论文标题
具有超叶复合物值模型的多光谱图像分类
Multi-Spectral Image Classification with Ultra-Lean Complex-Valued Models
论文作者
论文摘要
多光谱图像对于遥感，由于材料在灰度和RGB图像中通常相同的材料表现出不同的光谱特征，这对于遥感而言是无价的。与现代深度学习方法相结合，这种方式在各种遥感应用程序中具有巨大的潜在效用，例如人道主义援助和灾难恢复工作。最先进的深度学习方法从ImageNet等大规模注释中受益匪浅，但现有的MSI图像数据集在类似规模的情况下缺乏注释。作为在很少注释的情况下传输此类数据学习的一种替代方法，我们应用了复杂值的共域对称模型来对实现的MSI图像进行分类。我们在8频段XView数据上进行的实验表明，我们从头开始训练的超lean模型，而没有数据增强，可以超越数据增强和XView上的修改转移学习。我们的工作是第一个证明对实值MSI数据的复杂价值深度学习的价值。
Multi-spectral imagery is invaluable for remote sensing due to different spectral signatures exhibited by materials that often appear identical in greyscale and RGB imagery. Paired with modern deep learning methods, this modality has great potential utility in a variety of remote sensing applications, such as humanitarian assistance and disaster recovery efforts. State-of-the-art deep learning methods have greatly benefited from large-scale annotations like in ImageNet, but existing MSI image datasets lack annotations at a similar scale. As an alternative to transfer learning on such data with few annotations, we apply complex-valued co-domain symmetric models to classify real-valued MSI images. Our experiments on 8-band xView data show that our ultra-lean model trained on xView from scratch without data augmentations can outperform ResNet with data augmentation and modified transfer learning on xView. Our work is the first to demonstrate the value of complex-valued deep learning on real-valued MSI data.
