Uncertainty Analysis of Object-Based Land-Cover Classification Using Sentinel-2 Time-Series Data

Lei Ma,Xiaoxiang Zhu,Michael Schmitt

doi:10.3390/rs12223798

Abstract

Recently, time-series from optical satellite data have been frequently used in object-based land-cover classification. This poses a significant challenge to object-based image analysis (OBIA) owing to the presence of complex spatio-temporal information in the time-series data. This study evaluates object-based land-cover classification in the northern suburbs of Munich using time-series from optical Sentinel data. Using a random forest classifier as the backbone, experiments were designed to analyze the impact of the segmentation scale, features (including spectral and temporal features), categories, frequency, and acquisition timing of optical satellite images. Based on our analyses, the following findings are reported: (1) Optical Sentinel images acquired over four seasons can make a significant contribution to the classification of agricultural areas, even though this contribution varies between spectral bands for the same period. (2) The use of time-series data alleviates the issue of identifying the “optimal” segmentation scale. The finding of this study can provide a more comprehensive understanding of the effects of classification uncertainty on object-based dense multi-temporal image classification.

Highlights

There has been a progressive increase in the availability of open-source remote-sensing data (e.g., Landsat and Sentinel imagery)
They confirmed that the dynamic time warping (DTW) framework, representative of the first paradigm as it only uses enhanced normalized difference vegetation index (NDVI) time-series, is not superior to the random forests (RF) framework, which is representative of the second paradigm as it uses all of the features of individual spectral bands
We note that the results of this study are not consistent with those presented by Stromann et al [28] because they argue that dimensionality reduction should be a key step in land-cover classification using support vector machines (SVM); this discrepancy can be attributed to the usage of the sensitive SVM

Summary

Introduction

There has been a progressive increase in the availability of open-source remote-sensing data (e.g., Landsat and Sentinel imagery). This allows the application of satellite image time-series (SITS) data in remote sensing-based land-cover classification [1,2,3,4,5,6]. Belgiu et al [12] compared the performance of both time-series classification paradigms using the DTW method and an RF classifier. They confirmed that the DTW framework, representative of the first paradigm as it only uses enhanced normalized difference vegetation index (NDVI) time-series, is not superior to the RF framework, which is representative of the second paradigm as it uses all of the features of individual spectral bands. In this study, the RF classifier was selected for an uncertainty analysis of object-based classification using SITS

Objectives

Methods

Results

Conclusion