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共 3607 条记录，以下是71-80 订阅

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Can multiresolution fusion techniques improve classification accuracy?

Can multiresolution fusion techniques improve classification...

引用

conference on image and signal processing for remote sensing xii

作者： Bruzzone, L. Carlin, L. Alparone, L. Baronti, S. Garzelli, A. Nencini, F. Univ Trent Dept Informat & Commun Technol I-38050 Trento Italy

ISBN: (纸本)0819464600

In this paper we propose an analysis of the effects of the multiresolution fusion process on the accuracy provided by supervised classification algorithms. In greater detail, the rationale of this analysis consists in understanding in what conditions the merging process can increase/decrease the classification accuracy of different labeling algorithms. On the one hand, it is expected that the multiresolution fusion process can increase the classification accuracy of simple classifiers, characterized by linear or "moderately" non-linear discriminant functions. On the other hand, the spatial and spectral artifacts unavoidably included in the fused images can decrease the accuracy of more powerful classifiers, characterized by strongly non-linear discriminant functions. In this last case, in fact, the classifier is intrinsically able to extract and emphasize all the information present in the original images without any need of a merging procedure. These effects may be different by considering different fusion methodologies and different classification techniques. Several experiments are carried out by applying the different fusion and classification techniques to an image acquired by the Quickbird sensor on the city of Pavia (Italy). From these experiments it is possible to derive interesting conclusions on the effectiveness and the appropriateness of the different investigated multiresolution fusion techniques with respect to classifiers having different complexity and capacity.

关键词： very high geometrical resolution images multiresolution fusion automatic classification remote sensing

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Aircraft Segmentation from remote sensing image by Transferring Natual image Trained Forground Extraction CNN Model 4

Aircraft Segmentation from Remote Sensing Image by Transferr...

引用

4th IEEE International conference on signal and image processing (ICSIP)

作者： Zeng, Yanqing Niu, Xin Dou, Yong Natl Univ Def Technol Dept Comp Changsha Hunan Peoples R China

ISBN: (纸本)9781728136608

With the rapid development of remote sensing technology, remote sensing image processing has been widely used in the field of national defense and the people's livelihood. However, due to the high cost of manually collecting and annotating data, the number of currently public remote sensing datasets are limited, and the datasets of remote sensing images for foreground segmentation and detection is also confined. In this paper, because of the difficulty of small-sized datasets, the foreground segmentation of aircraft remote sensing image is realized by using the idea of transfer learning. We implemented three kinds of different aircraft segmentation networks for small-sized aircraft remote sensing images by transferring three kinds of different foreground segmentation networks trained on large-scaled datasets. The best aircraft segmentation accuracy is up to 79.201% under custom evaluation criteria. Experiment results showed that transferring a foreground extraction CNN model trained on public datasets to cope with the task of aircraft segmentation on small aircraft remote sensing images is significantly effective.

关键词： Foreground segmentation remote sensing images Transfer learning Deep learning

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Transform methods for remote sensing environmental monitoring

Transform methods for remote sensing environmental monitorin...

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33rd IEEE International conference on Acoustics, Speech and signal processing

作者： Chen, Chi Hau Univ Massachusetts Dept Elect & Comp Engn N Dartmouth MA 02747 USA

ISBN: (纸本)9781424414833

Transform methods in signal and image processing generally speaking are easy to use and can play a number of useful roles in remote sensing environmental monitoring. Examples are the pollution and forest fire monitoring. Transform methods offer effective procedures to derive the most important information for further processing or human interpretation and to extract important features for pattern classification. Most transform methods are used for image (or signal) enhancement and compression. However other transform methods are available for linear or nonlinear discrimination in the classification problems. In this paper we will examine the major transform methods which are useful for remote sensing especially for environmental monitoring problems. Many challenges to signal processing will be reviewed. Computer results are shown to illustrate some of the methods discussed.

关键词： transform methods environmental monitoring SAR image noise component analysis contextual image models

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An Algorithm of remotely Sensed Hyperspectral image Fusion Based on Spectral Unmixing and Feature Reconstruction 12

An Algorithm of Remotely Sensed Hyperspectral Image Fusion B...

