本文關(guān)鍵詞： Gabor多尺度 自適應(yīng)加權(quán) 稀疏表示 融合特征　出處：《小型微型計(jì)算機(jī)系統(tǒng)》2017年09期 　論文類型：期刊論文

【摘要】：針對(duì)遙感圖像中不同層次的空間結(jié)構(gòu)差異及目標(biāo)含有不同角度的旋轉(zhuǎn)的情況,提出一種基于Gabor多尺度自適應(yīng)加權(quán)與稀疏表示的遙感目標(biāo)識(shí)別方法.首先對(duì)訓(xùn)練樣本和待測樣本進(jìn)行Gabor小波變換,對(duì)各個(gè)方向的Gabor特征進(jìn)行綜合,使它們近似各向同性,根據(jù)各尺度特征包含信息量進(jìn)行自適應(yīng)加權(quán)求和并經(jīng)過PCA降維求得融合特征,將原始的訓(xùn)練字典改為融合特征字典,從而使字典更加具有判別能力,提高識(shí)別率.實(shí)驗(yàn)表明,該方法對(duì)遙感圖像目標(biāo)識(shí)別具有較好的魯棒性.
[Abstract]:In view of the spatial structure differences at different levels in remote sensing images and the rotation of objects with different angles, A method of remote sensing target recognition based on Gabor multi-scale adaptive weighting and sparse representation is proposed. Firstly, the Gabor wavelet transform is applied to the training samples and the samples to be tested, and the Gabor features in each direction are synthesized to make them nearly isotropic. According to the amount of information contained in each scale, the adaptive weighted summation is carried out and the fusion feature is obtained by reducing the dimension of the PCA. The original training dictionary is changed into the fusion feature dictionary, which makes the dictionary more discriminant and improves the recognition rate. This method is robust to target recognition in remote sensing images.
【作者單位】： 長沙理工大學(xué)計(jì)算機(jī)與通信工程學(xué)院綜合交通運(yùn)輸大數(shù)據(jù)智能處理湖南省重點(diǎn)實(shí)驗(yàn)室;
【基金】：國防"九七三"重點(diǎn)基礎(chǔ)研究項(xiàng)目(613XXX0301)資助
【分類號(hào)】：TP75

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本文編號(hào)：1545413