Cross-Domain Distance Metric Learning Framework With Limited Target Samples for Scene Classification of Aerial Images

In this paper, we concentrate on the problem of cross-domain aerial scene classification. The primary assumption of the proposed cross-domain distance metric learning (CDDML) framework is that training data are adequate in the source domain but limited in the target domain. One major problem of cross-domain scene classification caused by different dates, sensor positions, lighting conditions, and sensor types is data distribution bias. To solve this problem, the CDDML framework first replaces the existing color space with the proposed hybrid color features derived from all candidate color components to decrease the spectral shift between domains. Then, hybrid color features and bag of convolution features (BOCFs) are put into a discriminating DML (DDML) method to reduce the data distribution bias in the feature space. Finally, the image-to-subcategory distance measure is proposed to decrease the effect of intraclass variability on the nearest neighbor classifier by fusing hybrid color features and BOCF in the distance space. The experiments on three aerial target images or data sets confirm that the CDDML framework can obtain better results than most of the previous methods in the case of inadequate samples. Experimental results also demonstrate that DDML, hybrid color features, and the image-to-subcategory distance measure can increase the classification performance.