基于剪切波变换和无人机麦田图像的区域杂草识别方法

区域杂草的识别有利于植保作业中的除草剂精准喷施。现有图像处理技术主要针对行间和株间杂草,而传统的图像采集与分析设备对苗期麦田杂草的识别存在一定局限性,难以满足非人工的区域性喷洒农药等作业需求。由于麦田区域中的麦苗和杂草具有形态和颜色区分度差的特点,传统的图像识别方法难以有效识别。针对此问题,该文提出利用剪切波变换对无人机麦田区域图像中杂草进行识别。该方法利用其自身的方向敏感性以及在纹理识别中的方向无关性,根据麦田区域图像在杂草较多的部分叶片纹理杂乱,反之则纹理相对规则的特点,处理得到不同尺度和不同方向下小麦与杂草的剪切波系数。然后针对小麦和杂草剪切波系数的不同特征,对剪切波系数矩阵进行归一化处理,同时对其均值和方差进行了统计分析,得到麦苗和杂草剪切波系数图中竖直锥第二尺度所有系数均值的区分值约为0.07,第二尺度各个方向的均方差均值的区分值约为0.08。通过对含杂草麦苗区域图像以及麦苗区域图像的验证,准确率为69.2%,效果优于传统的灰度共生矩阵方法。此外,该文对无人机拍摄区域图采用分块的方法,实现了对非麦苗区域的有效标识。由此可见,剪切波变换方法能够为基于低空植保无人机喷洒农药中的区域杂草识别提供参考。

Regional weed identification method from wheat field based on unmanned aerial vehicle image and shearlets

Abstract: Weeds is one of the main harmful factors to the yield and quality of wheat and other main crops during seedling stage. Image processing technology is often used in weed recognition, but the method mainly cares about the weeds between different rows, which is always inefficient and wasteful for unmanned aerial vehicle (UAV) and machine spraying ways. In order to overcome the limitations above, this paper proposes a regional weed identification method, which takes advantage of properties of shearlets. Shearlets have attracted much attention in the field of image recognition because of its good sensitivity and fast computation in texture recognition. Meanwhile, it is a multi-scale analysis method with the characteristic of direction independence. Through the comparison of the regional images of the wheat and weed, it shows that the texture of the weed leaves is more complex while the wheat leaves are relatively regular. So we first choose 8 images including 4 wheat images and 4 weed images. Then we obtain shearlet transform coefficient (STC) at diverse scales and directions according to the different texture characteristics of wheat and weeds. In the STC images of different scales, the brightness from black to white represents different coefficient value. Moreover, the complexity of bright regional distribution represents the textural complexity, which can be used to distinguish wheat and weeds. Shearlets have self-adaptability because of different directions on these scales, so that obvious textural features in images taken from different angles can be detected. In our research, we take the self-adaptability of shearlets and the differences of STC images into account, and we choose the STC in the second scale of vertical cone to distinguish wheat weeds as experimental object. The result shows that the STC mean of wheat in the second scale is lower than that of weeds. Additionally, the fluctuation of STC mean of wheat is smaller than that of weeds. This study chooses 16 wheat images and 16 weeds images, aiming to distinguish weed and wheat more intuitively; we take a further statistical analysis on the mean and variance of coefficient matrixes of shearlets in the second scale of vertical cone. After normalization treatment, the distinction mean values and mean square error between wheat seedling and weeds are about 0.07 and 0.08 respectively. We randomly select 13 pictures of weeds and wheat seedling, and the recognition accuracy is 69.2%. The experimental results of contrast experiment show that the shearlet-transform method performs better than gray level co-occurrence matrix (GLCM) method to distinguish wheat seedling and weeds. We can get an explanation for the experimental results from the different theory of the shearlet-transform and GLCM. The theory of shearlet-transform shows that it can get different directions information adaptively. On the contrary, GLCM can only get the directions assigned, so the number of directions for image processing can''t be changed. In addition, the method of splitting blocks of larger image gathered by UAV is used to realize the effective identification of non-wheat region. From the experimental results, we can see that the difference between wheat and weeds is based on effective shearlet-transform, and we can generalize our method to other image classification based on textural features. Furthermore, this method performs with high flexibility and stability and it has the potential for herbicide spraying in the field.