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多源遙感影像融合 版權信息
- ISBN：9787121485602
- 條形碼：9787121485602 ; 978-7-121-48560-2
- 裝幀：平塑勒
- 冊數：暫無
- 重量：暫無
- 所屬分類：>
多源遙感影像融合 內容簡介
本書從多源遙感成像機理和人眼視覺對影像的理解出發，研究了結合PCNN 的配準算法及基于PCNN 的全色影像、多光譜影像、高分辨率SAR 影像、無人機航拍影像和高光譜影像等多源遙感影像融合的理論與算法。首先，簡要介紹了多源遙感影像融合的起源與現狀。其次，回顧了PCNN 的幾種常見模型。鑒于遙感影像配準是實現遙感影像像素級融合的前提，本書提出了兩種基于自適應PCNN 分割的遙感影像配準算法。在后續章節中，本書主要研究并提出了結合PCNN 分割特性的全色銳化融合算法、參數優化的PCNN 全色銳化融合算法、改進PCNN 的全色銳化融合模型、基于PCNN 的衛星多光譜影像與無人機航拍影像融合算法和基于PCNN 的高光譜影像融合算法等。本書內容為作者團隊多年來取得的科研成果，涵蓋了基于PCNN 及其改進模型在全色影像、多光譜影像、高分辨率SAR 影像、無人機航拍影像和高光譜影像等多源遙感影像融合中的*新成果。這些成果不僅豐富了遙感影像配準與融合理論，也為相關領域的研究提供了借鑒與支持。
多源遙感影像融合 目錄
目錄第 1 章緒論··············································································································11.1 多源遙感影像融合的起源與發展······························································11.2 多源遙感影像融合的意義··········································································21.3 多源遙感影像融合研究現狀······································································41.3.1 傳統遙感影像全色銳化融合研究現狀·····················································41.3.2 基于視皮層神經網絡的影像融合現狀·····················································51.4 多源遙感影像融合研究的關鍵問題··························································5第2 章 PCNN 模型及特性······················································································72.1 PCNN 模型發展背景··················································································72.2 標準PCNN 模型·························································································92.2.1 PCNN 模型描述····················································································92.2.2 PCNN 模型特性·················································································.112.3 雙輸出PCNN（Dual-output PCNN，DPCNN）模型····························.11目錄
第 1 章緒論··············································································································1
1.1 多源遙感影像融合的起源與發展······························································1
1.2 多源遙感影像融合的意義··········································································2
1.3 多源遙感影像融合研究現狀······································································4
1.3.1 傳統遙感影像全色銳化融合研究現狀·····················································4
1.3.2 基于視皮層神經網絡的影像融合現狀·····················································5
1.4 多源遙感影像融合研究的關鍵問題··························································5
第2 章 PCNN 模型及特性······················································································7
2.1 PCNN 模型發展背景··················································································7
2.2 標準PCNN 模型·························································································9
2.2.1 PCNN 模型描述····················································································9
2.2.2 PCNN 模型特性·················································································.11
2.3 雙輸出PCNN（Dual-output PCNN，DPCNN）模型····························.11
2.3.1 DPCNN 模型描述···············································································12
2.3.2 DPCNN 模型特性···············································································14
2.4 彩色DPCNN（Color DPCNN，CDPCNN）模型··································.16
2.4.1 HSV 彩色空間····················································································16
2.4.2 CDPCNN 模型描述·············································································18
2.5 SAPCNN 模型··························································································.20
2.5.1 SAPCNN 模型設計·············································································20
2.5.2 SAPCNN 模型分析·············································································21
2.6 其他PCNN 相關模型··············································································.24
2.6.1 ICM 模型描述····················································································24
2.6.2 SCM 模型描述···················································································25
2.6.3 DQPCNN 模型描述·············································································25
2.7 本章小結··································································································.26
第3 章結合 PCNN 模型的遙感影像配準····························································27
3.1 研究背景··································································································.28
3.2 遙感影像配準國內外研究現狀·······························································.28
3.2.1 基于區域的影像配準算法····································································28
3.2.2 基于特征的影像配準算法····································································29
3.3 基于自適應PCNN 分割的遙感影像配準算法·······································.31
3.3.1 算法總體框架·····················································································32
3.3.2 PCNN 影像分割··················································································32
3.3.3 參數自適應PCNN 設計·······································································34
3.3.4 分割區域描述與匹配···········································································36
3.3.5 基于FSC 的配準模型參數求解····························································39
3.3.6 實驗與分析························································································40
3.4 基于PCNN 分割與點特征的多源遙感影像配準算法···························.43
3.4.1 算法總體框架·····················································································44
3.4.2 UR-SIFT 點特征提取與匹配································································45
3.4.3 自適應PCNN 分割區域匹配································································50
3.4.4 實驗與分析························································································51
3.5 本章小結··································································································.58
第4 章 PCNN 分割特性與遙感影像全色銳化融合·············································59
4.1 研究
展開全部
多源遙感影像融合 作者簡介
李小軍，理學博士，博士后，碩士生導師。曾工作于中國工程物理研究院電子工程研究所，任職副研究員。現工作于蘭州交通大學測繪與地理信息學院，任職副教授。主持了多項軍委裝備發展部跨行業預研重點項目及國家自然科學基金項目。發表SCI、EI論文十余篇，獲批國家發明專利2項，研究領域主要包括遙感數字影像處理、影像融合和神經網絡等。
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