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

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Performance investigation of selected NoSQL databases for massive remote sensing image data storage 4

Performance investigation of selected NoSQL databases for ma...

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4th International conference on Advanced Technologies for signal and image processing (ATSIP)

作者： Hajjaji, Yosra Farah, Imed Riadh Manouba Univ Riadi Lab Natl Sch Comp Sci Manouba Tunisia Manouba Univ Telecom Bretagne Natl Sch Comp Sci Manouba Tunisia

ISBN: (纸本)9781538652398

today's sensors are like eyes in the sky, thanks to the growth of satellite remote sensing technologies. Therefore, we see a steady evolution of the usage of different types of sensor, from airborne and satellites platforms which are generating large quantities of remote sensing image for divers applications such as;smart city, disaster management, military intelligence and others. As a result, the rate of growth in the amount of data by satellite is increasing dramatically. The velocity has exceeded 1TB per day and it will certainly increase in the future. However, it becomes crucial for these huge volume data to be stored. So, how to store and manage it efficiently becomes a real challenge because traditional ways have intensive issues;they are expensive and difficult to extend. Therefore, we need some scalable and parallel models for remote sensing data storage and processing. In this paper, we describe a scalable and distributed architecture for massive remote sensing data storage based on three No SQL databases (Apache Cassandra, Apache HBase, MongoBD). Also, a Hadoop-based framework is proposed to manage the big remote sensing data in a distributed and parallel manner.

关键词： Big data remote sensing data NoSQL databases Cassandra HBase MongoDB cluster Hadoop/ MapReduce

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A Plane Target Detection Algorithm in remote sensing images based on Deep Learning Network Technology 2

A Plane Target Detection Algorithm in Remote Sensing Images ...

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2nd International conference on Communication, image and signal processing (CCISP)

作者： Li Shuxin Zhang Zhilong Li Biao Natl Univ Def Technol ATR Natl Key Lab Changsha Hunan Peoples R China

Plane is an important target category in remote sensing targets and it is of great value to detect the plane targets automatically. As remote imaging technology developing continuously, the resolution of the remote sensing image has been very high and we can get more detailed information for detecting the remote sensing targets automatically. Deep learning network technology is the most advanced technology in image target detection and recognition, which provided great performance improvement in the field of target detection and recognition in the everyday scenes. We combined the technology with the application in the remote sensing target detection and proposed an algorithm with end to end deep network, which can learn from the remote sensing images to detect the targets in the new images automatically and robustly. Our experiments shows that the algorithm can capture the feature information of the plane target and has better performance in target detection with the old methods.

关键词： remote sensing

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Survey of transform methods in remote sensing information processing

Survey of transform methods in remote sensing information pr...

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conference on image and signal processing for remote sensing VIII

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

ISBN: (纸本)081944667X

One unique feature in the remote sensing problems is that a significant amount of data are available, from which desired information must be extracted. Transform methods offer effective procedures to derive the most significant information for further processing or human interpretation and to extract important features for pattern classificaiton. In this paper a survey of the use of major transforms in remote sensing is presented. These transforms have significant effects on pattern recognition as features derived from orthogonal or related transforms tend to be very effective for classification, and on data reduction and compression. After the introduction, we will examine the empirical orthogonal function, the discrete Karhunen-Loeve transform and related transforms, the wavelet transform, and the component analysis.

关键词： remote sensing Independent component analysis Principal component analysis Wavelets Wavelet transforms Data processing image enhancement image classification Feature extraction Statistical analysis

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A Self-adaptive image Registration Method: From Local Learning to Overall processing

A Self-adaptive Image Registration Method: From Local Learni...

