基于Lab颜色空间的棉花覆盖度提取方法研究

基于手持高清可见光图像和无人机可见光遥感影像中植被与非植被像元在不同颜色空间单通道上分布的差异性,以苗期和蕾期的棉花为对象,进行了棉花覆盖度的提取方法研究。基于不同天气状况和不同采集时刻等光照条件下采集的29幅具有不同覆盖度的棉花地面可见光图像,分别对比分析了Lab颜色空间a通道、RGB颜色空间2G-R-B指数和HIS颜色空间H通道对棉花的识别能力,以及使用动态阈值和固定阈值两种情况下的棉花覆盖度提取精度。其中动态阈值通过植被与非植被像元的高斯分布交点确定,固定阈值在3种颜色空间分别设置为动态阈值的均值。结果表明,植被像元与非植被像元在a通道、2G-R-B指数和H通道上呈现高斯分布,可以采用非线性最小二乘算法实现高斯分布拟合。通过高斯分布拟合求解交点得到的动态分类阈值分布范围较为集中,将其均值-3.78、0.06、0.13设定为固定分类阈值。相比于2G-R-B指数和H通道,a通道对绿色植被的识别能力最好,更适合提取棉花植被覆盖度;相比于动态阈值,固定阈值的提取精度更好,平均提取误差为0.009 4。将该方法应用到无人机尺度时,同样可以较好地提取不同天气状况和不同土壤干湿类型的棉花覆盖度,且总体平均提取误差为0.012。经过初步检验和分析认为,基于植被与非植被像元在Lab颜色空间a通道上分布的差异性,结合固定分类阈值,可以精确地提取不同光照条件下的苗期和蕾期棉花覆盖度。

Extraction Methods of Cotton Coverage Based on Lab Color Space

The extraction method of cotton coverage was studied based on the difference of vegetation and non-vegetation pixels of RGB images from hand-held camera and unmanned aerial vehicle (UAV) remote sensing in different color spaces. Under different lighting conditions, totally 29 high-resolution (0.4mm) RGB images of cotton in seedling and bud stage were obtained by hand-held digital camera. The recognition abilities of cotton in Lab (a), RGB (2G-R-B) and HIS (H) color spaces were compared and analyzed. Two threshold classification threshold getting methods, dynamic threshold and fixed threshold were used to extract cotton coverage. The dynamic thresholds were determined by the intersection of the Gaussian distributions of vegetation and non-vegetation pixels. The fixed thresholds were set as the mean values of dynamic thresholds in the three color spaces, respectively. The results showed that vegetation and non vegetation pixels obeyed Gaussian distribution in a, 2G-R-B, and H color spaces, which could be fitted by using nonlinear least-squares algorithm. The distribution range of dynamic classification thresholds was relatively concentrated, and their mean values of -3.78, 0.06 and 0.13 could be set as fixed classification thresholds. Compared with 2G-R-B and H, the a color space had the best ability to identify green vegetation and was more suitable for extracting cotton vegetation coverage. Compared with dynamic threshold, the extraction accuracy based on fixed threshold was better and the average extraction error was 0.0094. It can also accurately extract fractional vegetation cover (FVC) from UAV images captured under different light conditions (sunny and cloudy) with different soil moistures. After preliminary tests and analysis, it was believed that based on the differences of vegetation and non-vegetation pixels in Lab (a) color space, combining with a fixed classification threshold of -3.78,cotton coverage in seedling and bud stage could be accurately extracted under different light conditions.