本文選題：場(chǎng)景類別　切入點(diǎn)：類別直方圖　出處：《遙感學(xué)報(bào)》2017年02期 　論文類型：期刊論文

【摘要】：傳統(tǒng)詞包模型的視覺詞典忽略了場(chǎng)景本身包含的類別信息,難以區(qū)分不同類別但外觀相似的場(chǎng)景,針對(duì)這個(gè)問題,本文提出一種顧及場(chǎng)景類別信息的視覺單詞優(yōu)化方法,分別使用Boiman的分配策略和主成分分析對(duì)不同場(chǎng)景類別視覺單詞的模糊性和單詞冗余進(jìn)行優(yōu)化,增強(qiáng)視覺詞典的辨識(shí)能力。本文算法通過計(jì)算不同視覺單詞的影像頻率,剔除視覺詞典中影像頻率較小的視覺單詞,得到每種場(chǎng)景的類別視覺詞典,計(jì)算類別直方圖,將類別直方圖和原始視覺直方圖融合,得到不同類別場(chǎng)景的融合直方圖,將其作為SVM分類器的輸入向量進(jìn)行訓(xùn)練和分類。選取遙感場(chǎng)景標(biāo)準(zhǔn)數(shù)據(jù)集,驗(yàn)證算法,實(shí)驗(yàn)結(jié)果表明:本算法能適應(yīng)不同大小的視覺詞典,在模型中增加場(chǎng)景類別信息,增強(qiáng)了詞包模型的辨識(shí)能力,有效降低場(chǎng)景錯(cuò)分概率,總體分類精度高達(dá)89.5%,優(yōu)于傳統(tǒng)的基于金字塔匹配詞包模型的遙感影像場(chǎng)景分類算法。
[Abstract]:The visual dictionary of traditional lexical packet model ignores the category information contained in the scene itself, and it is difficult to distinguish the scene with different categories but similar appearance. In view of this problem, this paper proposes a visual word optimization method which takes into account the scene category information. The fuzzy and redundancy of visual words in different scene categories are optimized by using Boiman's assignment strategy and principal component analysis, and the recognition ability of visual dictionaries is enhanced. This algorithm calculates the image frequency of different visual words. By eliminating the visual words with less image frequency in the visual dictionary, the category visual dictionary of each scene is obtained, the category histogram is calculated, the category histogram and the original visual histogram are fused, and the fusion histogram of different kinds of scene is obtained. It is used as input vector of SVM classifier for training and classification. The standard data set of remote sensing scene is selected to verify the algorithm. The experimental results show that the algorithm can adapt to different size visual dictionaries and add scene category information to the model. The recognition ability of the word packet model is enhanced and the probability of scene misclassification is effectively reduced. The overall classification accuracy is as high as 89.5, which is superior to the traditional remote sensing image scene classification algorithm based on pyramid matching lexical packet model.
【作者單位】： 武漢大學(xué)測(cè)繪學(xué)院;
【基金】：國(guó)土資源部公益性行業(yè)科研專項(xiàng)(編號(hào):201511009-01)
【分類號(hào)】：TP751

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本文編號(hào)：1596123