结合Gabor小波和形态学的高分辨率图像树冠提取方法

Tree Canopy Extraction Method of High Resolution Image based on Gabor Filter and Morphology

树冠信息的遥感提取能有效辅助森林参数反演、林分长势监测、树种识别等森林调查活动。随着遥感信息自动化提取的需求不断加强,本文基于高空间分辨率遥感数据,以滁州市皇甫山林场为研究区域,设计了一种结合Gabor小波和形态学的树冠提取方法。该方法首先采用Gabor小波提取出纹理特征,其次结合K-means聚类分析方法,对PCA降维后的纹理特征向量提取出阔叶林区,最后基于形态学理论降低影像噪声,并利用前景后景标记的分水岭方法进行单木树冠提取。经过与人工解译的树冠信息结果对比发现,在郁闭度较高的阔叶林区,该自动化方法提取树冠精度较高,分割准确率Ad为79.59%,F测度达到了79.00%能有效提供精确的单木树冠信息,为林业经济调查技术的发展具有一定的实践意义。

As an important part of forestry economy and an important physiological structure of photosynthesis,tree canopy is also an important forest parameter and stand factor in the process of forest inventory such as growth monitoring and tree species identification. Accurate extraction of tree crown information can effectively provide support for forest inventory. With the development of remote sensing technology, the disadvantages of traditional forest survey methods are obvious, and they are gradually developing towards the direction of combining remote sensing information extraction with traditional forest inventory. In order to improve the accuracy of remote sensing information extraction, people began to use high spatial resolution remote sensing images, combined with computer automation technology, to develop remote sensing information automatic extraction methods. As the demand of the automatic extraction of remote sensing information continuously strengthen, based on high spatial resolution remote sensing data in Chuzhou HuangFu Mountain tree farm field as the study area, we proposed a method that combination of Gabor wavelet and morphology of canopy extraction. First we extracted texture features by Gabor filter, K-means clustering analysis was used to extract dense forest area from the texture feature by PCA(Principal Component Analysis) extracted broadleaf forest region, based on the morphological theory to reduce image noise, and used the prospect foreground markers of the watershed method extract individual tree crown. After comparing with the artificial interpretation canopy information found that the canopy precision automation method extracted in the dense forest area, segmentation accuracy is 79.59%, F measure reached 79.00%, and can accurately provide individual tree crown information, it has a certain practical significance of the development of forestry economic survey technology.