检索结果-内蒙古大学图书馆

IEEE International conference on image processing (ICIP)

作者： Liu, Hai Zhang, Zhaoli Liu, Sanya Liu, Tingting Chang, Yi Cent China Normal Univ Natl Engn Res Ctr E Learning Wuhan 430079 Peoples R China

ISBN: (纸本)9781479983391

remote sensing image often suffers from the common problems of stripe noise and random noise. In this paper, we present a destriping method with unidirectional gradient L0 norm and L0 sparsity priori. The major novelty of the proposed method is that combining the unidirectional gradient L0 norm with the sparsity priori to address the destriping and denoising issues. Moreover, doubly augmented Lagrangian (DAL) method is adopted to solve the L0 regularized minimization problem. The proposed method is verified on heavily striped remote sensing images. Comparative results demonstrate that the proposed method outperforms the-state-of-art methods, which can suppress noise effectively as well as preserve image structures well.

关键词： Destriping remote sensing image Denoise Regularization L0 norm

来源：评论

学校读者我要写书评

暂无评论

SUPERPARSING BASED CHANGE DETECTION IN HIGH RESOLUTION remote sensing imageRY 12

SUPERPARSING BASED CHANGE DETECTION IN HIGH RESOLUTION REMOT...

引用

12th IEEE International conference on signal processing (ICSP)

作者： Ru, Hui Yang, Xiangli Peng, Dongqing Huang, Pingping Wuhan Univ Sch Elect Informat Wuhan 430072 Peoples R China Inner Mongolia Univ Technol Coll Informat Engn Hohhot 010051 Peoples R China

ISBN: (纸本)9781479921867

In this paper, we present a method to detect changes in high resolution remote sensing images based on superparsing proposed by Tighe et al. By comparing with several superpixel segmentation methods, we choose the SLIC (Simple Linear Iterative Clustering) method which can keep image boundary, produce consistent superpixels with similar size and shape, and also calculates fast. After superpixel segmentation, we obtain the category of each pixel in remote sensing images by using superparsing, therefore we can find change areas easily by comparing their category labels directly. Experiments on two Geo-Eye1 high-resolution remote sensing images demonstrate the effectiveness of our proposed change detection method.

关键词： high-resolution remote sensing images change detection superparsing

来源：评论

学校读者我要写书评

暂无评论

Foreword to the Special Issue on Analysis of Multitemporal remote sensing images

引用

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND remote sensing 2018年第12期11卷 4548-4550页

作者： Bruzzone, L. Deronde, B. Swinnen, E. Univ Trento Dept Comp Sci & Informat Engn I-38123 Trento Italy VITO Remote Sensing B-2400 Mol Belgium

The thirteen papers in this special section were presented at the 9th International Workshop on the Analysis of Multitemporal remote sensing images (MultiTemp 2017), hosted by VITO remote sensing on June 27-29, 2017.

关键词： Special issues and sections Meetings Vegetation mapping Time series analysis Unsupervised learning Change detection algorithms Deep learning

来源：评论

学校读者我要写书评

暂无评论

Design and Implementation of Automatic Interpretation System for remote sensing image 3rd

Design and Implementation of Automatic Interpretation System...

引用

3rd conference on signal and Information processing - Networking and Computers (ICSINC)

作者： Ni, Linna Jiang, Yu Beijing Inst Spacecraft Syst Engn Beijing Peoples R China

ISBN: (纸本)9789811075216;9789811075209

The high-precision remote sensors on satellite provide massive image data which brings new challenges to data processing and interpretation. The existing data processing systems are mostly semi-automatic which have the problems as low efficiency and low interpretation quality. This paper designs and implements a ground test system for processing and interpreting remote sensing images automatically and efficiently. The system introduces a two-step feature parameter correlation calculation algorithm to interpret the images. The authors realize the system based on the structure of universal server + FPGA which capabilities can achieve a processing rate of tens of gigabit per second.

关键词： remote sensing image image interpretation Feature parameter correlation calculation

来源：评论

学校读者我要写书评

暂无评论

PROGRESSIVE REFINEMENT LEARNING BASED ON FEATURE INTERACTIVE FUSION FOR SEMANTIC SEGMENTATION OF remote sensing LIMITED DATASET 30

PROGRESSIVE REFINEMENT LEARNING BASED ON FEATURE INTERACTIVE...

