in the recent years, among which the infrared video based gas leak detection is widely recognized as a viable tool. However, all the moving regions of a video frame can be detected as leaking gas regions by the existing infrared video based gas leak detection methods, without discriminating the property of each detected region, e.g., a walking person in a video frame may be also detected as gas by the current gas leak detection methods. To solve this problem, we propose a novel infrared video based gas leak detection method in this paper, which is able to effectively suppress strong motion disturbances. Firstly, the Gaussian mixture model(GMM) is used to establish the background model. Then due to the observation that the shapes of gas regions are different from most rigid moving objects, we modify the Features From Accelerated Segment Test ( FAST) algorithm and use the modified FAST (mFAST) features to describe each connected component. In view of the fact that the statistical property of the mFAST features extracted from gas regions is different from that of other motion regions, we propose the Pixel-Per-Points (PPP) condition to further select candidate connected components. Experimental results show that the algorithm is able to effectively suppress most strong motion disturbances and achieve real-time leaking gas detection.

关键词： leaking gas infrared video Gaussian mixture model modified FAST

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Multi-scale image segmentation method with visual saliency constraints and its application 10

Multi-scale image segmentation method with visual saliency c...

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10th international symposium on multispectral image processing and pattern recognition (MIPPR) - Remote Sensing image processing, Geographic Information Systems, and Other Applications

作者： Chen, Yan Yu, Jie Sun, Kaimin WenHua Coll Dept Informat Sci & Technol WenHua Rd Wuhan 430074 Hubei Peoples R China Wuhan Univ Off Sci Res & Dev Wuhan 430074 Hubei Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R 129 Luoyu Rd Wuhan 430079 Hubei Peoples R China

ISBN: (数字)9781510617261

ISBN: (纸本)9781510617261;9781510617254

Object-based image analysis method has many advantages over pixel-based methods, so it is one of the current research hotspots. It is very important to get the image objects by multi-scale image segmentation in order to carry out object based image analysis. The current popular image segmentation methods mainly share the bottom-up segmentation principle, which is simple to realize and the object boundaries obtained are accurate. However, the macro statistical characteristics of the image areas are difficult to be taken into account, and fragmented segmentation (or over segmentation) results are difficult to avoid. In addition, when it comes to information extraction, target recognition and other applications, image targets are not equally important, i.e., some specific targets or target groups with particular features worth more attention than the others. To avoid the problem of over-segmentation and highlight the targets of interest, this paper proposes a multi-scale image segmentation method with visually saliency graph constraints. Visual saliency theory and the typical feature extraction method are adopted to obtain the visual saliency information, especially the macroscopic information to be analyzed. The visual saliency information is used as a distribution map of homogeneity weight, where each pixel is given a weight. This weight acts as one of the merging constraints in the multi scale image segmentation. As a result, pixels that macroscopically belong to the same object but are locally different can be more likely assigned to one same object. In addition, due to the constraint of visual saliency model, the constraint ability over local-macroscopic characteristics can be well controlled during the segmentation process based on different objects. These controls will improve the completeness of visually saliency areas in the segmentation results while diluting the controlling effect for non-saliency background areas. Experiments show that this method works bet

关键词： Visual Saliency Multi-scale image Segmentation target detection over-segmentation

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Sparse representations via learned dictionaries for x-ray angiogram image denoising 10

Sparse representations via learned dictionaries for x-ray an...

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10th international symposium on multispectral image processing and pattern recognition, MIPPR 2017

作者： Shang, Jingfan Huang, Zhenghua Li, Qian Zhang, Tianxu North University of China China Wuhan Institute of Technology China Huazhong University of Science and Technology China

ISBN: (数字)9781510617247

ISBN: (纸本)9781510617230

X-ray angiogram image denoising is always an active research topic in the field of computer vision. In particular, the denoising performance of many existing methods had been greatly improved by the widely use of nonlocal similar patches. However, the only nonlocal self-similar (NSS) patch-based methods can be still be improved and extended. In this paper, we propose an image denoising model based on the sparsity of the NSS patches to obtain high denoising performance and high-quality image. In order to represent the sparsely NSS patches in every location of the image well and solve the image denoising model more efficiently, we obtain dictionaries as a global image prior by the K-SVD algorithm over the processing image;Then the single and effectively alternating directions method of multipliers (ADMM) method is used to solve the image denoising model. The results of widely synthetic experiments demonstrate that, owing to learned dictionaries by K-SVD algorithm, a sparsely augmented lagrangian image denoising (SALID) model, which perform effectively, obtains a state-of-the-art denoising performance and better high-quality images. Moreover, we also give some denoising results of clinical X-ray angiogram images. © 2018 SPIE.

