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多源遙感影像融合 版權(quán)信息
- ISBN：9787121485602
- 條形碼：9787121485602 ; 978-7-121-48560-2
- 裝幀：平塑勒
- 冊(cè)數(shù)：暫無(wú)
- 重量：暫無(wú)
- 所屬分類(lèi)：>
多源遙感影像融合 內(nèi)容簡(jiǎn)介
本書(shū)從多源遙感成像機(jī)理和人眼視覺(jué)對(duì)影像的理解出發(fā)，研究了結(jié)合PCNN 的配準(zhǔn)算法及基于PCNN 的全色影像、多光譜影像、高分辨率SAR 影像、無(wú)人機(jī)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(jiǎn)要介紹了多源遙感影像融合的起源與現(xiàn)狀。其次，回顧了PCNN 的幾種常見(jiàn)模型。鑒于遙感影像配準(zhǔn)是實(shí)現(xiàn)遙感影像像素級(jí)融合的前提，本書(shū)提出了兩種基于自適應(yīng)PCNN 分割的遙感影像配準(zhǔn)算法。在后續(xù)章節(jié)中，本書(shū)主要研究并提出了結(jié)合PCNN 分割特性的全色銳化融合算法、參數(shù)優(yōu)化的PCNN 全色銳化融合算法、改進(jìn)PCNN 的全色銳化融合模型、基于PCNN 的衛(wèi)星多光譜影像與無(wú)人機(jī)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(shū)內(nèi)容為作者團(tuán)隊(duì)多年來(lái)取得的科研成果，涵蓋了基于PCNN 及其改進(jìn)模型在全色影像、多光譜影像、高分辨率SAR 影像、無(wú)人機(jī)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(zhǔn)與融合理論，也為相關(guān)領(lǐng)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(fā)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(xiàn)狀······································································41.3.1 傳統(tǒng)遙感影像全色銳化融合研究現(xiàn)狀·····················································41.3.2 基于視皮層神經(jīng)網(wǎng)絡(luò)的影像融合現(xiàn)狀·····················································51.4 多源遙感影像融合研究的關(guān)鍵問(wèn)題··························································5第2 章 PCNN 模型及特性······················································································72.1 PCNN 模型發(fā)展背景··················································································72.2 標(biāo)準(zhǔn)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(fā)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(xiàn)狀······································································4
1.3.1 傳統(tǒng)遙感影像全色銳化融合研究現(xiàn)狀·····················································4
1.3.2 基于視皮層神經(jīng)網(wǎng)絡(luò)的影像融合現(xiàn)狀·····················································5
1.4 多源遙感影像融合研究的關(guān)鍵問(wèn)題··························································5
第2 章 PCNN 模型及特性······················································································7
2.1 PCNN 模型發(fā)展背景··················································································7
2.2 標(biāo)準(zhǔn)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(shè)計(jì)·············································································20
2.5.2 SAPCNN 模型分析·············································································21
2.6 其他PCNN 相關(guān)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(jié)··································································································.26
第3 章結(jié)合 PCNN 模型的遙感影像配準(zhǔn)····························································27
3.1 研究背景··································································································.28
3.2 遙感影像配準(zhǔn)國(guó)內(nèi)外研究現(xiàn)狀·······························································.28
3.2.1 基于區(qū)域的影像配準(zhǔn)算法····································································28
3.2.2 基于特征的影像配準(zhǔn)算法····································································29
3.3 基于自適應(yīng)PCNN 分割的遙感影像配準(zhǔn)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(shù)自適應(yīng)PCNN 設(shè)計(jì)·······································································34
3.3.4 分割區(qū)域描述與匹配···········································································36
3.3.5 基于FSC 的配準(zhǔn)模型參數(shù)求解····························································39
3.3.6 實(shí)驗(yàn)與分析························································································40
3.4 基于PCNN 分割與點(diǎn)特征的多源遙感影像配準(zhǔn)算法···························.43
3.4.1 算法總體框架·····················································································44
3.4.2 UR-SIFT 點(diǎn)特征提取與匹配································································45
3.4.3 自適應(yīng)PCNN 分割區(qū)域匹配································································50
3.4.4 實(shí)驗(yàn)與分析························································································51
3.5 本章小結(jié)··································································································.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
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多源遙感影像融合 作者簡(jiǎn)介
李小軍，理學(xué)博士，博士后，碩士生導(dǎo)師。曾工作于中國(guó)工程物理研究院電子工程研究所，任職副研究員。現(xiàn)工作于蘭州交通大學(xué)測(cè)繪與地理信息學(xué)院，任職副教授。主持了多項(xiàng)軍委裝備發(fā)展部跨行業(yè)預(yù)研重點(diǎn)項(xiàng)目及國(guó)家自然科學(xué)基金項(xiàng)目。發(fā)表SCI、EI論文十余篇，獲批國(guó)家發(fā)明專(zhuān)利2項(xiàng)，研究領(lǐng)域主要包括遙感數(shù)字影像處理、影像融合和神經(jīng)網(wǎng)絡(luò)等。
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