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

共 3572 条记录，以下是71-80 订阅

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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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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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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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Super-resolution techniques for Sentinel-5P products 29

Super-resolution techniques for Sentinel-5P products

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conference on image and signal processing for remote sensing xxix

作者： Carbone, Alessia Restaino, Rocco Vivone, Gemine Univ Salerno Dept Informat Engn Elect Engn & Appl Math I-84084 Fisciano SA Italy Inst Methodol Environm Anal CNR IMAA I-85050 Tito PZ Italy Natl Biodivers Future Ctr NBFC I-90133 Palermo PA Italy

ISBN: (纸本)9781510666955;9781510666962

Air pollution is considered a very critical environmental risk to human health. The World Health Organization reports that it is responsible for almost 7 million deaths. As so, motivation is enough to decrease population exposure. However, several unsolved issues that require additional research remain. In particular, despite global monitoring development, coverage is insufficient to accurately describe the spatial variability for specific pollutants within different areas. The TROPOshperic Monitoring Instrument mounted on Sentinel-5P is one of the satellite instruments that retrieve atmospheric pollutants' concentration with a comparatively high spatial resolution, around 5 km. However, the spatial detail of the available products is often unsuitable for the purpose at hand. Also, physical constraints prevent enhancing the sensor's nominal spatial resolution further. So, there is no alternative way to collect high-resolution information than through processing algorithms. In this research, we investigated the problem of super-resolving Sentinel-5P products by employing traditional and deep learning-based approaches. While the former do not require a training phase because they rely on simple physical models, the latter can attain higher performance by reproducing highly complicated models. However, the lack of high-resolution reference data makes the needed training phase of network parameters extremely challenging. In this paper, we studied different approaches tailored to the imagery at hand and evaluated their accuracy with Sentinel-5P data. This study provides insights into the techniques and how they should be employed to monitor air quality accurately. The results of this work give significant information for the development of suitable super-resolution algorithms.

关键词： remote sensing image processing super-resolution Sentinel-5P atmospheric pollution

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

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

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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...

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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

引用

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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Spatial information extraction of oil well sites based on medium-resolution satellite imagery 29

Spatial information extraction of oil well sites based on me...

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conference on image and signal processing for remote sensing xxix

作者： Wu, Hao Dong, Hongli Wang, Zhibao Bai, Lu Huo, Fengcai Tao, Jinhua Chen, Liangfu Northeast Petr Univ Artificial Intelligence Energy Res Inst Daqing Peoples R China Northeast Petr Univ Heilongjiang Prov Key Lab Networking & Intellige Daqing Peoples R China Northeast Petr Univ Sch Comp & Informat Technol Daqing Peoples R China Northeast Petr Univ Bohai Rim Energy Res Inst Qinhuangdao Hebei Peoples R China Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast Antrim North Ireland Chinese Acad Sci Aerosp Informat Res Inst State Key Lab Remote Sensing Sci Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781510666955;9781510666962

The oil extraction process has cumulative detrimental impacts on the environment. However, in the process of oil mining, a large number of petroleum-based pollutants cause severe effect to soil and groundwater, which poses a serious risk to the ecological environment and human health. Understanding the distribution of oil well sites, is of vital importance to sustainable mining development. Efficient mapping these sites require automated identification and extraction of the oil well sites from satellite images. With the development of remote sensing satellite technology and the wide application of deep learning-based algorithms, it has become possible to automatically extract oil well sites from remote sensing images. However, there is lack of usage of Sentinel-2 satellite data to explore the efficacy in oil well sites detection. Therefore, we conducted this work to explore the feasibility of detecting the oil well sites with semantic segmentation from Sentinel-2 imagery. In this work, we established the Northeast Petroleum University Oil Well Sites Version 2.0 (NEPU-OWS V2.0) with spatial coverage spanning the Austin region of United States. We then validate the usability and effectiveness of the dataset using semantic segmentation models based on DANet and Swin-Unet, which are more capable of recognising small targets. Our experimental results show that both models have great potential for remote sensing detection in the medium-sized oil well sites and the Swin-Unet model achieved a better performance for the detection of oil well sites with a MIoU of 77.53%.

关键词： Oil well sites Sentinel 2 semantic segmentation DANet Swin-Unet

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remote sensing image Scene Classification Based on SURF Feature and Deep Learning

Remote Sensing Image Scene Classification Based on SURF Feat...

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Annual Summit and conference of the Asia-Pacific-signal-and-Information-processing-Association (APSIPA ASC)

作者： Liang, Jinxiang Dang, Jianwu Wang, Yangping Yang, Jingyu Zhang, Zhenhai Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou Peoples R China Lanzhou Bocai Technol Co Ltd Gansu Prov Engn Res Ctr Artificial Intelligence & Gansu Prov Key Lab Syst Dynam & Reliabil Rail Tra Lanzhou Peoples R China

ISBN: (纸本)9781728132488

remote sensing image scene classification is one of the key points in remote sensing image interpretation. The traditional remote sensing image scene classification feature performance is not strong, and the deep learning extraction semantic feature process is complex. This paper proposes a fusion feature remote sensing image scene classification method which is based on artificial features and deep learning semantic features. Firstly, the SURF feature of the remote sensing image is extracted and encoded by the VLAD algorithm. The semantic feature of a remote sensing image is extracted by transfer learning. Then the feature reduction is performed by PCA algorithm and feature fusion is performed. Finally, the scene classifier is trained by using the random forest algorithm. The experimental results show that the classification accuracy and Kappa coefficient of this method are higher and the method is effective.

关键词： Decision trees

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