本文選題：信息熵 + 小波包子頻段��； 參考：《中國圖象圖形學(xué)報》2017年02期

【摘要】：目的針對高光譜數(shù)據(jù)波段多、數(shù)據(jù)存在冗余的特點,將小波包信息熵特征引入到高光譜遙感分類中。方法通過對光譜曲線進(jìn)行小波包分解變換,定義了小波包信息熵特征矢量光譜角分類方法(WPE-SAM),基于USGS光譜庫中4種礦物光譜數(shù)據(jù)的分析表明,WPE-SAM可增大類間地物的可區(qū)分性。在特征矢量空間對Salina高光譜影像進(jìn)行分類計算,并討論了小波包最佳分解層的確定,分析了WPE-SAM與光譜角制圖(SAM)方法的分類精度。結(jié)果 Salina數(shù)據(jù)實例計算表明:小波包信息熵矢量能較好地描述原始光譜特征,WPE-SAM分類方法可行,總體分類精度(OA)由SAM的78.62%提高到WPE-SAM的78.66%,Kappa系數(shù)由0.769 0增加到0.769 5,平均分類精度(AA)由83.14%提高到84.18%。此外,通過Pavia數(shù)據(jù)驗證了WPE-SAM分類方法具有較強的普適性。結(jié)論小波包信息熵特征可較好地表示原始光譜波峰、波谷等特征信息,定義的小波包信息熵特征矢量光譜角分類方法(WPE-SAM)可增大類間地物可區(qū)分性,有利于分類。實驗結(jié)果表明,WPE-SAM分類方法技術(shù)可行,總體精度及Kappa系數(shù)較SAM有一定的提高,且有較強的普適性。但WPE-SAM方法精度與效率有待進(jìn)一步提高。
[Abstract]:Aim to introduce the feature of wavelet packet information entropy into hyperspectral remote sensing classification. Methods based on the wavelet packet decomposition transformation of the spectral curve, the wavelet packet information entropy characteristic vector spectral angle classification method was defined. Based on the analysis of four mineral spectral data in the USGS spectral database, it was shown that WPE-SAM could increase the distinguishing ability of the ground objects among classes. Salina hyperspectral images are classified and calculated in feature vector space. The determination of optimal decomposition layer of wavelet packet is discussed. The classification accuracy of WPE-SAM and spectral angle mapping method is analyzed. Results the calculation of Salina data shows that the wavelet packet information entropy vector can well describe the original spectral features and the WPE-SAM classification method is feasible. The total classification accuracy increased from 78.62% of SAM to 78.66kappa coefficient of WPE-SAM from 0.769 to 0.769 5, and the average classification accuracy increased from 83.14% to 84.18%. In addition, the WPE-SAM classification method is verified by Pavia data. Conclusion the wavelet packet information entropy features can well represent the original spectral peaks and troughs. The defined wavelet packet information entropy feature vector spectral angle classification method (WPE-SAM) can increase the distinguishability of ground objects among classes and is beneficial to classification. The experimental results show that the WPE-SAM classification method is feasible, and the overall precision and Kappa coefficient are higher than that of SAM. However, the accuracy and efficiency of the WPE-SAM method need to be further improved.
【作者單位】： 中國礦業(yè)大學(xué)(北京)地球科學(xué)與測繪工程學(xué)院;安徽理工大學(xué)測繪學(xué)院;
【基金】：國家自然科學(xué)基金項目(41271436)~~
【分類號】：TP751

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 白t，

本文編號：2027367