关键词： image denoising

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Proceedings of SPIE - The international Society for Optical Engineering

Proceedings of SPIE - The International Society for Optical ...

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10th international symposium on multispectral image processing and pattern recognition, MIPPR 2017

ISBN: (纸本)9781510617254

The proceedings contain 61 papers. The topics discussed include: a weighted variational gradient-based fusion method for high-fidelity thin cloud removal of Landsat images;land cover's refined classification based on multi source of remote sensing information fusion: a case study of national geographic conditions census in China;automatic road extraction from high resolution remote sensing image by means of topological derivative and mathematical morphology;depth data research of GIS based on clustering analysis algorithm;airplane detection based on fusion framework by combining saliency model with deep convolutional neural networks;bathymetric mapping of shallow water surrounding Dongsha Island using QuickBird image;A NDVI assisted remote sensing image adaptive scale segmentation method;the investigation of identifying method on grass fire by FY-3 VIRR images;comparison of four machine learning methods for object-oriented change detection in high-resolution satellite imagery;multi-scale image segmentation method with visual saliency constraints and its application;study on spatial-temporal change of Changsha-Zhuzhou-Xiangtan urban agglomeration based on DMSP/OLS night light data;evidential analysis of difference images for change detection of multitemporal remote sensing images;the influence on the interferometry due to the instability of ground-based synthetic aperture radar work platform;and comparison of inversion accuracy of soil copper content from vegetation indices under different spectral resolution.

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Color Correction Using Weighted Moving Least Squares in image Mosaicking Applications 10

Color Correction Using Weighted Moving Least Squares in Imag...

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10th international symposium on multispectral image processing and pattern recognition (MIPPR) - pattern recognition and Computer Vision

作者： Du, Chengcai Zhong, Sheng Huazhong Univ Sci & Technol Sci & Technol Multispectral Informat Proc Lab Wuhan 430074 Hubei Peoples R China Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9781510617223;9781510617216

In image mosaicking applications, the color of images for mosaicking may be inconsistent due to different camera settings and lighting conditions. This paper proposes an effective color correction algorithm to correct the photometrical disparities. Firstly, corresponding points between two images are extracted using SIFT Flow. Secondly, the corresponding points will serve as the control points of the weighted moving least squares algorithm to correct the color of input image. This operation is conducted for each channel of input image respectively in RGB space. Finally, combining three corrected single channel image, we get the final corrected RGB image. Experimental results have shown that the proposed color correction method yields better performance than the state-of-art algorithms.

关键词： image mosaicking color correction weighted moving least squares

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Robust image matching via ORB feature and VFC for mismatch removal 10

Robust image matching via ORB feature and VFC for mismatch r...

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10th international symposium on multispectral image processing and pattern recognition (MIPPR) - pattern recognition and Computer Vision

作者： Ma, Tao Fu, Wenxing Fang, Bin Hu, Fangyu Quan, Siwen Ma, Jie Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Hubei Peoples R China China Aerosp Sci & Ind Corp Res Acad 3 Beijing 100074 Peoples R China

ISBN: (纸本)9781510617223;9781510617216

image matching is at the base of many image processing and computer vision problems, such as object recognition or structure from motion. Current methods rely on good feature descriptors and mismatch removal strategies for detection and matching. In this paper, we proposed a robust image match approach based on ORB feature and VFC for mismatch removal. ORB (Oriented FAST and Rotated BRIEF) is an outstanding feature, it has the same performance as SIFT with lower cost. VFC (Vector Field Consensus) is a state-of-the-art mismatch removing method. The experiment results demonstrate that our method is efficient and robust.

关键词： image matching ORB feature vector field consensus local invariant feature

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Single image super-resolution based on convolutional neural networks 10

Single image super-resolution based on convolutional neural ...

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10th international symposium on multispectral image processing and pattern recognition (MIPPR) - pattern recognition and Computer Vision

作者： Zou, Lamei Luo, Ming Yang, Weidong Li, Peng Jin, Liujia Huazhong Univ Sci & Technol Sch Automat Sci & Technol Multispectral Informat Proc Lab Wuhan 430074 Hubei Peoples R China

ISBN: (纸本)9781510617223;9781510617216

We present a deep learning method for single image super-resolution (SISR). The proposed approach learns end-to-end mapping between low-resolution (LR) images and high-resolution (HR) images. The mapping is represented as a deep convolutional neural network which inputs the LR image and outputs the HR image. Our network uses 5 convolution layers, which kernels size include 5x5, 3x3 and 1x1. In our proposed network, we use residual-learning and combine different sizes of convolution kernels at the same layer. The experiment results show that our proposed method performs better than the existing methods in reconstructing quality index and human visual effects on benchmarked images.

关键词： single image super resolution convolutional neural networks residual-learning

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