【摘要】：針對(duì)遙感影像由于載荷類型、觀測(cè)角度、地形起伏等內(nèi)外部因素造成的影像局部幾何畸變,而基于全局配準(zhǔn)方法制約著影像配準(zhǔn)精度的提高,基于像元的彈性配準(zhǔn)方法可大幅提升遙感影像的配準(zhǔn)精度,但是存在運(yùn)算效率這一瓶頸等問(wèn)題,該文利用像元彈性配準(zhǔn)參數(shù)的稀疏性,提出一種基于壓縮感知的彈性配準(zhǔn)方法。通過(guò)對(duì)遙感影像像元梯度幅值響應(yīng)較強(qiáng)的點(diǎn)進(jìn)行隨機(jī)抽樣,形成觀測(cè)樣本點(diǎn)集,采用彈性配準(zhǔn)局部參數(shù)解算模型求解樣本點(diǎn)平移參數(shù);通過(guò)壓縮感知稀疏重構(gòu)算法重構(gòu)影像各像元平移參數(shù)。實(shí)驗(yàn)表明,在配準(zhǔn)精度差異較小和一定的參數(shù)設(shè)置條件下,該方法可顯著提高彈性配準(zhǔn)運(yùn)算速度。
[Abstract]:Aiming at the local geometric distortion of remote sensing image caused by internal and external factors such as load type, observation angle, topography fluctuation and so on, the improvement of image registration accuracy is restricted by global registration method. The elastic registration method based on pixel can greatly improve the registration accuracy of remote sensing image, but there are some problems such as the bottleneck of computing efficiency. This paper proposes an elastic registration method based on compressed sensing based on the sparsity of pixel elastic registration parameters. Through random sampling of the points with strong gradient amplitude response of image pixel, the observation sample point set is formed, and the translation parameter of the sample point is solved by using the elastic registration local parameter solution model. The translational parameters of each pixel are reconstructed by compressed sparse reconstruction algorithm. The experimental results show that this method can significantly improve the speed of elastic registration under the condition of little difference in registration accuracy and certain parameter setting.
【作者單位】： 中國(guó)科學(xué)院電子學(xué)研究所;中國(guó)科學(xué)院光電研究院;中科九度(北京)空間信息技術(shù)有限責(zé)任公司;
【基金】：中國(guó)科學(xué)院科技服務(wù)網(wǎng)絡(luò)計(jì)劃項(xiàng)目(KFJ-EW-STS-046) 國(guó)家高技術(shù)研究發(fā)展計(jì)劃項(xiàng)目(2014AA09A511) 高分辨率對(duì)地觀測(cè)系統(tǒng)重大專項(xiàng)(E0303/1315/05)
【分類號(hào)】：TP751

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