Self-Supervised Learning of Satellite-Derived Vegetation Indices for Clustering and Visualization of Vegetation Types.
Self-Supervised Learning of Satellite-Derived Vegetation Indices for Clustering and Visualization of Vegetation Types.
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DOI：
10.3390/jimaging7020030
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发表时间：
2021-02-08
影响因子：
3.2
通讯作者：
Hara K
中科院分区：
文献类型：
--
作者：
Sharma RC;Hara K
植被指数是检索植被的生物物理和化学属性的常用技术。本文提出了一种自动编码器（AE）和卷积自动编码器（CAE）的自监督学习方法的去相关和降维的高维植被指数来自卫星观测的潜力。这项研究是在Mt。Zao及其基地位于日本东北部，气候冷温带，通过收集2018年属于16种植被类型（包括一些非植被类）的地面实况点。通过处理2017年至2019年研究区域的所有Sentinel-2场景，生成了16个植被指数的月度中位数复合数据。通过计算基于自助重采样的置信区间，对基于AE和CAE的压缩图像的植被类型聚类和可视化性能进行了定量评估。与经典方法相比，具有三个特征的基于AE和基于CAE的压缩图像的置信区间分别提高了约4%和9%。使用卷积神经网络的CAE表现出比AE更好的特征提取和降维能力。类的性能分析也显示了CAE的优越性。这项研究突出了潜在的AE和CAE实现一个很好的聚类和可视化的植被类型。
Vegetation indices are commonly used techniques for the retrieval of biophysical and chemical attributes of vegetation. This paper presents the potential of an Autoencoders (AEs) and Convolutional Autoencoders (CAEs)-based self-supervised learning approach for the decorrelation and dimensionality reduction of high-dimensional vegetation indices derived from satellite observations. This research was implemented in Mt. Zao and its base in northeast Japan with a cool temperate climate by collecting the ground truth points belonging to 16 vegetation types (including some non-vegetation classes) in 2018. Monthly median composites of 16 vegetation indices were generated by processing all Sentinel-2 scenes available for the study area from 2017 to 2019. The performance of AEs and CAEs-based compressed images for the clustering and visualization of vegetation types was quantitatively assessed by computing the bootstrap resampling-based confidence interval. The AEs and CAEs-based compressed images with three features showed around 4% and 9% improvements in the confidence intervals respectively over the classical method. CAEs using convolutional neural networks showed better feature extraction and dimensionality reduction capacity than the AEs. The class-wise performance analysis also showed the superiority of the CAEs. This research highlights the potential of AEs and CAEs for attaining a fine clustering and visualization of vegetation types.