在高分遥感影像中，同类地物目标形状具有多样性，单一尺度或单一形状模版不足以描述同类目标的形状。本文利用小波变换和Fourier描述子构建了一种目标形状的多尺度描述模型，并基于该模型给出了一种新的面向对象的高分遥感影像目标识别方法。从上到下，该模型采用尺度依次减小的小波近似系数对原始形状进行近似表示，并利用Fourier描述子对其进行定量描述。利用语义规则综合考虑多个尺度下的识别结果，得到最终识别结果，减小小尺度下分割目标破碎和大尺度下小目标无法识别造成的影响，提高识别精度。基于本文方法分别对高分遥感影像中的飞机和建筑物进行识别，对比实验表明，该方法具有较高识别精度。

In High Spatial Resolution Remote Sensing (HSRRS) images, targets in the same class have different shapes. The description at one scale or one template is inadequate to describe target shapes from the same class. In this study, a multiscale shape model based on wavelet transform and Fourier descriptors is constructed. A new object-oriented method for target recognition in HSRRS images is also developed. The model uses wavelet approximation coefficients with successively decreasing scale to represent the target shape from top to bottom approximately. Approximate shapes are described quantitatively using Fourier descriptors. The final recognition results are obtained using the semantic rule to synthesize recognition results at multiple scales. This method can reduce the effect arising from the broken objects segmented at a small scale and the underidentification of small objects at a large scale. Aircrafts and buildings in HSRRS images are identified, and the comparison results show that the method proposed in this paper has higher identification accuracy.