【摘要】：利用無人機(jī)采集油松樣地圖像,提取圖像中的單株樣本樹圖像,計算單株樣本樹圖像的多個紋理特征值,對紋理特征值進(jìn)行災(zāi)害分級,與地面基于失葉率調(diào)查的災(zāi)害分級進(jìn)行比對,探索能準(zhǔn)確描述油松受災(zāi)情況的無人機(jī)圖像紋理特征。實驗結(jié)果表明,受災(zāi)油松圖像的三種分形特征,即分形維數(shù)、縫隙量及維數(shù)升降因子能較好地反映油松的失葉率狀況,可作為油松受災(zāi)級別的圖像判定特征,同時上述分形特征也適用于整塊油松樣地的受災(zāi)級別判定。
[Abstract]:An unmanned aerial vehicle (UAV) was used to collect the image of Pinus tabulaeformis sample plot, extract the image of a single sample tree, calculate the multi-texture eigenvalues of the image of a single sample tree, and classify the feature values of the texture. Compared with the disaster classification based on the investigation of leaf loss rate on the ground, the texture features of UAV images which can accurately describe the disaster situation of Pinus tabulaeformis are explored. The experimental results show that the three fractal features of the image of Pinus tabulaeformis (Pinus tabulaeformis), that is, fractal dimension, slit volume and dimension rise and fall factor, can well reflect the leaf loss rate of Pinus tabulaeformis, and can be used as the image decision feature of the disaster level of Pinus tabulaeformis. At the same time, the fractal features mentioned above can also be used to determine the disaster level of the whole plot of Pinus tabulaeformis.
【作者單位】： 北京林業(yè)大學(xué)信息學(xué)院;北京林業(yè)大學(xué)林學(xué)院;
【基金】：林業(yè)公益性行業(yè)科研專項資助項目(201404401)
【分類號】：S763.7;TP391.41

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