基于小波包变换的农作物分类无人机遥感影像适宜尺度筛选

为寻找适宜分类的空间尺度,该文提出一种基于小波包的空间尺度选择方法。该文以无人机航拍农作物影像为数据源,针对高空间分辨率遥感影像农作物分类问题,基于小波包变换对影像分类特征进行多尺度定量分析。将七种农作物影像样本进行小波包分解,从高频部分获取均值,方差,能量,能量差四种纹理信息,从低频部分获取光谱信息,构建分类特征矢量,通过作物样本之间的J-M距离,分析在不同小波包分解层样本之间的可分性,并进一步通过农作物面向对象分类精度和分割耗时评价适宜尺度。该文选择位于河北的涿州农场为研究区,利用无人机航空影像,对提出的方法进行试验验证,结果显示:小波包分解到第三、四层级时,即空间分辨率为0.32~0.64 m时,适宜农作物面向对象分类;在适宜尺度下,基于小波包分解的面向对象分类总体分类精度可达到89%,Kappa系数可达到0.85。研究结果可为高空间分辨率遥感农作物精细识别提供支撑。

Optimal scale of crop classification using unmanned aerial vehicle remote sensing imagery based on wavelet packet transform

Abstract: For the high-resolution remote sensing imagery, the space scale has effect on the classification accuracy and efficiency. The UAV image can achieve very high spatial resolution, which has promoted the development of object-oriented classification, at the same time, it also takes some influence on high-resolution remote sensing imagery classification. Therefore, it''s necessary to select optimal scale for classification. In this paper, to resolve high-resolution remote sensing imagery classification problem, we used UAV aerial crop images in Zhuozhou, Hebei province, as a data source, and applied wavelet packet transform to the multi-scale quantitative analysis of classification characteristics that belong to high-resolution remote sensing imagery. Wavelet packet decomposition was applied to seven of the most popular crops images, and then considering multi-factors, the most optimal wavelet packet decomposition tree were selected. Then, we selected the high frequency leaf node of each level according to the optimal wavelet packet decomposition tree and built the texture characteristic vector by four kinds of textural information including mean, variance, energy and energy difference which were computed statistically by the wavelet packet coefficient of selected node. Spectral information was obtained from the low frequency part. The classification characteristic vector was built by integrating the texture characteristic vector and the spectral information. To analyze the separability of the crop sample in different levels of wavelet packet decomposition tree, we needed to calculate the J-M distance of the classification characteristic vector between samples in different levels by matching the wavelet packet levels and the resolution, and then acquiring the optimal spatial scales for object-oriented classification. To verify the result, we conducted object-oriented classification experiment based on wavelet packet transform on imagery of different resolution, and chose accuracy of object-oriented classification and time-consuming of division as evaluation criterion to evaluate the result. The original images were decomposed to five levels, from which wavelet packet transform method was used. The texture information and spectral information which can extract from the optimal wavelet packet decomposition tree were used to build classification characteristic diagram. Then we acquired resampling images of different resolutions which matched with the wavelet packet decomposition levels. Classification characteristic diagram as the thematic layers was used to classify the imagery. Finally we employed overall accuracy, Kappa and time-consuming to assess the suitable scale. The results showed that, 1) In the third and fourth levels of wavelet packet decomposition tree (the spatial resolution was 0.32 -0.64 m), the J-M distance of different samples become maximum which meant the strongest separability; 2) The accuracy of object-oriented classification based on wavelet packet transform was the highest in overall accuracy (0.90 and 0.89) when the spatial resolution was 0.32-0.64 m, and also saved a lot of time than the higher resolution (0.16 m). We concluded that it was suitable for crop object-oriented classification in the third and fourth levels of wavelet packet decomposition (the spatial resolution is 0.32-0.64 m). The method used in this paper for selecting optimal spatial scale for crop classification in high-resolution remote sensing imagery base on wavelet packet transform can accurately select the spatial scale with optimal classification accuracy and the highest classification efficiency. To some extent, the classification accuracy was improved by the classification characteristic which extracted via the method of wavelet packet transform. This proposed method may help with the fine recognition of crops using high-resolution remote sensing images.