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检索条件"任意字段=Conference on Image and Signal Processing for Remote Sensing XXIX"

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Estimation of blur and noise parameters in remote sensing

Estimation of blur and noise parameters in remote sensing

引用

IEEE International conference on Acoustics, Speech, and signal processing

作者： Jalobeanu, A Blanc-Féraud, L Zerubia, J INRIA Sophia Antipolis UNSA CNRS Joint Res GrpAriana F-06902 Sophia Antipolis France

ISBN: (纸本)0780374029

In this paper we propose a new algorithm to estimate the parameters of the noise related to the sensor and the impulse response of the optical system, from a blurred and noisy satellite or aerial image. The noise is supposed to be white, Gaussian and stationary. The blurring kernel has a parametric form and is modeled in such a way as to take into account the physics of the system (the atmosphere, the optics and the sensor). The observed scene is described by a fractal model, taking into account the scale invariance properties of natural images. The estimation is performed automatically by maximizing a marginalized likelihood, which is achieved by a deterministic algorithm whose complexity is limited to O(N), where N is the number of pixels.

关键词： remote sensing Gaussian noise computational complexity deterministic algorithms image processing optimisation white noise maximum likelihood estimation blur estimation noise parameters estimation remote sensing sensor impulse response optical system blurred satellite image noisy satellite image blurred aerial image noisy aerial image white noise Gaussian noise stationary. noise blurring kernel system physics atmosphere optics fractal model scale invariance properties natural images marginalized maximum likelihood estimation deterministic algorithm complexity optimization algorithm

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image registration and fusion studies for the integration of multiple remote sensing data

Image registration and fusion studies for the integration of...

引用

31st IEEE International conference on Acoustics, Speech and signal processing

作者： Le Moigne, Jacqueline Cole-Rhodes, Arlene Eastman, Roger Jain, Peyush Joshua, Aimee Memarsadeghi, Nargess Mount, David Netanyahu, Nathan Morisette, Jeffrey Uko-Ozoro, Ezinne NASA Goddard Space Flight Ctr Adv Architectures & Automat Branch Greenbelt MD 20771 USA

ISBN: (纸本)9781424404681

The future of remote sensing will see the development of spacecraft formations, and with this development will come a number of complex challenges such as maintaining precise relative position and specified attitudes. At the same time, there will be increasing needs to understand planetary system processes and build accurate prediction models. One essential technology to accomplish these goals is the integration of multiple source data. For this integration, image registration and fusion represent the first steps and need to be performed with very high accuracy. In this paper, we describe studies performed in both image registration and fusion, including a modular framework that was built to describe registration algorithms, a web-based image registration toolbox, and the comparison of several image fusion techniques using data from the EO-1/ALI and Hyperion sensors.

关键词： remote sensing

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UNSUPERVISED remote sensing HAZE REMOVAL BASED ON SALIENCY-GUIDED TRANSMISSION REFINEMENT 49

UNSUPERVISED REMOTE SENSING HAZE REMOVAL BASED ON SALIENCY-G...

引用

49th IEEE International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Zheng, Ruohui Zhang, Libao Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Haze causes information loss and quality degradation in remote sensing images. Unsupervised learning-based dehazing methods aim to reduce reliance on paired hazy images and their labels. However, complex mapping relationships often increase the difficulty in network convergence, resulting in color distortion and loss of texture details in remote sensing images. To address these issues, we propose an unsupervised haze removal method based on saliency-guided transmission refinement for remote sensing images. Firstly, we introduce a saliency-guided transmission refinement method, which decomposes and recombines two transmission maps obtained under different conditions, guided by saliency information. Secondly, we propose a loss function comprising energy loss and texture loss. The energy loss provides an energy reference based on the coarse transmission estimation, while the texture loss enhances the preservation of texture details. Experimental results demonstrate that our method achieves comparable performance to several supervised methods.

关键词： image restoration image enhancement remote sensing dehazing unsupervised learning

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Super resolution remote sensing image processing Algorithm Based on Wavelet Transform and Interpolation

Super resolution Remote Sensing Image Processing Algorithm B...

引用

conference on image processing and Pattern Recognition in remote sensing

作者： Tao, HJ Tang, XJ Liu, J Tian, JW Wuhan Univ Technol Wuhan 430063 Peoples R China

ISBN: (纸本)081944684X

According to characteristic of image wavelet transform and interpolation, this paper proposes an remote sensing image interpolation method combining wavelet transform and interpolation algorithm, which can improve the remote sensing image resolution. Experiments show that the algorithm can properly retain abundant high frequency information in original remote sensing image. After interpolation processing and wavelet reconstruction, we can obtain an remote sensing image with higher resolution, better visual effect, higher signal Noise Ratio (SNR), more detail information and no apparent warp. Therefore, this algorithm is an effective method of super-resolution remote sensing image processing.

关键词： wavelet transform interpolation remote sensing image processing super resolution

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MIXED PIXEL CLASSIFICATION IN remote sensing

MIXED PIXEL CLASSIFICATION IN REMOTE SENSING

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conference on image and signal processing for remote sensing

作者： BOSDOGIANNI, P PETROU, M KITTLER, J Univ. of Surrey Guildford UK

ISBN: (纸本)0819416452

In this paper we present a novel method for mixed pixel classification where the classification of groups of pixels is achieved taking into consideration the higher order moments of the distributions of the pure and the mixed classes. The method is demonstrated using simulated data and is also applied to real Landsat TM data for which ground data are available.

