基于神经网络与决策树的土壤粗糙度测量

利用野外拍摄照片判读土壤粗糙度是一种简单快速的测算方法。为了克服人工判读处理效率低、结果易受人为因素影响和目前的计算机自动读取结果易受野外杂草影响、自动化程度有待提高的缺点,提出了一种基于神经网络和决策树的土壤粗糙度测量方法。该方法在建立神经网络时充分考虑土壤边界线、白板边缘及参考方框的特征选取输入特征向量,消除噪声影响,实现图像边缘检测;并在此基础上建立决策树区分白板、土壤、参考方框等信息,完成土壤边界线和比例尺的获取。野外试验中不可避免有杂草和光照的影响,考虑到阴影、土壤和杂草色彩的差异性,决策树判决准则的选择不仅包含了纹理信息而且考虑了色彩信息。试验表明,基于神经网络与决策树的土壤粗糙度测量采用带有简单方框的参考白板能快速高效地从复杂的野外照片中获取土壤粗糙度信息,降低了拍摄要求。自动提取结果与人工判读相比,所得均方根高度的误差在5%以内,相关长度的计算误差在1%以内,具有较高的精度和可靠性。该研究方法为土壤粗糙度实时在线测算提供了有效的解决方案。

Soil surface roughness measuring method based on neural network and decision tree

Abstract: Soil surface roughness is one of the important indices commonly used to describe soil hydrological characteristics and Lambert characteristic. In microwave quantitative remote sensing application, it affects the microwave scattering values and therefore impacts the accuracy of soil moisture retrieved using microwave sensing data. Therefore, measuring soil surface roughness has become one of the research hotspots in the field of microwave remote sensing. Two kinds of techniques are used to calculate soil surface roughness, including contact method, such as the pin meter and profile meter, and non-contact method, such as ultrasonic measurement, laser scanning, three-dimensional photography, infrared measurement and radar measurement method. All these methods need some special device. The development of image processing technology and the popularization of digital camera provide a simple measuring method which only needs a reference whiteboard and a camera. However, the detailed scale information commonly used on the reference whiteboard increases the requirements for data acquisition and data processing. The purpose of this study is to provide a method to obtain the soil surface image with a simplified reference whiteboard and then to measure soil surface roughness in the presence of field environmental noise. Therefore, a simple image acquisition method is introduced and then an image processing method combining the neural network and the decision tree is proposed. The neural network is built to detect image edge points. To reduce the environmental noise effect, the input characteristic parameters of the neural network are selected carefully, which include not only gradient information, but also image direction and neighborhood consistency information. The cutting of the background section on the original image based on image edge detection result improves the computing speed effectively. A decision tree model is introduced to divide image segments into 4 classes including soil, whiteboard, reference square and vegetation, which are not easy to classify correctly using other classification methods. Considering the effects of weeds and light which are inevitable in field environment, the decision criteria of decision tree integrate the texture and color information. The texture information used is the entropy, the correlation and the first-order invariant central moment, while the color information includes gray value and a component value in Lab color space. To assess the effect of the proposed method under the conditions of different illumination and with different line widths of reference square (1 and 2 mm), 6 photos taken at 11:00, 15:00 and 18:00 on September 14, 2014 are used to measure soil surface roughness. Experiments show that the proposed measuring method combining the neural network and the decision tree can calculate soil roughness from the complex field photos efficiently. The error of root mean square height error can be controlled under 5%, and the calculation error of correlation length less than 1%. Considering the photo-taken distance and the illumination condition, the width of 2 mm for reference square on the whiteboard will be more suitable for high-precision soil roughness measuring. The method proposed in this paper is easy to understand and easy to implement. Its accuracy which can meet the requirement of soil surface roughness measuring makes it widely applicable. The suggestion based on experimental results will further improve the measuring accuracy for soil surface roughness.