Existing scene classification methods allocate the same computational resources, i.e., all model parameters in the neural network, to each remote sensing image whenever from any geographic scene. However, this might be redundant for images of certain scenes that are easy to discriminate, e.g., homogeneous scenes. This observation motivates us to propose an efficient method for on-orbit scene classification, namely, the Scale Boosting Model (SBM). Specifically, during the training process, the SBM is built as a set of different scale learners in a scale-increasing manner, each of which is used to learn and classify image features at a specific scale. During inference, the scale learners in the SBM will be selectively run in a scale-increasing manner and automatically decide when to exit early or expand the computation according to the scene complexity. In addition, by replacing the backbone of the scale learner, the SBM could provide a deployment possibility for computationally limited models for on-orbit processing, thereby reducing their computational requirements. Extensive experiments on UC Merced Land Use, NWPU-RESISC45 and RSD46-WHU datasets show that the SBM achieved a more effective classification performance more efficiently.

中文翻译：

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使用缩放提升模型进行在轨场景分类的动态推理


现有的场景分类方法无论何时来自任何地理场景，都会为每张遥感影像分配相同的计算资源，即神经网络中的所有模型参数。但是，对于某些易于区分的场景的图像，例如同质场景，这可能是多余的。这一观察结果促使我们提出了一种有效的在轨场景分类方法，即 Scale Boosting Model （SBM）。具体来说，在训练过程中，SBM 以增加比例的方式构建为一组不同比例的学习者，每个学习者都用于在特定比例下学习和分类图像特征。在推理过程中，SBM 中的缩放学习器将有选择地以增加缩放的方式运行，并根据场景复杂性自动决定何时提前退出或扩展计算。此外，通过替换尺度学习器的主干，SBM 可以为计算受限的模型提供部署可能性，以进行在轨处理，从而降低它们的计算要求。对加州大学默塞德分校土地利用、NWPU-RESISC45 和 RSD46-WHU 数据集的广泛实验表明，SBM 更有效地实现了更有效的分类性能。