论文标题
高光谱图像分析深度学习的进步 - 在实用成像方案中引起的挑战
Advances in Deep Learning for Hyperspectral Image Analysis--Addressing Challenges Arising in Practical Imaging Scenarios
论文作者
论文摘要
事实证明，深度神经网络对计算机视觉任务非常有效，例如图像分类，对象检测和语义细分 - 这些主要用于彩色图像和视频。近年来，将深度学习算法应用于遥感和生物医学任务的高光谱和多光谱图像。这些多通道图像带有自己独特的挑战集，必须解决有效的图像分析。挑战包括有限的地面真相（注释昂贵，广泛的标记通常不可行），尽管数据的高维质（每个像素都由数百个频谱频段表示），尽管有大量未标记的数据和利用相同场景的多个传感器/来源的潜力。在本章中，我们将回顾社区中的最新进展，尽管这些独特的挑战，但仍将深度学习用于强大的高光谱图像分析 - 具体来说，我们将审查无监督，半监督和积极的学习方法，以进行图像分析，以及多种源的转移学习方法（例如，多发音，多态，或多动态）图像分析。
Deep neural networks have proven to be very effective for computer vision tasks, such as image classification, object detection, and semantic segmentation -- these are primarily applied to color imagery and video. In recent years, there has been an emergence of deep learning algorithms being applied to hyperspectral and multispectral imagery for remote sensing and biomedicine tasks. These multi-channel images come with their own unique set of challenges that must be addressed for effective image analysis. Challenges include limited ground truth (annotation is expensive and extensive labeling is often not feasible), and high dimensional nature of the data (each pixel is represented by hundreds of spectral bands), despite being presented by a large amount of unlabeled data and the potential to leverage multiple sensors/sources that observe the same scene. In this chapter, we will review recent advances in the community that leverage deep learning for robust hyperspectral image analysis despite these unique challenges -- specifically, we will review unsupervised, semi-supervised and active learning approaches to image analysis, as well as transfer learning approaches for multi-source (e.g. multi-sensor, or multi-temporal) image analysis.
