An improved LBP transfer learning for remote sensing object recognition

作者：Dan, Zhiping^*; Sang, Nong; He, Yonggang; Sun, Shuifa

来源：Optik, 2014, 125(1): 482-485.

DOI：10.1016/j.ijleo.2013.07.021

• Sensors applications
• Object recognition
• Image processing
• Machine learning

In the object recognition process of the remote sensing image, as the object data is influenced by the imaging scale, intensity and the shape of the object, the distribution of the object data is very different with that of known training data which leads to the low reliability of the object recognition. Aimed at the problem, an object recognition method based on transfer learning framework for the remote sensing image is proposed in this paper. The feature vectors of the object data are extracted by an improved LBP firstly, and then the transfer learning is used to find the common parameters among the feature spaces of the object data with the different distributions. The transfer learning method can transfer knowledge from the old object data to the new object data and improve the performance of the object recognition. According to the experiments in the satellite remote sensing images, it shows that the accuracy of object recognition has been greatly improved by our proposed method compared with the other classical methods.

• 出版日期2014
• 单位华中科技大学; 三峡大学