关键词： remote sensing

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Preliminary filtering and lossy compression of noisy remote sensing images 24

Preliminary filtering and lossy compression of noisy remote ...

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conference on image and signal processing for remote sensing XXIV

作者： Zemliachenko, Alexander N. Abramov, Sergey K. Lukin, Vladimir V. Vozel, Benoit Chehdi, Kacem Natl Aerosp Univ UA-61070 Kharkov Ukraine Univ Rennes 1 IETR UMR 6164 CNRS F-22305 Lannion France

ISBN: (纸本)9781510621626

Compression is a typical stage in processing of remote sensing images. Here we consider lossy compression of images that contain noise. Then, there are, at least, two approaches - to compress images without pre-filtering and with pre-filtering. The second approach is shown to have some advantages but only under certain conditions. Several metrics characterizing quality of compressed images are used. Main conclusions are in good agreement in cases of using different metrics. Recommendations concerning parameter selection for lossy compression with pre-filtering are given.

关键词： image compression Chromium remote sensing Visualization image quality image filtering Transform theory Denoising Optical inspection image processing

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Imaging mechanism based remote sensing image fusion with multi-wavelet transform

Imaging mechanism based remote sensing image fusion with mul...

引用

7th International conference on signal processing

作者： Na, Y Ehlers, M Yang, WH Li, YZ Xidian Univ Sch Elect Engn Xian 710071 Peoples R China

ISBN: (纸本)7121002159

This paper discusses the fusion of multispectral and panchromatic remote sensing images. A multi-wavelet transform based fusion method is presented after analyzing the basic principles of remote sensing imaging systems and the rational for image fusion. The simulation results show that the multi-wavelet transform based fusion method is superior to the ordinary wavelet transform based fusion method. The fused image provides more spatial and spectral information.

关键词： multi-wavelet transform multi-spectral image panchromatic image image fusion

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Evaluation of kernels for multiclass classification of hyperspectral remote sensing data

Evaluation of kernels for multiclass classification of hyper...

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31st IEEE International conference on Acoustics, Speech and signal processing

作者： Fauvel, Mathieu Chanussot, Jocelyn Benediktsson, Jon Atli Inst Natl Polytech Grenoble Lab Images & Signaux F-38402 St Martin Dheres France

ISBN: (纸本)9781424404681

Classification of hyperspectral remote sensing data with support vector machines (SVMs) is investigated. SVMs have been introduced recently in the field of remote sensing image processing. Using the kernel method, SVMs map the data into higher dimensional space to increase the separability and then fit an optimal hyperplane to separate the data. In this paper, two kernels have been considered. The generalization capability of SVMs as well as the ability of SVMs to deal with high dimensional feature spaces have been tested in the situation of very limited training set. SVMs have been tested on real hyperspectral data. The experimental results show that SVMs used with the two kernels are appropriate for remote sensing classification problems.

关键词： Data reduction

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Efficiency analysis for 3D filtering of multichannel images 22

Efficiency analysis for 3D filtering of multichannel images

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conference on image and signal processing for remote sensing XXII

作者： Kozhemiakin, Ruslan A. Rubel, Oleksii Abramov, Sergey K. Lukin, Vladimir V. Vozel, Benoit Chehdi, Kacem Natl Aerosp Univ UA-61070 Kharkov Ukraine Univ Rennes 1 IETR UMR CNRS 6164 F-22305 Lannion France

ISBN: (数字)9781510604131

ISBN: (纸本)9781510604124;9781510604131

Modern remote sensing systems basically acquire images that are multichannel (dual- or multi-polarization, multi-and hyperspectral) where noise, usually with different characteristics, is present in all components. If noise is intensive, it is desirable to remove (suppress) it before applying methods of image classification, interpreting, and information extraction. This can be done using one of two approaches - by component-wise or by vectorial (3D) filtering. The second approach has shown itself to have higher efficiency if there is essential correlation between multichannel image components as this often happens for multichannel remote sensing data of different origin. Within the class of 3D filtering techniques, there are many possibilities and variations. In this paper, we consider filtering based on discrete cosine transform (DCT) and pay attention to two aspects of processing. First, we study in detail what changes in DCT coefficient statistics take place for 3D denoising compared to component-wise processing. Second, we analyze how selection of component images united into 3D data array influences efficiency of filtering and can the observed tendencies be exploited in processing of images with rather large number of channels.

关键词： Denoising image filtering 3D image processing image processing Interference (communication) Statistical analysis Filtering (signal processing) remote sensing signal to noise ratio signal processing

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GEOMETRICAL FEATURES FOR THE CLASSIFICATION OF VERY HIGH RESOLUTION MULTISPECTRAL remote-sensing imageS

GEOMETRICAL FEATURES FOR THE CLASSIFICATION OF VERY HIGH RES...

引用

IEEE International conference on image processing

作者： Luo, Bin Chanussot, Jocelyn Grenoble Inst Technol GIPSA Lab Grenoble France

ISBN: (纸本)9781424479948

In order to extract geometrical features from a multispectral image and derive a classification, an approach based on the topographic map of the image is proposed. For each pixel, the most significant structure containing it is extracted. The classification of this pixel is based on its spectral information and the geometrical features of the corresponding structure (its area and perimeter). The results obtained on multispectral remote sensing images taken by two different sensors show the efficiency of the extracted geometrical features for separating some classes with very similar spectral attributes but of different semantic meanings.

关键词： computational geometry feature extraction geometrical feature extraction high resolution multispectral remote sensing image image classification image classification image resolution pixel spectral information topographic map

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