摘 要 ：目的：针对阴影在高分辨率遥感影像的特性，提出了一种多尺度分割和形态学运算相结合的阴影检测方法。方法：该方法基于面向对象思想，首先利用均值漂移法实现影像分割生成对象，并以对象为基本单元分别进行形态学膨胀和腐蚀运算，从而获得面向对象的阴影指数；然后对影像进行多尺度分割，生成阴影指数矢量；最后对阴影指数矢量和亮度均值分别指定高低阈值，进而获得阴影检测结果。结果：选取高分二号和Google earth影像进行实验，采用误检率、漏检率和总错误率三个指标进行定量分析，并将实验结果与结合多特征法和形态学阴影指数法进行比较。在阴影检测定量精度分析中，相比于对比方法，本文方法的误检率偏高，但漏检率平均降低了7.31%；在建筑物阴影检测实验中，本文方法的漏检率同样下降了4.5个百分点；在多尺度效果融合分析中，本文方法在多组尺度组合下，各项精度指标均较理想；在阴影压盖地物实验中，三种方法的误检情况差异不大，但本文方法的漏检率得到较大改善，其下降程度平均达到了19.29%。结论：本文提出的阴影检测方法具备一定的抗干扰能力，适用性强，可靠性高。
Abstract ： Objective: In high resolution remote sensing imagery, tall objects, such as buildings and trees, often cause interference with part of the light, resulting in the absence of corresponding spectral information and forming the shadow. Therefore, the effective and accurate detection of the shadow is helpful to get the shape of the corresponding objects, the relative position, surface properties, height and other information. On the one hand, considering the common phenomenon of “same object with different spectra and same spectrum with different objects” and the details are rich in high resolution remote sensing imagery, traditional pixel-level methods are often affected by noise when detect shadow, but the imagery segmentation can allow spatially adjacent and spectrally similar pixels to be merged into the whole, so as to avoid noise interference. On the other hand, morphology operation has certain recognition ability to the prominent region of the spectrum and spectral characteristics of the shadow are often dark. On the basis of the analysis above, a method based on multi-scale segmentation and morphology operation is proposed. Method: The proposed method is based on objected-oriented idea. First, imagery segmentation objects are generated by mean shift algorithm, and object-based shadow index is obtained by objected morphology dilation and erosion operation. Then, shadow index vector and brightness mean are constructed by setting different sizes of color space and coordinate space kernel function bandwidth. Finally, the shadow index vector and brightness mean are designated high and low threshold, and the shadow detection is accomplished. Result: GF-2 imagery from Guangzhou and Google earth imageries from Ohio are used in verifying the validity of the proposed method. The proposed method is compared with principal component analysis + HSV transformation + histogram segmentation algorithm and morphological shadow index algorithm by using error rate, miss rate and total error rate. In the shadow quantitative detection experiment, although the error rate of the proposed method is relatively high, the miss rate decreases 7.31%. In the building shadow detection experiment, the miss rate of the proposed method also dropped by 4.5 percentage points. In the multi-scale effect fusion analysis, the accuracy of the proposed method is ideal under the different combination of multiple scales. In the capped ground shadow detection experiment, the error rate of the three methods are roughly the same, but the miss rate of the proposed method is significantly lower than the comparative methods, and the extent of its decrease reached an average of 19.29 percentage points. Conclusion: This study presents a shadow detection method that combines morphology operation and multi-scale segmentation. Objected-oriented idea is used in the proposed method, which effectively solves the salt and pepper phenomenon. First, On the basis of imagery segmentation, the morphology operation is used to extract dark area of spectral information. Then, shadow detection result is further determined by the brightness mean. In addition, in view of the difficulty of determining the optimal segmentation scale, the shadow index vector is constructed by multi-scale segmentation. Thus, it is helpful to effectively combine the advantages of each scale, enhance the applicability of the proposed method and reduce the dependence on the segmentation scale. From the detection results, the method of this study has achieved ideal results for different types of high resolution remote sensing imageries, and also has a good detection effect on the capped ground shadow, showing strong robustness and universal. However, the proposed method also needs to be improved. For example, if the spectral features are close to the shadows and the shapes of the areas are not much different, it needs further exploration. Moreover, how to remove some non-building shadows is also a difficult problem, especially when building shadows are adjacent to non-building shadows.