基于多特征融合的城市遥感图像自动解译方法

An Automatic Interpretation Approach for Urban Remote Sensing Image Based on Multiple Features Integration

为更全面有效地解译城市遥感图像,提出了一种新的基于多特征融合的自动解译方法。该方法定义对象网络来表达图像结构并获取更为准确的处理单元。在此基础上,综合分析颜色、纹理、形状和位置等众多特征,通过自适应的概率学习训练最优分类器并标记目标类别。方法中还结合上下文信息进行空间平滑,大大消除了噪音、遮挡等影响,矢量标绘后得到最终解译结果。实验表明,该方法准确率高、鲁棒性好,适用于多种遥感图像城市场景的自动解译。

A new automatic approach based on multiple features integration is proposed to interpret urban remote sensing images more effectively and comprehensively. The approach builds a hierarchical objects network to organize image structure and gets precise processing units. Then the probabilistic learning integrating multiple features including colour, texture, shape and position are performed to train a best classifier,and label all of the objects according to their classification values. The approach also applies spatial smoothing which incorporates contextual information to eliminate the adverse effects caused by background disturbance, occlusion and so on. After vectorization procedure, final results are given. Experiments demonstrate that proposed approach achieve high exactness and robustness in interpreting manifold urban remote sensing images.