Discriminative Latent-Space Learning for Fine-Grained Object Detection in Remote Sensing Images

Fine-grained object detection (FOD) is essential in many remote sensing image interpretation tasks. Existing FOD methods have achieved remarkable progress in modeling discriminative features for FOD in remote sensing images. However, they receive unsatisfactory recognition accuracy due to the curse of dimensionality (CoD) problem. In this article, we propose an orthogonal constraint-based discriminative latent-space learning (DLL) method to address the CoD problem. We first optimize a sparse optimization paradigm with convex relaxation to extract shared features between fine-grained objects into a low-dimensional latent space. Then, we solve the orthogonal space of the latent space for extracting irrelevant features related to common features from input features, i.e., discriminative features. The sparse optimization paradigm reprojects the underlying trends hidden in high-dimensional feature space into a low-dimensional latent space and hence addresses the CoD problem. We use neural parameters with latent space and orthogonal constraints to approximately solve the proposed DLL, which can be efficiently optimized under convex programming tools. We theoretically prove the effectiveness of the proposed DLL. By adding our DLL to the existing deep learning-based object detection method, extensive experiments conducted on two datasets demonstrate that our method achieves superior performance compared with other state-of-the-art methods.