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conference on image and signal processing for remote sensing XIX

作者： Ye, Peng Li, Na Liu, Fang Wu, Juhong Guo, Guirong Natl Univ Def Technol Sch Elect Sci & Engn 47 Yanwachi St Changsha Hunan Peoples R China

ISBN: (纸本)9780819497611

image registration is a fundamental and crucial step in remote sensing image analysis. However, it is known that image registration method is application-based. The type and content of remote sensing images affect the choice of image registration methods. Previous image registration task took experts to manually choose the image registration elements. This paper presents a self-adaptive image registration method which could automatically choose registration elements which are more appropriate for remote sensing images under processing. The proposed method first chooses several local regions for the representation of the whole image, and then different registration elements are tested on these local regions. The local registration results are evaluated and the registration of the whole image is done with learned registration elements from local registrations. The registration chain is automatic;therefore it is a self-adaptive registration method. The proposed method is demonstrated on several real remote sensing image pairs, and its feasibility and superiority are proved by the results.

关键词： image registration local registration local learning overall processing remote sensing

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GAN-NL: UNSUPERVISED REPRESENTATION LEARNING FOR remote sensing image CLASSIFICATION

GAN-NL: UNSUPERVISED REPRESENTATION LEARNING FOR REMOTE SENS...

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IEEE Global conference on signal and Information processing (GlobalSIP)

作者： Duan, Yiping Tao, Xiaoming Xu, Mai Han, Chaoyi Lu, Jianhua Tsinghua Univ Dept Elect Engn Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China Beihang Univ Dept Elect & Informat Engn Beijing 100191 Peoples R China

ISBN: (纸本)9781728112954

Recently, deep learning methods have greatly enhanced the classification performance because of their strong representation ability in the local receptive field. However, the non-local spatial information always exist in the images. Moreover, the limited amount of the labeled data imposes great challenges on the supervised representation learning model, especially the remote sensing images. With the consideration, we propose a generative adversarial network with non-local spatial information (GAN-NL) for remote sensing image classification. Specifically, a non-local layer is incorporated into a generative adversarial network for unsupervised representation learning. Then, a classification network is designed to infer the labels of the images. The classification results on the challenging NWPU-RESISC45 remote sensing image dataset show that our proposed method performs favorably against the state-of-the-art methods in terms of the classification accuracy without any pre-training.

关键词： Non-local spatial information generative adversarial networks (GANs) unsupervised representation learning remote sensing image classification

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A Multi-Source remote sensing image Matching Approach Based on Texture-Enhanced Region Features

A Multi-Source Remote Sensing Image Matching Approach Based ...

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9th International conference on signal and image processing (ICSIP)

作者： Zhao Zilu Wang Feng You Hongjian Li Peifeng Zhang Tingtao Chinese Acad Sci Aerosp Informat Res Inst Beijing Peoples R China Key Lab Technol Geospatial Informat Proc & Applic Beijing Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing Peoples R China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350920

Multi-source remote sensing image matching is crucial for remote sensing technology applications. However, the variations in factors such as grayscale, perspective, and sensors between multi-source images present certain challenges for image matching. In response to the challenges in matching multi-source remote sensing images, a matching method based on texture-enhanced region features is proposed. Initially, Gabor filters and the gray-level co-occurrence matrix (GLCM) are used to obtain the texture energy maps, followed by the extraction of maximally stable extremal regions (MSER) on the texture energy maps to acquire region features. Subsequently, the contour descriptors of the features are computed using Fourier descriptors. Finally, feature matching and refinement of the matching results are conducted in conjunction with the fast sample consensus (FSC). We conducted experimental region feature matching on multiple pairs of multi-source remote sensing images, and the results validate the effectiveness of our method.

关键词： feature region matching remote image match maximally stable extremal regions (MSER) Fourier descriptors

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RESOLUTION ASSESSMENT IN DYNAMIC image FORMATION

RESOLUTION ASSESSMENT IN DYNAMIC IMAGE FORMATION

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18th IEEE International conference on image processing (ICIP)

作者： Butala, Mark D. CALTECH Jet Prop Lab Natl Aeronaut & Space Adm Pasadena CA 91125 USA

ISBN: (纸本)9781457713033

remote sensing and astronomical image formation is often complicated by deficiencies in measurement quality, density, or diversity. Penalized likelihood methods can incorporate additional first-principles physical prior knowledge and improve the image reconstructions, but a systematic bias is unavoidable as a consequence. This work derives theory to understand the bias and develops a computational tool to probe its effect on the reconstructed image and bound resolution limits. Though the focus is on image formation, the contributions of this paper apply to any inference problem that can be expressed under the linear state-space signal model.

