本文選題：概率潛在語義分析　切入點：濕地檢測　出處：《測繪學報》2017年08期

【摘要】：提出了一種基于概率潛在語義分析的多源遙感影像濕地檢測方法。首先提取高分辨率影像的光譜、紋理和濕地場景的地物組成成分,并結合由多光譜遙感數(shù)據(jù)提取的濕地地表溫度、土壤含水量,組成濕地場景的特征空間;然后利用概率潛在語義分析將濕地場景表示成多個潛在語義的組合,并用潛在語義的權值向量來描述濕地場景的特征空間;最后利用SVM分類器實現(xiàn)濕地場景的檢測。試驗表明,概率潛在語義分析能夠將濕地的高維特征空間映射到低維的潛在語義空間中,地物組成成分和定量環(huán)境特征的加入能更加有效地表征濕地特征空間,提高濕地檢測精度。
[Abstract]:A multi-source remote sensing image wetland detection method based on probabilistic latent semantic analysis is proposed. Combined with the wetland surface temperature and soil water content extracted from multispectral remote sensing data, the wetland scene is represented as a combination of multiple potential semantics by using probabilistic latent semantic analysis. The potential semantic weight vector is used to describe the feature space of the wetland scene. Finally, the SVM classifier is used to detect the wetland scene. Probabilistic latent semantic analysis can map the high-dimensional feature space of wetland to low-dimensional potential semantic space. The addition of the composition of ground objects and quantitative environmental features can more effectively represent the wetland feature space and improve the accuracy of wetland detection.
【作者單位】： 中國地質大學(武漢)信息工程學院;武漢大學遙感信息工程學院;
【基金】：國家重點研發(fā)計劃(2016YFB0502603) 地理國情監(jiān)測國家測繪地理信息局重點實驗室開放基金(2016NGCM09)~~
【分類號】：P237
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本文編號：1688701