Machine learning

Operations management

Deep learning

Redundancy (engineering)

Metric (unit)

Contextual image classification

Operating system

Computer science

Image (mathematics)

Pattern recognition (psychology)

Artificial intelligence

Economics

IEEE Transactions on Geoscience and Remote Sensing

Deep models with multiple layers have demonstrated their potential in learning abstract and invariant features for better representation and classification of remote sensing images. Moreover, metric learning (ML) is usually introduced into the deep models to further increase the discrimination of deep representations. However, the usual deep ML methods treat the training samples in each training batch in the stochastic gradient descent-based learning procedure independently, and thus, they neglect the important contextual (structural) information in the training samples. In this paper, we first introduce deep structural ML (DSML) into the literature of remote sensing scene classification and specifically capture and use the structural information during the training on the remote sensing images. Further analysis demonstrates that DSML usually makes many learned metric parameters similar. This similarity leads to obvious model redundancy and thus decreases the representational ability of the model. To address ...

Diversity-Promoting Deep Structural Metric Learning for Remote Sensing Scene Classification