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optical algorithm for Cloud Shadow Detection Over Water

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2013年第2期51卷 732-741页

作者： Amin, Ruhul Gould, Richard Hou, Weilin Arnone, Robert Lee, Zhongping USN Res Lab Stennis Space Ctr Stennis Space Ctr MS 39529 USA Univ Massachusetts Dept Environm Earth & Ocean Sci Boston MA 02125 USA

The application of ocean color product retrieval algorithms for pixels containing cloud shadows leads to erroneous results. Thus, shadows are an important scene type that should be identified and excluded from the set of clear-sky pixels. In this paper, we present an optical cloud shadow-detection technique called the Cloud Shadow Detection Index (CSDI). This approach is for homogeneous water bodies such as deep waters where shadow detection is very challenging due to the relatively small differences in the brightness values of the shadows and neighboring sunlit or some other regions. The CSDI technique is developed based on the small differences between the total radiances reaching the sensor from the shadowed and neighboring sunlit regions of similar optical properties by amplifying the differences through integrating the spectra of the two regions. The Integrated Value (IV) is then normalized by the mean of the IVs within a spatial adaptive sliding box where atmospheric and marine optical properties are assumed homogeneous. Assuming that the true color and the IV images represent accurate shadow locations, the results were visually compared. The CSDI images agree reasonably well with the corresponding true color and the IV images over open ocean. Also, the shape of the cloud shadow particularly for the isolated cloud closely follows that of the cloud, as expected, reconfirming the potential of the CSDI technique.

关键词： Hyperspectral imagery ocean color optical algorithm remote sensing shadow detection

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Face Tracking Based on Fusion Skin Color Model and optical Flow algorithm

Face Tracking Based on Fusion Skin Color Model and Optical F...

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International Conference on Wireless Networks and Information Systems

作者： Zhao, Xiaogang Hui, Yanbo Henan Univ Technol Coll Informat Sci & Engn Zhengzhou 450001 Peoples R China

ISBN: (纸本)9780769539010

A fusion algorithm based on skin color model and optical flow is proposed to detect and track faces. It uses skin color model to detect faces and adopts optical flow algorithm to estimate the continuity of the video frames and obtain the position of faces in the frames. Taking advantages of the algorithms, the proposed method, in some degrees, gives a solution to the effects of face rotation, partial occlusion, expression variation and illumination on face detecting and face tracking.

关键词： skin color model optical algorithm face detection fusion algorithm

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Automated Detection and Removal of Cloud Shadows on HICO Images

Automated Detection and Removal of Cloud Shadows on HICO Ima...

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Conference on the Ocean Sensing and Monitoring III

作者： Amin, Ruhul Gould, Richard Hou, Weilin Lee, Zhongping Arnone, Robert US Navy Naval Res Lab Code 7333 Stennis Space Ctr MS 39529 USA Mississippi State Univ GRI Stennis Space Ctr MS 39529 USA

ISBN: (纸本)9780819486042

Clouds cause a serious problem for optical satellite sensors. Clouds not only conceal the ground, they also cast shadows, which cause either a reduction or total loss of information in an image, by reducing the illumination falling on the shadowed pixels. Ocean color bio-optical inversion algorithms rely on measurements of remote sensing reflectance (R-rs(lambda)) at each pixel. If shadows are not removed properly across a scene, erroneous R-rs(lambda) values will be calculated for the shadowed pixels, leading to incorrect retrievals of ocean color products such as chlorophyll. The cloud shadow issue becomes significant especially for high-resolution sensors such as the Hyperspectral Imager for the Coastal Ocean (HICO). On the other hand, the contrast of pixels in and outside a shadow provides opportunities to remove atmospheric contributions for ocean color remote sensing. Although identifying cloud is relatively straightforward using simple brightness thresholds, identifying their shadows especially over water is quite challenging because the brightness of the shadows is very close to the brightness of neighboring sunny regions especially in deep waters. In this study, we present automated procedures for our recently proposed cloud shadow detection technique called the Cloud Shadow algorithm (CSA) and Lee et al. (2007) cloud and shadow atmospheric correction algorithm. We apply both automated procedures to HICO imagery and show examples of the results.

关键词： atmospheric correction HICO cloud shadow optical algorithm automated ocean color remote sensing

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optical computing

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APPLIED MATHEMATICS AND COMPUTATION 2009年第4期215卷 1417-1430页

作者： Woods, Damien Naughton, Thomas J. Univ Seville Dept Comp Sci & Artificial Intelligence Seville Spain Natl Univ Ireland Dept Comp Sci Maynooth Kildare Ireland Univ Oulu RFMedia Lab Oulu So Inst Ylivieska 84100 Finland

In this survey we consider optical computers that encode data using images and compute by transforming such images. We give an overview of a number of such optical computing architectures, including descriptions of the type of hardware commonly used in optical computing, as well as some of the computational efficiencies of optical devices. We go on to discuss optical computing from the point of view of computational complexity theory, with the aim of putting some old, and some very recent, results in context. Finally, we focus on a particular optical model of computation called the continuous space machine. We describe some results for this model including characterisations in terms of well-known complexity classes. (C) 2009 Elsevier Inc. All rights reserved.

关键词： optical computing optical algorithm optical implementation Continuous space machine Computational complexity Fourier transform Search algorithm

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optical computing

Optical computing

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International Workshop on Physics and Computation

关键词： optical computing optical algorithm optical implementation Continuous space machine Computational complexity Fourier transform Search algorithm

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