引用

conference on remotely Sensed Data Compression, Communications, and processing xii

作者： Sun, Xuejian Zhang, Lifu Cen, Yi Zhang, Mingyue Chinese Acad Sci Inst Remote Sensing & Digital Earth Beijing 100101 Peoples R China

ISBN: (纸本)9781510601154

In order to get high spatial resolution hyperspectral data, many studies have examined methods to combine spectral information contained in hyperspectral image with spatial information contained in multispectral/ panchromatic image. This paper developed a new hyperspectral image fusion method base on the non-negative matrix factorization (NMF) theory. Data sets obtained by the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) was used to evaluate the performance of the method. Experimental results show that the proposed algorithm can provide a good way to solve the problem of high spatial resolution hyperspectral data shortage.

关键词： hyperspectral remote sensing multispectral remote sensing data fusion spectral unmixing spatial resolution improvement

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Wavelets - a universal tool for the processing of remote sensing image data?

Wavelets - a universal tool for the processing of remote sen...

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conference on image processing, signal processing, and Synthetic Aperture Radar for remote sensing

作者： Schwarz, G Datcu, M DLR German Remote Sensing Data Ctr German Aerosp Res Estab DFD D-82234 Oberpfaffenhofen Germany

ISBN: (纸本)0819426490

During the last years, wavelets have become very popular in the fields of signal processing and pattern recognition and have led to a large number of publications. In the discipline of remote sensing several applications of wavelets have emerged, too. Among them are such diverse topics as image data compression, image enhancement, feature extraction, and detailed data analysis. On the other hand, the processing of remote sensing image data-both for optical and radar data-follows a well-known systematic sequence of correction and data management steps supplemented by dedicated image enhancement and data analysis activities. In the following we will demonstrate where wavelets and wavelet transformed data can be used advantageously within the standard processing chain usually applied to remote sensing image data. Summarizing potential wavelet applications for remote sensing image data, we conclude that wavelets offer a variety of new perspectives especially for image coding, analysis, classification, archiving, and enhancement. However, applications requiring geometrical corrections and separate dedicated representation bases will probably remain a stronghold of classical image domain processing techniques.

关键词： wavelets image processing remote sensing radiometry geometry enhancement data management

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Extraction of spatial and spectral scene statistics for hyperspectral scene simulation - art. no. 63650X

Extraction of spatial and spectral scene statistics for hype...

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conference on image and signal processing for remote sensing xii

作者： Kennett, Rosemary Sundberg, Robert L. Gruninger, John Haren, Raymond Spectral Sci Inc Burlington MA 01803 USA

ISBN: (纸本)0819464600

A method for extracting statistics from hyperspectral data and generating synthetic scenes suitable for scene generation models is presented. Regions composed of a general surface type with a small intrinsic variation, such as a forest or crop field, are selected. The spectra are decomposed using a basis set derived from spectra present in the scene and the abundances of the basis members in each pixel spectrum found. Statistics such as the abundance means, covariances and channel variances are extracted. The scenes are synthesized using a coloring transform with the abundance covariance matrix. The pixel-to-pixel spatial correlations are modeled by an autoregressive moving average texture generation technique. Synthetic reflectance cubes are constructed using the generated abundance maps, the basis set and the channel variances. Enhancements include removing any pattern from the scene and reducing the skewness. This technique is designed to work on atmospherically-compensated data in any spectral region, including the visible-shortwave infrared HYDICE and AVIRIS data presented here. Methods to evaluate the performance of this approach for generating scene textures include comparing the statistics of the synthetic surfaces and the original data, using a signal-to-clutter ratio metric, and inserting sub-pixel spectral signatures into scenes for detection using spectral matched filters.

关键词： scene statistics remote sensing hyperspectral multispectral scene simulation

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Zero-watermarking for Copyright Protection of remote sensing image

Zero-watermarking for Copyright Protection of Remote Sensing...

