无人机航拍林业虫害图像分割复合梯度分水岭算法

Image segmentation method for forestry unmanned aerial vehicle pest monitoring based on composite gradient watershed algorithm

针对林业信息监测方式实时性差、监测范围有限等问题,为更加实时、准确地对林业虫害信息进行监测并计算监测样地中虫害区域比例,该文在搭建面向林区虫害监测的多旋翼无人飞行器航拍监测系统基础上,提出了一种基于复合梯度分水岭算法的图像分割方法。该方法引入全局直方图均衡化消除了图像暗纹理的影响,并采用形态学混合开闭重构滤波完成了图像样本的去噪处理。计算灰度图像各像素点的复合梯度实现了非相关区域(道路及裸地)的提取,最终利用分水岭算法实现了监测图像虫害区域的分割提取。利用该文所提算法对8幅虫害侵蚀程度不同的监测图像进行分割,并与传统分水岭算法、K-means聚类算法进行对比试验。试验结果表明,该文算法虫害区域提取的平均相对误差率分别降低6.56%、3.17%,平均相对极限测量精度分别改善7.19%、2.41%,能够相对准确地将虫害区域从监测图像中分割出来,可为后续林业虫害监测与防护提供参考。

The application of multi-rotor unmanned aerial vehicle monitoring system for forest pest information collecting has many advantages, such as low running cost, operating flexibility, easy access to data, high image resolution etc. It has been regarded as a quick access to forest insect pest information collecting. By use of unmanned aerial vehicle system, valid segmentation and extraction of pest images acquired with the help of multi-rotor unmanned aerial vehicle can be used to calculate the insect pest proportion in monitored sample field. It can provide forest conservation experts with evidence for assessing the insect pest damage. To conduct forest monitoring work and calculate the proportion of pest infested area in monitored sample field with more preciseness and fast turnaround, in this paper, we aimed to solve poor time response circle and limited monitoring range problems that exist in current forestry information monitoring method. Firstly, in this paper, we built both hardware and software systems of multi-rotor unmanned aerial vehicle. Aerial vehicle equipped with image collecting devices was used to monitor in forestry pest insect infested area and collect data in the Liaoning testing forest. In order to obtain proper resolution images, aerial vehicle took off the center of the chosen monitoring area vertically to collect photo resources. By considering needed resolution requirements on image segmentation comprehensively, the height of about 50 m was chosen for image acquisition. On the analytical basis of monitoring images, an image segmentation method based on composite gradient watershed algorithm was proposed. This method introduced global histogram equalization to eliminate the influence of dark texture and adopted the morphological hybrid open-closing reconstruction filter to complete the denoising work of the image samples, eliminate the image interference to the segmentation effect, and suppress the over-segmentation phenomenon in image segmentation process. The gray-scale image was obtained by gray-scale transformation of the pre-processed image. The non-correlation regions （road and bare ground） were extracted by calculating the composite gradient of each pixel point in the gray image. Interference to the segmentation result may arise in segmenting process due to the similar color of non-correlation region and pest insect infested area. In this paper, the mentioned region was removed from the original image, which greatly avoided the interference of the non-related region to the pest area and ensured the accuracy of the result. Finally, the watershed algorithm was applied to realize the segmentation and extraction of insect pest area in images. In order to verify the effectiveness of the proposed method, the traditional watershed algorithm and K-means clustering algorithm were used for comparing experiment methods in the segmentation of eight images with different levels of insect pest. With the help of mage segmentation device, the accurate pest insect infested area was labeled manually, and it was taken as reference value in pest insect proportion calculating step. The experiment result showed that the segmentation effect was much more similar to the manual operation result. Specifically, the relative error rate decreased by 6.56% and 3.17% and the relative limit measuring accuracy was improved by 7.19% and 2.41% in this proposed method when traditional watershed algorithm was compared with K-means cluster algorithm. Our result showed that multi-rotor unmanned aerial vehicle was helpful in real time and effective monitoring of forest pest insect. The algorithm proposed in this paper was able to accurately segment and extract pest insect area in monitoring images and the proportion of pest area in whole sample fields was acquired, thus providing valid data support for forest pest monitoring and preventing work in the future.