本文選題：飛機(jī)目標(biāo)識(shí)別 + 遙感圖像��； 參考：《中國(guó)圖象圖形學(xué)報(bào)》2017年04期

【摘要】：目的遙感圖像飛機(jī)目標(biāo)的檢測(cè)與識(shí)別是近年來(lái)國(guó)內(nèi)外研究的熱點(diǎn)之一。傳統(tǒng)的飛機(jī)目標(biāo)識(shí)別算法一般是先通過(guò)目標(biāo)分割,然后提取不變特征進(jìn)行訓(xùn)練來(lái)完成目標(biāo)的識(shí)別。在干擾較少的情況下,傳統(tǒng)算法的識(shí)別效果較好。但遙感圖像存在著大量的干擾因素,如光照變化、復(fù)雜背景及噪聲等,因此傳統(tǒng)算法識(shí)別精度較低,耗時(shí)量較大。為快速、準(zhǔn)確識(shí)別遙感圖像中飛機(jī)目標(biāo),提出一種基于顯著圖和全局特征、局部特征結(jié)合的飛機(jī)目標(biāo)識(shí)別算法。方法首先使用改進(jìn)的Itti顯著算法提取遙感圖像中的顯著目標(biāo);接著使用基于區(qū)域增長(zhǎng)和線標(biāo)記算法尋找連通區(qū)域來(lái)確定候選目標(biāo)的數(shù)量和位置;然后提取MSA(multi-scale autoconvolution)、Pseudo-Zernike矩和HarrisLaplace特征描述子,并使用標(biāo)準(zhǔn)差和均值的比值來(lái)評(píng)估特征的穩(wěn)定性,再把提取的特征結(jié)合成特征向量;最后應(yīng)用支持向量機(jī)的方法完成對(duì)候選目標(biāo)的識(shí)別。結(jié)果實(shí)驗(yàn)結(jié)果表明,本文算法檢測(cè)率和識(shí)別率分別為97.2%和94.9%,均高于現(xiàn)有算法,并且耗時(shí)少,虛警率低(為0.03),對(duì)噪聲干擾、背景影響以及光照變化和仿射變化均具有良好的魯棒性。結(jié)論本文算法使用了圖像的3種特征信息,包括MSA、Pseudo-Zernike矩和Harris-Laplace特征描述子,有效克服單一特征的缺點(diǎn),提高了遙感圖像飛機(jī)目標(biāo)的識(shí)別率和抗干擾能力。
[Abstract]:Objective the detection and recognition of aircraft targets in remote sensing images is one of the hot research topics at home and abroad in recent years. The traditional aircraft target recognition algorithm usually uses target segmentation, and then extracts invariant features for training to complete target recognition. In the case of less interference, the recognition effect of the traditional algorithm is better. However, there are a lot of interference factors in remote sensing image, such as illumination variation, complex background and noise, etc. In order to identify aircraft targets in remote sensing images quickly and accurately, an algorithm of aircraft target recognition based on salient map and global features and local features is proposed. Methods first, the improved Itti saliency algorithm is used to extract salient targets in remote sensing images, and then the number and location of candidate targets are determined by searching connected regions based on region growth and line marking algorithms. Then MSA(multi-scale autoconvolutional pseudo-Zernike moments and HarrisLaplace feature descriptors are extracted, the stability of the features is evaluated by the ratio of standard deviation and mean value, and the extracted features are combined into feature vectors. Finally, support vector machine is used to complete the recognition of candidate targets. Results the experimental results show that the detection rate and recognition rate of the proposed algorithm are 97.2% and 94.9%, respectively, which are higher than those of the existing algorithms, and take less time, and the false alarm rate is low (0.03%), which interferes with the noise. Background effects, illumination changes and affine changes have good robustness. Conclusion the algorithm uses three kinds of feature information of image, including MSA-Pseudo-Zernike moment and Harris-Laplace feature descriptor, which overcomes the shortcoming of single feature and improves the recognition rate and anti-jamming ability of aircraft target in remote sensing image.
【作者單位】： 南昌航空大學(xué)計(jì)算機(jī)視覺(jué)研究所;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(61165011,61662049) 南昌航空大學(xué)研究生創(chuàng)新專項(xiàng)資金項(xiàng)目(YC2015058)~~
【分類號(hào)】：TP751

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本文編號(hào)：1917265