A novel method of aerial image classification based on attention-based local descriptors
This paper propose a novel method for object-based classification in very high spatial resolution aerial image. It combines the saliency maps very closely to extract the conspicuous local regions for better description of object and classification. Unlike the previous work on detectionof the local regions, a biologically motivated selective attention model is presented in this paper, since not all the local regions are important for describing the objects. In order to model the attention region, we propose a new attention-based local descriptor using the saliency map and relative local features to reflect the region of interest (ROI). The experimental results on the VHR aerial image dataset show that the proposed approach can obtain the state-of-the-art classification performance.
of-visual-words object-based classification saliency map
Sheng Xu Tao Fang Hong Huo Deren Li
Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai , Chin The State Key Laboratory forInformation Engineering in Surveying, Mapping and Remote Sensing, Wuhan
国际会议
The 6th International Conference on Mining Science & Technology ICMST 2009(第六届国际矿业科学技术大会)
徐州
英文
1-7
2009-10-18（万方平台首次上网日期，不代表论文的发表时间）