An Automatic Interpretation Approach for Urban Remote Sensing Image Based on Multiple Features Integration
基于多特征融合的城市遥感图像自动解译方法

For the purpose of interpreting urban remote sensing images more effectively and comprehensively, this paper proposes a new automatic interpretation approach based on multiple features integration. The approach builds a hierarchical objects network at first to organize image structure, getting precise processing units. Then the probabilistic learning integrating multiple features including colour, texture, shape and position is 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 result is given. Experiments demonstrate that proposed approach achieve high exactness and robustness in interpreting manifold urban remote sensing images.