S. Muller, R. Feitosa, G.L. Abelha Mota, D. da Costa, V. da Silva, K. Tanisaki
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引用次数: 6
摘要
本文研究了一种基于语义网络的基于知识的方法在低分辨率卫星图像辅助下的土地利用自动制图中的应用。与视觉照片解译一样,自动图像解译考虑了由专业照片解译人员提供的场景和传感器知识,以及有关该地区的其他信息,如数字高程模型、突现岩石的位置、水体的映射和道路网络。通过这种方法,可以自动执行场景分析,模仿照片解释器的推理。该建议的实施采用了GEOAIDA [J]。Buckner et al., June 2001]系统,是汉诺威大学开发的一种灵活的图像解释环境,它利用语义网络来构建特定领域的知识。在报告的实验中,对多光谱SPOT 3xs图像进行了分析,并将结果与人工制作的调查场景参考图进行了评估和比较。对自动获得的结果进行评估,并与人工制作的调查场景参考地图进行比较。实验结果证明了基于知识的低分辨率卫星图像解译方法的潜力。
GEOAIDA applied to SPOT satellite image interpretation
This paper investigates the application of a knowledge-based approach, founded on semantic networks, to the automatic land use mapping assisted by low resolution satellite images. Like the visual photo-interpretation, the automatic image interpretation considers scene and sensors knowledge, delivered by an expert photo-interpreter, as well as additional information about the region like the digital elevation model, the position of the emergent rocks, the mapping of the water bodies and the road-network. By this means, the analysis of a scene can be automatically performed, mimicking the reasoning of the photo-interpreter. The implementation of such proposal employed the GEOAIDA [J. Buckner et al., June 2001] system, a flexible environment for image interpretation developed at the University of Hanover, which exploits semantic networks to structure the domain specific knowledge. In the reported experiments, a multispectral SPOT 3 XS image was analysed resulting were evaluated and compared with a manually made reference map of the investigated scene. The automatically obtained results were evaluated and compared with a manually made reference map of the investigated scene. The experimental results demonstrate the potential of a knowledge-based approach for low resolution satellite images interpretation.