本文關(guān)鍵詞： 海洋光學(xué) 遙感定量反演 測(cè)量不確定性 懸浮物濃度 極限學(xué)習(xí)機(jī) 隨機(jī)抽樣一致性 N鄰近點(diǎn)抽樣一致性　出處：《光學(xué)學(xué)報(bào)》2016年07期 　論文類型：期刊論文

【摘要】：在遙感定量反演的地面同步實(shí)測(cè)環(huán)節(jié)中,人為因素、環(huán)境變化、條件限制等測(cè)量不確定性因素會(huì)不可避免地引入數(shù)據(jù)噪聲,致使水體懸浮物濃度反演精度降低。為此,提出一種顧及測(cè)量不確定性的水體懸浮物濃度遙感定量反演方法,即自適應(yīng)抽樣一致性極限學(xué)習(xí)機(jī)(ASAC-ELM)算法。該算法結(jié)合了極限學(xué)習(xí)機(jī)(ELM)、隨機(jī)抽樣一致性(RANSAC)和N鄰近點(diǎn)抽樣一致性(NAPSAC)方法的優(yōu)勢(shì)與特點(diǎn),利用參數(shù)維度自適應(yīng)地選取RANSAC或NAPSAC算法進(jìn)行參數(shù)估計(jì),避免了ELM算法易受非零均值正態(tài)分布數(shù)據(jù)噪聲影響的缺陷。ASAC-ELM算法通過選取局內(nèi)點(diǎn)(非噪聲點(diǎn))數(shù)據(jù)建立模型,可去除噪聲數(shù)據(jù)的干擾,提升模型的精度與適應(yīng)性。通過模擬多組不同數(shù)量級(jí)且服從非零均值正態(tài)分布的隨機(jī)數(shù),將加性噪聲引入訓(xùn)練數(shù)據(jù)中,實(shí)現(xiàn)不同噪聲比條件下對(duì)ASAC-ELM算法的檢驗(yàn),并與ELM算法、傳統(tǒng)反向傳播(BP)神經(jīng)網(wǎng)絡(luò)算法進(jìn)行了對(duì)比。結(jié)果表明,不同噪聲比條件下,ASAC-ELM算法的水質(zhì)懸浮物濃度反演精度高于ELM算法和傳統(tǒng)BP神經(jīng)網(wǎng)絡(luò)算法,且反演結(jié)果穩(wěn)定性較高。
[Abstract]:In the synchronous ground measurement of remote sensing quantitative inversion, the data noise will inevitably be introduced into the uncertainty factors such as human factors, environmental changes, conditions and so on, which will reduce the accuracy of the inversion of the concentration of suspended matter in the water body. In this paper, a quantitative inversion method for the concentration of suspended matter in water body by remote sensing is proposed, which takes into account the uncertainty of measurement. This algorithm combines the advantages and characteristics of the extreme learning machine (LLM), random sampling consistency (RANSAC) and the N neighborhood sampling consistency (NAPSAC) method, which is based on the adaptive sampling consistency limit learning machine (ASAC-ELM) algorithm, which combines the advantages and characteristics of the extreme learning machine (LLM) and the random sampling consistency (RANSAC). The parameter dimension is used to adaptively select RANSAC or NAPSAC algorithm for parameter estimation, which avoids the defect of ELM algorithm which is vulnerable to the non-zero mean normal distribution data noise. ASAC-ELM algorithm builds a model by selecting local points (non-noise points) data. The interference of noise data can be removed, and the accuracy and adaptability of the model can be improved. By simulating multiple random numbers of different orders of magnitude and applying normal distribution from non-zero mean, additive noise is introduced into the training data. The ASAC-ELM algorithm is tested under different noise ratio, and compared with the ELM algorithm and the traditional BP neural network algorithm. The results show that, The accuracy of ASAC-ELM algorithm under different noise ratio is higher than that of ELM algorithm and traditional BP neural network algorithm, and the stability of the inversion results is higher than that of the traditional BP neural network algorithm.
【作者單位】： 中國地質(zhì)大學(xué)(武漢)信息工程學(xué)院;
【基金】：國家自然科學(xué)基金(41501459;41301380)
【分類號(hào)】：X87
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本文編號(hào)：1538828