Advances in Hyperspectral Image Classification
Advances in Hyperspectral Image Classification
复制标题
DOI：
10.1109/msp.2013.2279179
复制
发表时间：
2014-01-01
影响因子：
14.9
通讯作者：
Benediktsson, Jon Atli
中科院分区：
文献类型：
--
作者：
Camps-Valls, Gustavo;Tuia, Devis;Benediktsson, Jon Atli
在过去的几十年里，光学传感器的技术发展为遥感分析提供了丰富的空间，光谱和时间信息。特别是，高光谱图像（HSI）和红外探测器的光谱分辨率的增加打开了新的应用领域的大门，并提出了新的数据分析方法的挑战。HSI允许表征感兴趣的对象（例如，土地覆被类别），并保持库存最新。光谱分辨率的提高要求信号处理和开发算法的进步。本文重点介绍了高光谱图像分类的挑战性问题，该问题最近越来越受欢迎，并吸引了其他科学学科的兴趣，如机器学习，图像处理和计算机视觉。在遥感领域，术语分类用于表示将单个像素分配给一组类别的过程，而术语分割用于将像素聚合成对象然后分配给类别的方法。
The technological evolution of optical sensors over the last few decades has provided remote sensing analysts with rich spatial, spectral, and temporal information. In particular, the increase in spectral resolution of hyperspectral images (HSIs) and infrared sounders opens the doors to new application domains and poses new methodological challenges in data analysis. HSIs allow the characterization of objects of interest (e.g., land-cover classes) with unprecedented accuracy, and keeps inventories up to date. Improvements in spectral resolution have called for advances in signal processing and exploitation algorithms. This article focuses on the challenging problem of hyperspectral image classification, which has recently gained in popularity and attracted the interest of other scientific disciplines such as machine learning, image processing, and computer vision. In the remote sensing community, the term classification is used to denote the process that assigns single pixels to a set of classes, while the term segmentation is used for methods aggregating pixels into objects and then assigned to a class.