【摘要】：[目的]本文旨在克服光照不均引起的低對比度、反光、陰影、光斑及遮擋等對大田復(fù)雜背景下小麥冠層圖像分割的干擾。[方法]設(shè)計(jì)了一種結(jié)合脈沖耦合神經(jīng)網(wǎng)絡(luò)(pulse coupled neural network,PCNN)與同態(tài)濾波的自適應(yīng)圖像增強(qiáng)和基于L*a*b*顏色空間α角度模型的K均值聚類分割算法。首先,將小麥冠層圖像轉(zhuǎn)換到HSI顏色空間,采用自適應(yīng)算法對HSI空間的I分量進(jìn)行增強(qiáng)處理,適當(dāng)調(diào)節(jié)飽和度S分量,補(bǔ)償光照強(qiáng)度分布不均,去除陰影及拉大對比度;其次,將增強(qiáng)處理后的圖像映射到L*a*b*顏色空間,提取a*、b*分量建立α角度模型;最后,基于α進(jìn)行K均值聚類分割處理。[結(jié)果]拔節(jié)前后光照強(qiáng)度不一、光照不均的冬小麥冠層圖像的分割試驗(yàn)結(jié)果表明,該算法可一定程度避免基于L*a*b*顏色空間α角度分量K均值聚類的過分割現(xiàn)象;改善基于HSI空間H分量K均值聚類的欠分割缺陷,且對光斑、陰影遮擋、反光突出的圖像分割更完整準(zhǔn)確。[結(jié)論]本算法可為大田復(fù)雜背景下光照多變的作物冠層圖像分割提供參考方法。
[Abstract]:[aim] the aim of this paper is to overcome the interference of low contrast, reflection, shadow, spot and occlusion on wheat canopy image segmentation in complex background of field. [methods] an adaptive image enhancement algorithm based on pulse-coupled neural network (pulse coupled neural network,PCNN) and homomorphism filtering and K-means clustering algorithm based on a-angle model of Lana * color space was designed. [methods] an adaptive image enhancement algorithm based on pulse-coupled neural network and homomorphism filter was designed. Firstly, the wheat canopy image is converted to HSI color space, and the I component of HSI space is enhanced by adaptive algorithm. The S component of saturation is adjusted appropriately to compensate the uneven distribution of light intensity, remove the shadow and enlarge the contrast. Secondly, the enhanced image is mapped to the color space, and the 偽-angle model is built. Finally, the K-means clustering segmentation is carried out based on 偽. [results] the experimental results of segmentation of winter wheat canopy images with different intensity and uneven illumination before and after jointing show that the proposed algorithm can avoid the over-cut of K-means clustering of 偽-angle component in color space. It improves the defect of under-segmentation based on K-means clustering of H-component in HSI space, and it is more complete and accurate for image segmentation of speckle, shadow occlusion and reflective highlight. [conclusion] this algorithm can provide a reference method for crop canopy image segmentation under complex background.
【作者單位】： 南京農(nóng)業(yè)大學(xué)國家信息農(nóng)業(yè)工程技術(shù)中心 南京農(nóng)業(yè)大學(xué)信息科學(xué)與技術(shù)學(xué)院 中國移動通信集團(tuán)浙江有限公司嘉興分公司
【基金】：國家重點(diǎn)研發(fā)計(jì)劃項(xiàng)目(2016YFD0300607) 江蘇省農(nóng)業(yè)科技自主創(chuàng)新資金項(xiàng)目[CX(14)2116]
【分類號】：S512.1;TP391.41

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