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9th International conference on signal processing

作者： Jing, Li Zhang, Yadong Chen, Guangyu Henan Univ Finance & Econ Coll Informat Zhengzhou 450002 Peoples R China

ISBN: (纸本)9781424421787

Watermarking technique is the main method for copyright protection. But this technique does not suit remote sensing image because the embedded watermark heavily affects the results of its applicative tasks. In order to protect remote sensing image, this paper proposes a robust zero-watermarking method that constructs watermarks from the host data instead of embedding watermark into that data. The proposed method constructs two watermarks from host image. One is constructed from low-frequency coefficients in discrete wavelet transform domain of the host image, and the other is constructed from that of the log-polar mapping image of the host image. Experiments show that this method is robust to both signal processes and geometrical distortions.

关键词： Discrete wavelet transforms

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remote-sensing image fusion based on multiwavelet transform and IHS transform

Remote-sensing image fusion based on multiwavelet transform ...

引用

8th International conference on signal processing

作者： Wang, Tianpeng Li, Bicheng Yu, Chaoqun Cao, Wen Zhengzhou Informat Sci & Technol Inst Zhengzhou 450002 Peoples R China Inst Surveying & Mapping Zhengzhou 450002 Peoples R China

ISBN: (纸本)9780780397361

Multiwavelet transform is a new development to the wavelet theory. It can offer orthogonality, symmetry, and short support simultaneously, and can offer a more precise way for image analysis than wavelet multi-resolution. A now remote-sensing image fusion method based on intensity-hue-saturation transform(IHST) and Mulliwavelet Transform(MWT) is presented to fusion a multi-spectral image and a panchromatic image. Firstly, the multi-spectral image is transformed with IHST;Secondly, the panchromatic image and I component of the multi-spectral image are merged with MWT-based fusion method, and I component is replaced with the merged data;finally, the fused image is obtained by inverse IHST. Comparing with other methods, the new method can enhance spatial detail information of remote-sensing images largely and provide more spatial information at the same time.

关键词： remote-sensing image fusion IHS transform multiwavelet transformation

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remote sensing images Classification Using Moment Features and Attribute Profiles 5

Remote Sensing Images Classification Using Moment Features a...

引用

IEEE International conference on signal and image processing Applications (ICSIPA)

作者： Roochi, Niloofar Ghasemi Ghassemian, Hassan Mirzapour, Fardin Tarbiat Modares Univ Fac Elect & Comp Engn Image Proc & Informat Anal Lab Tehran Iran

ISBN: (纸本)9781509055593

remote sensing is the acquisition of information about an object or phenomenon without making physical contact with the object. remote sensing is used in numerous fields, including geography, land surveying and most Earth Science disciplines. In supervised classification, all of the feature extraction methods try to increase the accuracy of classification and simultaneously time of computation. At the present work, we use the moments and Attribute Morphology Profiles (APs) to extract texture information from satellite panchromatic images. We use four conventional moments in pattern recognition such as Geometric, Chebyshev, Legendre and Zernike moments and APs to extract features from remote sensing image. An MP is constructed based on the repeated use of openings and closings by reconstruction of a structuring elements (SE) of an increasing size, applied to a scalar image. Then, we use those two set of features together. The well-known support vector machine (SVM) is used for supervised classification. We compare our proposed method with moments and APs. Different criteria such as average accuracy, overall accuracy, kappa statistic and computation time are used for assessment of classification performance.

关键词： Moment Chebyshev Geometric Legendre Zernike Attribute Morphology Profile classification support vector machine (SVM) remote sensing

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image Resolution Enhancement for remote sensing Applications 2018

Image Resolution Enhancement for Remote Sensing Applications

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2nd International conference on Vision, image and signal processing (ICVISP)

作者： Kwan, Chiman Signal Proc Inc Rockville MD 20850 USA

ISBN: (纸本)9781450365291

We present a brief overview of recent image resolution enhancement algorithms with emphasis on remote sensing applications. Because resolution may have different meanings, we emphasize that our focus in this paper is on spatial, spectral, and temporal resolution enhancement algorithms. We will discuss and review recent and representative algorithms in enhancing spatial resolution, spectral resolution, spatial-spectral resolution, and spatio-temporal resolution of remote sensing images. Several interesting applications related to the fusion of Landsat and MODIS images, the fusion of color and hyperspectral images, and the fusion of Mars rover images will be presented. Finally, some future directions in this research area will be highlighted.

关键词： image Resolution Enhancement remote sensing Spectral Resolution Spatial Resolution

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