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A blind deconvolution method based on L1/L2 regularization prior in the gradient space 10

A blind deconvolution method based on L1/L2 regularization p...

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

作者： Cai, Ying Shi, Yu Hua, Xia School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan Hubei430074 China

ISBN: (数字)9781510617186

ISBN: (纸本)9781510617179

In the process of image restoration, the result of image restoration is very different from the real image because of the existence of noise, in order to solve the ill posed problem in image restoration, a blind deconvolution method based on L1/L2 regularization prior to gradient domain is proposed. the method presented in this paper first adds a function to the prior knowledge, which is the ratio of the L1 norm to the L2 norm, and takes the function as the penalty term in the high frequency domain of the image. then, the function is iteratively updated, and the iterative shrinkage threshold algorithm is applied to solve the high frequency image. In this paper, it is considered that the information in the gradient domain is better for the estimation of blur kernel, so the blur kernel is estimated in the gradient domain. this problem can be quickly implemented in the frequency domain by fast Fast Fourier Transform. In addition, in order to improve the effectiveness of the algorithm, we have added a multi-scale iterative optimization method. this paper proposes the blind deconvolution method based on L1/L2 regularization priors in the gradient space can obtain the unique and stable solution in the process of image restoration, which not only keeps the edges and details of the image, but also ensures the accuracy of the results. © 2018 SPIE.

关键词： image reconstruction

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Bathymetric mapping of shallow water surrounding Dongsha Island using QuickBird image 10

Bathymetric mapping of shallow water surrounding Dongsha Isl...

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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： Li, Dongling Zhang, Huaguo Lou, Xiulin Second Inst Oceanog State Key Lab Satellite Ocean Environm Dynam Hangzhou 310012 Zhejiang Peoples R China

ISBN: (数字)9781510617261

ISBN: (纸本)9781510617261;9781510617254

this article presents an experiment of water depth inversion using the band ratio method in Dongsha Island shallow water. the remote sensing data is from QuickBird satellite on April 19, 2004. the bathymetry result shows an extensive agreement with the charted depths. 129 points from the chart depth data were chosen to evaluate the accuracy of the inversion depth. the results show that when the water depth is less than 20m, the inversion depth is accord with the chart, while the water depth is more than 20m, the inversion depth is still among 1525m. therefore, the remote sensing methods can only be effective with the inversion of 20m in Dongsha Island shallow water, rather than in deep water area. the total of 109 depth points less than 20m were used to evaluate the accuracy, the root mean square error is 2.2m.

关键词： Bathymetric mapping remote sensing shallow water Dongsha Island

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A threshold auto-adjustment algorithm of feature points extraction based on grid 10

A threshold auto-adjustment algorithm of feature points extr...

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

作者： Yao, Zili Li, Jun Dong, Gaojie School of Electronic Engineering and Automation Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Optoelectronic Information Processing Guilin University of Electronic Technology Guilin541004 China

ISBN: (数字)9781510617209

ISBN: (纸本)9781510617193

When dealing with high-resolution digital images, detection of feature points is usually the very first important step. Valid feature points depend on the threshold. If the threshold is too low, plenty of feature points will be detected, and they may be aggregated in the rich texture regions, which consequently not only affects the speed of feature description, but also aggravates the burden of following processing;if the threshold is set high, the feature points in poor texture area will lack. To solve these problems, this paper proposes a threshold auto-adjustment method of feature extraction based on grid. By dividing the image into numbers of grid, threshold is set in every local grid for extracting the feature points. When the number of feature points does not meet the threshold requirement, the threshold will be adjusted automatically to change the final number of feature points the experimental results show that feature points produced by our method is more uniform and representative, which avoids the aggregation of feature points and greatly reduces the complexity of following work. © 2018 SPIE.

关键词： Feature extraction

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Introduction

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

引用

Proceedings of SPIE - the international Society for Optical Engineering 2018年 10611卷 xiii-xiv页

作者： Bhanu, Bir

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Fusion of shallow and deep features for classification of high-resolution remote sensing images 10

Fusion of shallow and deep features for classification of hi...

