Multi-Granularity Domain-Adaptive Teacher for Unsupervised Remote Sensing Object Detection - Fang Fang; Jianing Kang; Shengwen Li; Panpan Tian; Yang Liu; Chaoliang Luo; Shunping Zhou (Remote Sensing)
Object detection in remote sensing images (RSIs) is pivotal for various tasks such as natural disaster warning, environmental monitoring, teacher–student urban planning. Object detection methods based on domain adaptation have emerged, which effectively decrease the dependence on annotated samples, making significant advances in unsupervised scenarios. However, these methods fall short in their ability to learn remote sensing object features of the target domain, thus limiting the detection capabilities in many complex scenarios. To fill this gap, this paper integrates a multi-granularity feature alignment strategy and the teacher–student framework to enhance the capability of detecting remote sensing objects, and proposes a multi-granularity domain-adaptive teacher (MGDAT) framework to better bridge the feature gap across target and source domain data. MGDAT incorporates the teacher–student framework at three granularities, including pixel-, image- and instance-level feature alignment. Extensive experiments show that MGDAT surpasses SOTA baselines in detection accuracy, and exhibits great generalizability. This proposed method can serve as a methodology reference for various unsupervised interpretation tasks of RSIs.