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An Adaptive Multiview Active Learning Approach for Spectral-Spatial Classification of Hyperspectral Images
IEEE Transactions on Geoscience and Remote Sensing ( IF 8.2 ) Pub Date : 2020-04-01 , DOI: 10.1109/tgrs.2019.2952319
Zhou Zhang , Edoardo Pasolli , Melba M. Crawford

Combining spectral and spatial features in hyperspectral image classification is a common practice due to the improvements in classification accuracy that can be obtained by extracting information from neighboring pixels. However, the resulting high dimensionality of the input data and the typically limited number of labeled samples are two key challenges that affect the overall performance of supervised classification methods. To alleviate these two issues, we propose an adaptive multiview (MV)-based active learning (AL) approach that is different from the existing MV AL methods in two main ways: 1) to improve the view sufficiency, a spectral–spatial view generation approach is proposed by incorporating spatial features derived from the segmentation maps into each view and 2) to increase the diversity across views, a dynamic view is generated at each AL iteration by selecting important features from the predefined views. The performance of each view is further improved by applying the proposed AL algorithm in conjunction with an ensemble approach as back-end classifier, a scenario less explored in the remote sensing community than single classifier-based AL methodologies. The proposed approach is applied to three widely analyzed hyperspectral data sets [i.e., Kennedy Space Center (KSC), Indian Pine, and University of Houston (UH)], and the results demonstrate the efficacy of the proposed method compared with other state-of-the-art AL classification methods.

中文翻译:

一种用于高光谱图像光谱空间分类的自适应多视图主动学习方法

由于可以通过从相邻像素中提取信息来提高分类精度,因此在高光谱图像分类中结合光谱和空间特征是一种常见的做法。然而,由此产生的输入数据的高维度和通常有限的标记样本数量是影响监督分类方法整体性能的两个关键挑战。为了缓解这两个问题,我们提出了一种基于自适应多视图 (MV) 的主动学习 (AL) 方法,该方法在两个主要方面不同于现有的 MV AL 方法:1) 提高视图充分性,光谱-空间视图生成提出的方法是将来自分割图的空间特征合并到每个视图中,以及 2) 增加视图之间的多样性,通过从预定义视图中选择重要特征,在每次 AL 迭代时生成动态视图。通过将所提出的 AL 算法与作为后端分类器的集成方法相结合,每个视图的性能得到进一步提高,与基于单个分类器的 AL 方法相比,在遥感社区中探索的场景较少。所提出的方法被应用于三个广泛分析的高光谱数据集 [即肯尼迪航天中心 (KSC)、印度松和休斯顿大学 (UH)],结果证明了所提出方法与其他状态相比的有效性- 最先进的 AL 分类方法。通过将所提出的 AL 算法与作为后端分类器的集成方法相结合,每个视图的性能得到进一步提高,与基于单个分类器的 AL 方法相比,在遥感社区中探索的场景较少。所提出的方法应用于三个广泛分析的高光谱数据集 [即肯尼迪航天中心 (KSC)、印度松和休斯顿大学 (UH)],结果证明了所提出方法与其他状态的比较的有效性- 最先进的 AL 分类方法。通过将所提出的 AL 算法与作为后端分类器的集成方法相结合,每个视图的性能得到进一步提高,与基于单个分类器的 AL 方法相比,在遥感社区中探索的场景较少。所提出的方法应用于三个广泛分析的高光谱数据集 [即肯尼迪航天中心 (KSC)、印度松和休斯顿大学 (UH)],结果证明了所提出方法与其他状态的比较的有效性- 最先进的 AL 分类方法。
更新日期:2020-04-01
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