AUTOMATIC DETECTION AND RECOGNITION OF MAN-MADE OBJECTS IN HIGH RESOLUTION REMOTE SENSING IMAGES USING HIERARCHICAL SEMANTIC GRAPH MODEL
In this paper, we propose a hierarchical semantic graph model to detect and recognize man-made objects in high resolution remote sensing images automatically. Following the idea of part-based methods, our model builds a hierarchical possibility framework to explore both the appearance information and semantic relationships between objects and background. This multi-levels structure is promising to enable a more comprehensive understanding of natural scenes. After training local classifiers to calculate parts properties, we use belief propagation to transmit messages quantitatively, which could enhance the utilization of spatial constrains existed in images. Besides, discriminative learning and generative learning are combined interleavely in the inference procedure, to improve the training error and recognition efficiency. The experimental results demonstrate that this method is able to detect manmade objects in complicated surroundings with satisfactory precision and robustness.