ATRNet-STAR: A Large Dataset and Benchmark Toward Remote Sensing Object Recognition in the Wild
Yongxiang Liu; Weijie Li; Li Liu; Jie Zhou; Bowen Peng; Yafei Song; Xuying Xiong; Wei Yang · 2026 · IEEE Transactions on Pattern Analysis and Machine Intelligence
WASTE classifies this as Null Dataset · AI classification, approximate
A negative-result dataset — reusable evidence that an expected effect wasn't there.
Abstract
The absence of publicly available, large-scale, high-quality datasets for Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has significantly hindered the application of rapidly advancing deep learning techniques, which hold huge potential to unlock new capabilities in this field. This is primarily because collecting large volumes of diverse target samples from SAR images is prohibitively expensive, largely due to privacy concerns, the characteristics of microwave radar imagery perception, and the need for specialized expertise in data annotation. Throughout the history of SAR AT
Abstract by Yongxiang Liu; Weijie Li; Li Liu; Jie Zhou; Bowen Peng; Yafei Song; Xuying Xiong; Wei Yang, IEEE Transactions on Pattern Analysis and Machine Intelligence (2026) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1109/tpami.2026.3658649