Optimization Framework for Spatiotemporal Analysis Units Based on Floating Car Data

Few-shot remote sensing image scene classification based on multiscale covariance metric network (MCMNet)

Few-shot learning (FSL) is a paradigm that simulates the fast learning ability of human beings, which can learn the feature differences between two groups of small-scale samples with common label space, and the label space of the training set and the test set is not repeated. By this way, it can quickly identify the categories of the unseen image in the test set. This method is widely used in image scene recognition, and it is expected to overcome difficulties of scarce annotated samples in remote sensing (RS). However, among most existing FSL methods, images were embed into Euclidean space, and the similarity between features at the last layer of deep network were measured by Euclidean distance. It is difficult to measure the inter-class similarity and intra-class difference of RS images. In this paper, we propose a multi-scale covariance network (MCMNet) for the application of remote sensing scene classification (RSSC). Taking Conv64F as the backbone, we mapped the features of the 1, 2, and 4 layers of the network to the manifold space by constructing a regional covariance matrix to form a covariance network with different scales. For each layer of features, we introduce the center in manifold space as a prototype for different categories of features. We simultaneously measure the similarity of three prototypes on the manifold space with different scales to form three loss functions and optimize the whole network by episodic training strategy. We conducted comparative experiments on three public datasets. The results show that the classification accuracy (CA) of our proposed method is from 1.35 % to 2.36% higher than that of the most excellent method, which demonstrates that the performance of MCMNet outperforms other methods.