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

【摘要】：針對遙感圖像中不同層次的空間結構差異及目標含有不同角度的旋轉的情況,提出一種基于Gabor多尺度自適應加權與稀疏表示的遙感目標識別方法.首先對訓練樣本和待測樣本進行Gabor小波變換,對各個方向的Gabor特征進行綜合,使它們近似各向同性,根據(jù)各尺度特征包含信息量進行自適應加權求和并經(jīng)過PCA降維求得融合特征,將原始的訓練字典改為融合特征字典,從而使字典更加具有判別能力,提高識別率.實驗表明,該方法對遙感圖像目標識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】： 長沙理工大學計算機與通信工程學院綜合交通運輸大數(shù)據(jù)智能處理湖南省重點實驗室;
【基金】：國防"九七三"重點基礎研究項目(613XXX0301)資助
【分類號】：TP75

【相似文獻】

相關期刊論文 前1條

1 吳秀蕓;李艷;錢磊;;基于多尺度自適應加權的改進Canny算子[J];遙感信息;2010年04期

，

本文編號：1545413