Self-Supervised Learning of Satellite-Derived Vegetation Indices for Clustering and Visualization of Vegetation Types

Ram C Sharma,Keitarou Hara

doi:10.3390/jimaging7020030

Abstract

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.

Highlights

Vegetation is an integral component of life, and identification and classification of vegetation types provides valuable information for understanding the distribution and dynamics of vegetation as for environmental changes
The major objective of this paper is to present 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
Dimensionality reduction of high-dimensional vegetation indices is a relevant technique, while a large number of vegetation indices exist in the literature

Summary

Introduction

Vegetation is an integral component of life, and identification and classification of vegetation types provides valuable information for understanding the distribution and dynamics of vegetation as for environmental changes. Spectral reflectance measured from remote sensing platforms provides crucial information on identification and discrimination of vegetation types. A large number of vegetation indices exist in the literature, and large numbers of input variables complicate modelling and prediction, and impairs accuracy, known as the “curse of dimensionality” [6,7]. To cope with this problem, dimensionality reduction techniques, which transform high-dimensional dataset into lower-dimensional representations have been proposed [8,9]

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Discussion

Conclusion