The ability to detect aerial objects with limited annotation is pivotal to the development of real-world aerial intelligence systems. In this work, we focus on a demanding but practical sparsely annotated object detection (SAOD) in aerial images, which encompasses a wider variety of aerial scenes with the same number of annotated objects. Although most existing SAOD methods rely on fixed thresholding to filter pseudo-labels for enhancing detector performance, adapting to aerial objects proves challenging due to the imbalanced probabilities/confidences associated with predicted aerial objects. To address this problem, we propose a novel Progressive Exploration-Conformal Learning (PECL) framework to address the SAOD task, which can adaptively perform the selection of high-quality pseudo-labels in aerial images. Specifically, the pseudo-label exploration can be formulated as a decision-making paradigm by adopting a conformal pseudo-label explorer and a multi-clue selection evaluator. The conformal pseudo-label explorer learns an adaptive policy by maximizing the cumulative reward, which can decide how to select these high-quality candidates by leveraging their essential characteristics and inter-instance contextual information. The multi-clue selection evaluator is designed to evaluate the explorer-guided pseudo-label selections by providing an instructive feedback for policy optimization. Finally, the explored pseudo-labels can be adopted to guide the optimization of aerial object detector in a closed-looping progressive fashion. Comprehensive evaluations on two public datasets demonstrate the superiority of our PECL when compared with other state-of-the-art methods in the sparsely annotated aerial object detection task.

Assume 6-adjacency for FG and 26-adjacent for BG.

The AGL task is associated with two important challenges.

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The cross-view matching task is extremely challenging due to drastic appearance and geometry differences across views.

As a fundamental building block for visual analysis across aerial images, scene text etc., rotated object detection has recently been developed rapidly [

Multi-temporal resolution: Sentinel-2 provides a consistent yearly time series. Sentinel-2 sensor acquires images at 10, 20 and 60 m spatial resolutions. T able 2: Provided cloud and snow masks. Note that cloudy dates are not suppressed from the time series. The spatial size of Sentinel-2 time series has been empirically determined and set to 40.

Information (IGN) that provides a unique and rich resource for large-scale geospa-tial analysis.

The ability to detect aerial objects with limited annotation is pivotal to the development of real-world aerial intelligence systems. In this work, we focus on a demanding but practical sparsely annotated object detection (SAOD) in aerial images, which encompasses a wider variety of aerial scenes with the same number of annotated objects.