引用

30th IEEE International conference on image processing (ICIP)

作者： Lyu, Xinran Zhang, Libao Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China

ISBN: (纸本)9781728198354

Due to the labor cost and the accuracy of manual identification, it is very difficult to make a strong label dataset of remote sensing images with a large amount of data. Therefore, the limited remote sensing dataset has become a research hotspot in recent years. However, due to insufficient precision and the lack of label accuracy, these methods often have insufficient expression ability. In this paper, we proposed a semantic segmentation method for remote sensing images by progressive refinement learning. Firstly, we construct multiple classification networks to vote for label noise cleaning, and select a network to retrain. Then, the method based on hierarchical feature learning is used to realize the pixel-level pseudo label calculation. Secondly, we proposed to construct feature interactive fusion module in the multi-level codec to achieve image group semantic segmentation. Comprehensive evaluations and the comparison with 7 methods validate the superiority of the proposed model.

关键词： remote sensing limited dataset progressive refinement learning feature interactive fusion

来源：评论

学校读者我要写书评

暂无评论

Collabrative Sparse image Fusion With Application to Pan-Sharpening

Collabrative Sparse Image Fusion With Application to Pan-Sha...

引用

18th International conference on Digital signal processing (DSP)

作者： Zhu, Xiao Xiang Grohnfeldt, Claas Bamler, Richard German Aerosp Ctr DLR Remote Sensing Technol Inst IMF D-82234 Oberpfaffenhofen Wessling Germany

ISBN: (纸本)9781467358057

Recently sparse signal re presentation of image patches was explored to solve the pan-sharpening problem for remote sensing images. Although the proposed sparse reconstruction based methods lead to motivating results, yet none of them has considered the fact that the information contained in different multispectral channels may be mutually correlated. In this paper, we extend the Sparse Fusion of images (SparseFI, pronounced "sparsify") algorithm, proposed by the authors before, to a Jointly Sparse Fusion of images (J-SparseFI) algorithm by exploiting these possible signal structural correlations between different multispectral channels. This is done by making use of the distributed compressive sensing (DCS) theory that restricts the solution of an underdetermined system by considering an ensemble of signals being jointly sparse. The algorithm is validated with UltraCam data. In the final presentation, results with Hyspex data will be presented.

关键词： Pan-Sharpening SparseFI J-SparseFI Joint Sparsity

来源：评论

学校读者我要写书评

暂无评论

processing algorithms for quantum remote sensing image data 27

Processing algorithms for quantum remote sensing image data

引用

27th Annual conference on Infrared remote sensing and Instrumentation held as part of the Annual SPIE Optics and Photonics Symposium

作者： Bi, Siwen Chen, Hao Ke, Yuxian Rao, Siwei Liu, Jiaying Shenzhen Jingwan Quantum Remote Sensing Technol C Beijing 518108 Peoples R China Chinese Acad Sci Inst Remote Sensing & Digital Earth Beijing 100101 Peoples R China

ISBN: (数字)9781510629509

ISBN: (纸本)9781510629509

Since Professor Siwen Bi proposed quantum remote sensing (QRS) in early 2001, the first QRS imaging prototype was developed after many stages of researches. Based on the results, our group has also undertaken in-depth theoretical and algorithmic experiments on QRS image processing. It's both a quantum system simulation algorithm, preparing for the future quantum physical devices and calculation technology, and an expansion of quantum theories to RS image processing fields. It combines quantum mechanics theory and RS image processing technology, which introduces a new research direction for RS image processing technology. Now our researches achievements include a quantum denoising algorithm theory and simulation, a quantum enhancement algorithm theory and simulation, and a quantum segmentation algorithm theory research and simulation. A RS denoising algorithm based on the quantum-inspired concept is proposed for image denoising. Key benefits of the algorithm, which include improvements in transmission and accuracy, are demonstrated experimentally. Experiments showed that the peak signal to noise ratio (PSNR) for the proposed algorithm is improved by over 2dB and the edge retention index (EPI) is 0.1 higher than that for common methods. Given the low contrast ratio and brightness as well as insufficient detail for some RS images, a quantum algorithm based on the combination of a quantum inspired and unsharp masking to enhance and segment the RS image data was proposed. Results showed that the contrast ratio and brightness of images processed by the quantum algorithm improved, the image entropy and peak signal to noise ratio is higher.

