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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2013-05-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-1-W1/333/2013/isprsarchives-XL-1-W1-333-2013.pdf |
| Summary: | 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. |
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| ISSN: | 1682-1750 2194-9034 |