本文選題：作物 + 遙感　； 參考：《農(nóng)業(yè)工程學(xué)報(bào)》2017年10期

【摘要】：針對(duì)作物遙感影像因?qū)Ρ榷鹊退鶎?dǎo)致的使用尺度不變特征變換算法(scale-invariant feature transform,SIFT)提取特征點(diǎn)數(shù)目少,拼接效果不理想的情況,提出了一種基于圖像銳化的自適應(yīng)修改采樣步長(zhǎng)的非極小值抑制拼接算法,該算法在圖像預(yù)處理中引入銳化濾波器對(duì)平滑后的圖像進(jìn)行卷積,增強(qiáng)圖像細(xì)節(jié),增加特征點(diǎn)提取數(shù)目,同時(shí)通過(guò)基于尺度的自適應(yīng)修改采樣步長(zhǎng),使圖像特征點(diǎn)分布更加均勻,根據(jù)低對(duì)比度作物遙感影像的成像特性,采用非極小值抑制,提高圖像匹配效率。在查找匹配點(diǎn)的過(guò)程中,引入最優(yōu)節(jié)點(diǎn)優(yōu)先算法(best-bin-first,BBF)查找最近鄰與次近鄰,采用隨機(jī)抽樣一致算法(random sample consensus,RANSAC)優(yōu)選特征點(diǎn)。通過(guò)試驗(yàn)驗(yàn)證,該文改進(jìn)后的算法相比于標(biāo)準(zhǔn)SIFT算法,在處理低空作物遙感影像時(shí),特征點(diǎn)提取數(shù)目平均增加77.5%,特征點(diǎn)匹配對(duì)數(shù)平均增加15對(duì),對(duì)于標(biāo)準(zhǔn)SIFT算法無(wú)法匹配的低對(duì)比度作物遙感影像,提取到了8對(duì)以上的匹配點(diǎn)對(duì),滿(mǎn)足了拼接條件。該改進(jìn)算法相對(duì)于標(biāo)準(zhǔn)SIFT算法更適于低對(duì)比度遙感影像的拼接。
[Abstract]:Because of the low contrast of crop remote sensing image, the scale-invariant feature transform (sift) algorithm is used to extract the feature points and the splicing effect is not satisfactory. In this paper, an adaptive modified sampling step size suppression algorithm based on image sharpening is proposed. In this algorithm, sharpening filter is introduced into the image preprocessing to convolution the smooth image to enhance the image details. The number of feature points is increased, and the sampling step is modified adaptively based on scale to make the distribution of feature points more uniform. According to the imaging characteristics of low contrast crop remote sensing images, the non-minimum value is used to suppress the feature points. Improve the efficiency of image matching. In the process of finding matching points, the optimal node-first algorithm is introduced to find the nearest neighbor and the next nearest neighbor, and the random sample consensus algorithm is used to select the feature points. The experimental results show that compared with the standard SIFT algorithm, the improved algorithm increases the number of feature points and the logarithm of feature points by an average of 77.5 and 15 pairs respectively in processing low-altitude crop remote sensing images. For the low contrast crop remote sensing images which can not be matched by the standard SIFT algorithm, 8 pairs of matching points are extracted, which satisfy the stitching condition. Compared with the standard SIFT algorithm, the improved algorithm is more suitable for low contrast remote sensing image stitching.
【作者單位】： 東北農(nóng)業(yè)大學(xué)電氣與信息學(xué)院;
【基金】：國(guó)家重點(diǎn)研發(fā)計(jì)劃專(zhuān)項(xiàng)(2016YFD0200701);國(guó)家重點(diǎn)研發(fā)計(jì)劃專(zhuān)項(xiàng)(2016YFD020060305) 863計(jì)劃項(xiàng)目(2013AA102303)
【分類(lèi)號(hào)】：TP751

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