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10th international symposium on multispectral image processing and pattern recognition (mippr) - multispectral image Acquisition, processing, and Analysis (mippr)

作者： Gao, Lang Tian, Tian Sun, Xiao Li, Hang China Univ Geosci Sch Comp Sci Hubei Key Lab Intelligent Geoinformat Proc 388 Lumo Rd Wuhan 430074 Hubei Peoples R China China Acad Launch Vehicle Technol Beijing 100076 Peoples R China

ISBN: (数字)9781510617186

ISBN: (纸本)9781510617186;9781510617179

Effective spectral and spatial pixel description plays a significant role for the classification of high resolution remote sensing images. Current approaches of pixel-based feature extraction are of two main kinds. one includes the widely used principal component analysis (PCA) and gray level co-occurrence matrix (GLCM) as the representative of the shallow spectral and shape features, and the other refers to the deep learning-based methods which employ deep neural networks and have made great promotion on classification accuracy. However, the former traditional features are insufficient to depict complex distribution of high resolution images, while the deep features demand plenty of samples to train the network otherwise over fitting easily occurs if only limited samples are involved in the training. In view of the above, we propose a GLCM-based convolution neural network (CNN) approach to extract features and implement classification for high resolution remote sensing images. the employment of GLCM is able to represent the original images and eliminate redundant information and undesired noises. Meanwhile, taking shallow features as the input of deep network will contribute to a better guidance and interpretability. In consideration of the amount of samples, some strategies such as L2 regularization and dropout methods are used to prevent over-fitting. the fine-tuning strategy is also used in our study to reduce training time and further enhance the generalization performance of the network. Experiments with popular data sets such as PaviaU data validate that our proposed method leads to a performance improvement compared to individual involved approaches.

关键词： image classification convolutional neural network gray-level co-occurrence matrix high-resolution remote sensing imagery

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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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A sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image 10

A sea-land segmentation algorithm based on multi-feature fus...

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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： Li, Jing Xie, Weixin Pei, Jihong ShenZhen Univ ATR Key Lab Natl Def Technol Shenzhen 518060 Peoples R China

ISBN: (数字)9781510617261

ISBN: (纸本)9781510617261;9781510617254

Sea-land segmentation is one of the key technologies of sea target detection in remote sensing images. At present, the existing algorithms have the problems of low accuracy, low universality and poor automatic performance. this paper puts forward a sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image removing island. Firstly, the coastline data is extracted and all of land area is labeled by using the geographic information in large-field remote sensing image. Secondly, three features (local entropy, local texture and local gradient mean) is extracted in the sea-land border area, and the three features combine a 3D feature vector. And then the Multi Gaussian model is adopted to describe 3D feature vectors of sea background in the edge of the coastline. Based on this multi-gaussian sea background model, the sea pixels and land pixels near coastline are classified more precise. Finally, the coarse segmentation result and the fine segmentation result are fused to obtain the accurate sea-land segmentation. Comparing and analyzing the experimental results by subjective vision, it shows that the proposed method has high segmentation accuracy, wide applicability and strong anti-disturbance ability.

关键词： remote sensing image sea-land segmentation multi-feature fusion multi-gaussian background model

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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)9781510617230

the proceedings contain 13 papers. the topics discussed include: a fast method for single image haze removal based on multiscale dark channel prior;a GPU-based mipmapping method for water surface visualization;hardware-efficient implementation of digital FIR filter using fast first-order moment algorithm;simulation and analysis of traffic flow based on cellular automation;a novel configurable VLSI architecture design of window-based image processing method;novel structures for discrete Hartley transform based on first-order moments;route constraints model based on polychromatic sets;analysis of parameter estimation and optimization application of ant colony algorithm in vehicle routing problem;a threshold-based fixed predictor for JPEG-LS image compression;a block-based JPEG-LS compression technique with lossless region of interest;sparse representations via learned dictionaries for x-ray angiogram image denoising;research on segmentation based on multi-atlas in brain MR image;and application of deep learning in the identification of TAO.

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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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