关键词： Quantum remote sensing image data processing quantum-inspired quantum superposition double density dual-tree complex wavelet peak signal to noise ratio edge retention index

来源：评论

学校读者我要写书评

暂无评论

AFDN: ATTENTION-BASED FEEDBACK DEHAZING NETWORK FOR UAV remote sensing image HAZE REMOVAL

AFDN: ATTENTION-BASED FEEDBACK DEHAZING NETWORK FOR UAV REMO...

引用

IEEE International conference on image processing (ICIP)

作者： Wang, Shan Wu, Hanlin Zhang, Libao Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China

ISBN: (纸本)9781665441155

To efficiently remove haze in unmanned aerial vehicle (UAV) remote sensing images, a novel attention-based feedback dehazing network (AFDN) is proposed, which is constructed by feedback connections and attention-based feedback blocks (AFBs). It has three major advantages compared with other dehazing algorithms: 1) The feedback connections, which allow network to use previous state to improve current performance, can effectively help the proposed AFDN generate clear remote sensing scenes progressively. 2) The AFBs are specially designed to extract global residual features, in which the dual attention block can usefully reduce redundant information and improve the fitting ability of network. 3) To obtain abundant texture information from UAV remote sensing images and restore real ground surfaces, an energy loss is employed for texture features learning. Experiments on synthetic datasets and real UAV remote sensing images verify the superiority of AFDN over several state-of-the-art methods in terms of qualitative and quantitative analysis.

关键词： image enhancement remote sensing image dehazing feedback mechanism CNN

来源：评论

学校读者我要写书评

暂无评论

STRIPE NOISE REMOVAL OF remote sensing image WITH A DIRECTIONAL l₀ SPARSE MODEL 24

STRIPE NOISE REMOVAL OF REMOTE SENSING IMAGE WITH A DIRECTIO...

引用

24th IEEE International conference on image processing (ICIP)

作者： Dou, Hong-Xia Huang, Ting-Zhu Deng, Liang-Jian Chen, Yong Univ Elect Sci & Technol China Sch Math Sci Chengdu Sichuan Peoples R China

ISBN: (纸本)9781509021758

This paper commits to remove the stripe noise to enhance the visual quality of remote sensing images, in the meanwhile preserves image details of stripe-free regions. Instead of solving the underlying image as most of researches, we propose a non-convex l(0) model for remote sensing image destriping by taking full consideration of the intrinsically directional and structural priors of stripe noise. Moreover, the proposed non convex model can be solved by the proximal alternating direction method of multipliers (PADMM) method which theoretically guarantees converging to a KKT point. Extensively experimental results on simulated and real data demonstrate that the proposed method outperforms recent state-of-the-art destriping methods, both visually and quantitatively.

关键词： remote sensing Mathematical model Visualization Minimization Superluminescent diodes Convergence Closed-form solutions

来源：评论

学校读者我要写书评

暂无评论

TRANSFORMATION CONSISTENCY FOR remote sensing image SUPER-RESOLUTION 30

TRANSFORMATION CONSISTENCY FOR REMOTE SENSING IMAGE SUPER-RE...

引用

30th IEEE International conference on image processing (ICIP)

作者： Deng, Kai Yao, Ping Cheng, Siyuan Bi, Junyu Zhang, Kun Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China INRIA Saclay Ile De France Palaiseau France Inst Polytech Paris Paris France

ISBN: (纸本)9781728198354

Single image Super-Resolution (SISR) based on deep learning methods has been widely studied for applications on remote sensing images. With limited remote sensing images, most of the existing SISR methods simply adopt the regular data augmentation approaches (such as flip) in natural images to improve model performance. Considering the fact that remote sensing images are all taken from a bird's-eye view and objects appear in multiple directions, we first introduce rotation augmentation method in remote sensing images to promote diversity of samples dramatically, as rotation does not cause semantic problems like people standing upside down in natural images. However, image rotation at various angles implemented by interpolation will cause the inconsistent pixel distribution problem for the pixel level task. Thus, we propose Transformation Consistency Loss Function (TCLF) to narrow the gap between the augmented and original distribution, while expanding the feature space with rotation augmentation method. Extensive experiments are performed on UC-Merced Land-use dataset of 21 remote sensing scenes, and the results as well as ablation studies demonstrate our proposed method outperforms mainstream methods.

关键词： image processing Super-resolution Transformation consistency

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：