Abstract. Satellite remote sensing has gained a key role for vegetation mapping distribution. Given the availability of multi-temporal satellite data, seasonal variations in vegetation dynamics can be used trough time series analysis for vegetation distribution mapping. These types of data have a very high variability within them and are subjected by artifacts. Therefore, a pre-processing phase must be performed to properly detect outliers, for data smoothing process and to correctly interpolate the data. In this work, we compare four pre-processing approaches for functional analysis on 4-years of remotely sensed images, resulting in four time series datasets. The methodologies presented are the results of the combination of two outlier detection methods, namely tsclean and boxplot functions in R and two discrete data smoothing approaches (Generalized Additive Model ”GAM” on daily and aggregated data). The approaches proposed are: tsclean-GAM on aggregated data (M01), boxplot-GAM on aggregated data (M02), tsclean-GAM on daily data (M03), boxplot-GAM on daily data (M04). Our results prove that the approach which involves tsclean function and GAM applied to daily data (M03) is ameliorative to the logic of the procedure and leads to better model performance in terms of Overall Accuracy (OA) which is always among the highest when compared with the others obtained from the other three different approaches.

A comparison of pre-processing approaches for remotely sensed time series classification based on functional analysis / Balestra, Mattia; Pierdicca, Roberto; Cesaretti, L.; Quattrini, G.; Mancini, A.; Galli, A.; Savina MALINVERNI, Eva; Casavecchia, S.; Pesaresi, S.. - X-1/W1-2023:(2023), pp. 33-40. (Intervento presentato al convegno ISPRS Geospatial Week 2023 tenutosi a Il Cairo, Egypt) [10.5194/isprs-annals-x-1-w1-2023-33-2023].

A comparison of pre-processing approaches for remotely sensed time series classification based on functional analysis

L. Cesaretti;
2023

Abstract

Abstract. Satellite remote sensing has gained a key role for vegetation mapping distribution. Given the availability of multi-temporal satellite data, seasonal variations in vegetation dynamics can be used trough time series analysis for vegetation distribution mapping. These types of data have a very high variability within them and are subjected by artifacts. Therefore, a pre-processing phase must be performed to properly detect outliers, for data smoothing process and to correctly interpolate the data. In this work, we compare four pre-processing approaches for functional analysis on 4-years of remotely sensed images, resulting in four time series datasets. The methodologies presented are the results of the combination of two outlier detection methods, namely tsclean and boxplot functions in R and two discrete data smoothing approaches (Generalized Additive Model ”GAM” on daily and aggregated data). The approaches proposed are: tsclean-GAM on aggregated data (M01), boxplot-GAM on aggregated data (M02), tsclean-GAM on daily data (M03), boxplot-GAM on daily data (M04). Our results prove that the approach which involves tsclean function and GAM applied to daily data (M03) is ameliorative to the logic of the procedure and leads to better model performance in terms of Overall Accuracy (OA) which is always among the highest when compared with the others obtained from the other three different approaches.
2023
ISPRS Geospatial Week 2023
time-series; data rpe-processing; outliers detection; generalized additive model (GAM); functional ripncipal component analysis (FPCA)
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
A comparison of pre-processing approaches for remotely sensed time series classification based on functional analysis / Balestra, Mattia; Pierdicca, Roberto; Cesaretti, L.; Quattrini, G.; Mancini, A.; Galli, A.; Savina MALINVERNI, Eva; Casavecchia, S.; Pesaresi, S.. - X-1/W1-2023:(2023), pp. 33-40. (Intervento presentato al convegno ISPRS Geospatial Week 2023 tenutosi a Il Cairo, Egypt) [10.5194/isprs-annals-x-1-w1-2023-33-2023].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1697652
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