In recent years, the application of traditional deep learning methods in the agricultural field using remote sensing techniques, such as crop area and growth monitoring, crop classification, and agricultural disaster monitoring, has been greatly facilitated by advancements in deep learning. The accuracy of image classification plays a crucial role in these applications. Although traditional deep learning methods have achieved significant success in remote sensing image classification, they often involve convolutional neural networks with a large number of parameters that require extensive optimization using numerous remote sensing images for training purposes. To address these challenges, we propose a novel approach called multiscale attention network (MAN) for sample‐based remote sensing image classification. This method consists primarily of feature extractors and attention modules to effectively utilize different scale features through multiscale feature training during the training phase. We evaluate our proposed method on three datasets comprising agricultural remote sensing images and observe superior performance compared to existing approaches. Furthermore, we validate its generalizability by testing it on an oil well indicator diagram specifically designed for classification tasks.

中文翻译：

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少样本图像分类的多尺度注意力

近年来，深度学习的进步极大促进了传统深度学习方法在利用遥感技术的农业领域的应用，如作物面积和长势监测、作物分类、农业灾害监测等。图像分类的准确性在这些应用中起着至关重要的作用。尽管传统的深度学习方法在遥感图像分类方面取得了显着的成功，但它们通常涉及具有大量参数的卷积神经网络，需要使用大量遥感图像进行广泛的优化以进行训练。为了应对这些挑战，我们提出了一种称为多尺度注意网络（MAN）的新方法，用于基于样本的遥感图像分类。该方法主要由特征提取器和注意模块组成，在训练阶段通过多尺度特征训练有效地利用不同尺度的特征。我们在包含农业遥感图像的三个数据集上评估了我们提出的方法，并观察到与现有方法相比具有优越的性能。此外，我们通过在专门为分类任务设计的油井指示图上进行测试来验证其普遍性。