中文摘要：

提出了一种基于频谱能量的高分辨率遥感图像地物识别方法。首先将预处理后的典型地物的遥感图像通过傅立叶变换从空间域转变到频率域,然后用位于频谱中心的一个矩形窗口提取频谱图上对能量贡献起主导作用的低阶频谱能量系数作为目标识别的主要特征值,并利用该特征值结合SVM分类方法对目标地物样本进行识别和分类。研究结果表明,每种地物样本均获得了较高的识别结果,总体精度达到了88.96%。

英文摘要：

An object recognition method for high-resolution remotely sensed imagery based on energy in frequency domain was proposed.Firstly,the pre-processed remote sensing images of typical objects were transformed from spatial domain into frequency domain by using the two-dimensional fast Fourier transform processing.Then the selected coefficients were composed feature vectors and sent into SVM（support vector machine） for training.Finally,SVM was used for recognition for test samples of typical objects,and the effect of feature window length on the object recognition rate has been investigated.The experimental results show that each object sample achieves comparatively high correct recognition rate when the width of feature window is 6,and the overall recognition rate is up to 88.96%.