COFGA: Classification Of Fine-Grained Features In Aerial Images
This addresses the problem of fine-grained object classification in aerial images for researchers in computer vision, though it appears incremental as it focuses on dataset creation and benchmarking rather than novel methods.
The paper introduces COFGA, a dataset for fine-grained multi-class classification of objects in aerial images, and evaluates existing state-of-the-art models and modified deep neural networks on it, while also detailing the first published competition for this task.
Classification between thousands of classes in high-resolution images is one of the heavily studied problems in deep learning over the last decade. However, the challenge of fine-grained multi-class classification of objects in aerial images, especially in low resource cases, is still challenging and an active area of research in the literature. Solving this problem can give rise to various applications in the field of scene understanding and classification and re-identification of specific objects from aerial images. In this paper, we provide a description of our dataset - COFGA of multi-class annotated objects in aerial images. We examine the results of existing state-of-the-art models and modified deep neural networks. Finally, we explain in detail the first published competition for solving this task.