关键词： remote sensing multidimensional signal processing recursive estimation Kalman filter

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Study on the integration of GIS and remote sensing data in grouping-interpretation system for remote sensing image

Study on the integration of GIS and remote sensing data in g...

引用

conference on image and signal processing for remote sensing X

作者： Liu, YL Yan, SY Chinese Acad Sci GIS Div Inst Remote Sensing Applicat Beijing 100101 Peoples R China

ISBN: (纸本)0819455202

In this paper, we present a new approach to integrate Geographic information System and remote sensing. Its implementation environment is in Grouping Interpretation System (GrIS). GrIS was developed based on application task requirements, visual interpreting procedure and manner, and multi-technique integration. GrIS can operate in both single-computer mode and multi-coniputer mode with client/server structure in LAN and WAN environment. This system was designed to function within an integrated Geographic information System remote sensing processing and image interpretation function. Moreover, it allows the incorporation of raster format with vector format for image interpretation, automatic and semi-automatic interpretation mode respectively. The integration result of image interpretation into grouping interpretation system (GrIS) is demonstrated. The use of this integration technology and the relevant information from GIS leads to an enhanced information extraction and effective analysis in remote sensing images.

关键词： remote sensing image grouping interpretation GIS integration information extraction

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Cramer-Rao lower bounds for support-constrained and pixel-based multi-frame blind deconvolution - art. no. 636503

Cramer-Rao lower bounds for support-constrained and pixel-ba...

引用

conference on image and signal processing for remote sensing xii

作者： Matson, Charles L. Haji, Alim USAF Res Lab Opt Div DES Kirtland AFB NM 87117 USA

ISBN: (纸本)0819464600

Multi-frame blind deconvolution (MFBD) algorithms can be used to reconstruct a single high-resolution image of an object from one or more measurement frames of that are blurred and noisy realizations of that object. The blind nature of MFBD algorithms permits the reconstruction process to proceed without having separate measurements or knowledge of the blurring functions in each of the measurement frames. This is accomplished by estimating the object common to all the measurement frames jointly with the blurring functions that are different from frame to frame. An issue of key importance is understanding how accurately the object pixel intensities can be estimated with the use of MFBD algorithms. Here we present algorithm-independent lower bounds to the variances of estimates of the object pixel intensities to quantify the accuracy of these estimates when the blurring functions are estimated pixel by pixel. We employ support constraints on both the object and the blurring functions to aid in making the inverse problem unique. The lower bounds are presented as a function of the sizes and shapes of these support regions and the number of measurement frames.

关键词： support blind deconvolution multi-frame blind deconvolution Cramer-Rao lower bounds image reconstruction noise reduction

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image based Land Cover Classification for remote sensing Applications-A review 3

Image based Land Cover Classification for Remote Sensing App...

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3rd International conference on signal processing and Communication (ICPSC)

作者： Manikandan, Mishaa M. Andrushia, A. Diana Neebha, T. Mary Karunya Inst Technol & Sci Dept Elect & Commun Engn Coimbatore Tamil Nadu India

ISBN: (纸本)9781665428644

Land cover is a material present in the earth surface. It consists of forest, farming land, fertile, infertile lands, desert, water bodies and agricultural areas etc., The basic need for capturing the information of the land cover is to classify the land resources according to their soil, water, climatic condition and to utilize it in the proper manner. Land use gives the knowledge about the uses and the benefits of the land used for man like mining, industrial and cultivation land. The aim of this article is to perform a detailed review of image-based land cover classification with respect to remote sensing applications.

关键词： land cover classification remote sensing image processing urban development

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