Feature Fusion Method based on Discriminant Correlation Analysis for Land Use Classification with Few-Shot

Due to the particularity of spatial structure in geographic data and the inadequacy of labeled data, the deep learning features can not reflect the spatial information and scale changes of objects in land-use/land-cover (LULC) images. To overcome these limitations, a feature fusion method is proposed to obtain well-discriminative and low-dimensional features, which can effectively fuse the features extracted from different levels. Given the inadequacy of labeled data, a pre-trained deep convolution neural network is proposed and used as a feature extractor to obtain the semantic features of images. To overcome the variety of scale and rotation in LULC images, the scale-invariant feature transform feature is introduced to represent the spatial structure information of the LULC images. Then different features are fused for LULC classification. In order to effectively distinguish easily-confused categories, an improved discriminant correlation analysis method based on the distance of scatter matrix between different categories is designed. The distance coefficient minimizes the correlation among categories. The proposed methods are applied on the UC Merced land use dataset, and achieve outstanding classification performance with dozens of feature dimensions under the case of limited training samples. The results demonstrated that the proposed method outperform several state-of-the-art methods.