一级黄色片免费播放|中国黄色视频播放片|日本三级a|可以直接考播黄片影视免费一级毛片

高級搜索

留言板

尊敬的讀者、作者、審稿人, 關(guān)于本刊的投稿、審稿、編輯和出版的任何問題, 您可以本頁添加留言。我們將盡快給您答復。謝謝您的支持!

姓名
郵箱
手機號碼
標題
留言內(nèi)容
驗證碼

基于半正定約束的極化相似度最優(yōu)模型匹配目標分解

范慶輝,  盧紅喜,  保錚,  肖春寶

范慶輝, 盧紅喜, 保錚, 肖春寶. 基于半正定約束的極化相似度最優(yōu)模型匹配目標分解[J]. 電子與信息學報, 2015, 37(8): 1821-1827. doi: 10.11999/JEIT141468
引用本文: 范慶輝, 盧紅喜, 保錚, 肖春寶. 基于半正定約束的極化相似度最優(yōu)模型匹配目標分解[J]. 電子與信息學報, 2015, 37(8): 1821-1827. doi: 10.11999/JEIT141468
Fan Qing-hui, Lu Hong-xi, Bao Zheng, Xiao Chun-bao. Positive-semidefinite Based Target Decomposition Using Optimal Model-matching with Polarization Similarity[J]. Journal of Electronics & Information Technology, 2015, 37(8): 1821-1827. doi: 10.11999/JEIT141468
Citation: Fan Qing-hui, Lu Hong-xi, Bao Zheng, Xiao Chun-bao. Positive-semidefinite Based Target Decomposition Using Optimal Model-matching with Polarization Similarity[J]. Journal of Electronics & Information Technology, 2015, 37(8): 1821-1827. doi: 10.11999/JEIT141468

基于半正定約束的極化相似度最優(yōu)模型匹配目標分解

doi: 10.11999/JEIT141468
基金項目: 

國家自然科學基金(61271024, 61201292, 61201283)，新世紀優(yōu)秀人才支持計劃(NCET-09-0630)，全國優(yōu)秀博士學位論文作者專項資金(FANEDD-201156)，省部級基金和中央高校基本科研業(yè)務(wù)費

Positive-semidefinite Based Target Decomposition Using Optimal Model-matching with Polarization Similarity

  • 摘要: 目標分解是實現(xiàn)極化合成孔徑雷達目標分類、檢測與識別應(yīng)用的重要手段。傳統(tǒng)方法由于優(yōu)先對體散射分量進行提取，其體散射能量的高估或二面角散射能量的低估現(xiàn)象較為嚴重。該文通過引入極化相似度量，基于數(shù)據(jù)驅(qū)動自適應(yīng)地對基本散射機制的最優(yōu)匹配模型進行選擇。在此基礎(chǔ)上，根據(jù)極化相似度量確定基本散射機制散射能量提取的優(yōu)先順序，并以各階次剩余矩陣能量非負為約束，最終確定面散射、二面角散射、體散射這3種基本散射機制的能量貢獻值。實測數(shù)據(jù)處理結(jié)果及其與光學圖像的對比結(jié)果表明，該文方法獲取的極化目標分解結(jié)果優(yōu)于傳統(tǒng)方法，能夠準確地提取目標區(qū)域的基本散射特征。
    Abstract: Target decomposition is an important tool to realize target classification, detection and recognition applications with Polarimetric SAR (PolSAR). However, the traditional method with priority of volume scattering component extraction seriously performs overestimation in the volume scattering energy or underestimation in the dihedral scattering energy. In this paper, by introducing polarimetric similarity measure, data-driven model- matching for basic scattering mechanism is proposed. On this basis, the priority of scattering mechanisms energy extraction is determined with the similarity measure. Based on the non-negative constraint of energy, all the orders of residual matrix are reextracted for the final energy contribution of the dihedral scattering, volume scattering, and surface scattering mechanism. The processing results of real data and their comparison with the optical image results show that the proposal is better than traditional methods for the accurate extracttion of the basic scattering characteristics in the targets region.
    • HTML全文
  • Boerner W M, Yan W L, Xi A Q, et al.. Basic Concepts of Radar Polarimetry[M]. Netherlands: Springer, 1992: 155-245.
    Boerner W M. Basics of SAR Polarimetry I[R]. Chicago, IL: 2007.
    Mott H. Remote Sensing with Polarimetric Radar[M]. New York: Wiley-IEEE Press, 2007: 3-19.
    Cloude S R. Polarisation: Applications in Remote Sensing[M]. Oxford: Oxford University Press, 2009: 4-103.
    Lee J S and Pottier E. Polarimetric Radar Imaging From Basics to Applications[M]. United States: CRC Press, 2009: 5-53.
    Zebker H A and van Zyl J J. Imaging radar polarimetry: a review[J]. Proceedings of the IEEE, 1991, 79(11): 1583-1606.
    Chen Q, Kuang G Y, Li J, et al.. Unsupervised land cover/land use classification using PolSAR imagery based on scattering similarity[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(3): 1817-1825.
    Frery A C, Cintra R J, and Nascimento A. Entropy-based statistical analysis of PolSAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(6): 3733-3743.
    Kajimoto M and Susaki J. Urban-area extraction from polarimetric SAR images using polarization orientation angle[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(2): 337-341.
    Zhang P, Li M, Wu Y, et al.. Unsupervised multi-class segmentation of SAR images using fuzzy triplet Markov fields model[J]. Pattern Recognition, 2013, 46(4): 1-16.
    Ballester-Berman J D and Lopez-Sanchez J M. Applying the Freeman-Durden decomposition concept to polarimetric SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(1): 466-479.
    Freeman A and Durden S L. A three-component scattering model for polarimetric SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(3): 963-973.
    Yamaguchi Y, Sato A, Sato R, et al.. Four-component scattering power decomposition with rotation of coherency matrix[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(6): 2251-2258.
    Yamada H, Komaya R, Yamaguchi Y, et al.. Scattering component decomposition for POL-InSAR dataset and its applications[C]. Geoscience and Remote Sensing Symposium, Cape Town, 2009: V-154-V-157.
    Van Zyl J J, Arii M, and Kim Y. Model-based decomposition of polarimetric SAR covariance matrices constrained for nonnegative eigenvalues[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(9): 3452-3459.
    Cloude S R and Pottier E. A review of target decomposition theorems in radar polarimetry[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(2): 498-518.
    Singh G, Yamaguchi Y, and Park S E. General four- component scattering power decomposition with unitary transformation of coherency matrix[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(5): 3014-3022.
  • 加載中
計量
  • 文章訪問數(shù):  1316
  • HTML全文瀏覽量:  133
  • PDF下載量:  656
  • 被引次數(shù): 0
出版歷程
  • 收稿日期:  2014-11-24
  • 修回日期:  2015-04-24
  • 刊出日期:  2015-08-19

目錄

    /

    返回文章
    返回