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Crop Type Classification Using Fusion of Sentinel-1 and Sentinel-2 Data: Assessing the Impact of Feature Selection, Optical Data Availability, and Parcel Sizes on the Accuracies

Orynbaikyzy, Aiym und Gessner, Ursula und Mack, Benjamin und Conrad, Christopher (2020) Crop Type Classification Using Fusion of Sentinel-1 and Sentinel-2 Data: Assessing the Impact of Feature Selection, Optical Data Availability, and Parcel Sizes on the Accuracies. Remote Sensing, 12 (2779), Seiten 1-24. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/rs12172779. ISSN 2072-4292.

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Offizielle URL: https://www.mdpi.com/2072-4292/12/17/2779

Kurzfassung

Crop type classification using Earth Observation (EO) data is challenging, particularly for crop types with similar phenological growth stages. In this regard, the synergy of optical and Synthetic-Aperture Radar (SAR) data enables a broad representation of biophysical and structural information on target objects, enhancing crop type mapping. However, the fusion of multi-sensor dense time-series data often comes with the challenge of high dimensional feature space. In this study, we (1) evaluate how the usage of only optical, only SAR, and their fusion affect the classification accuracy; (2) identify the combination of which time-steps and feature-sets lead to peak accuracy; (3) analyze misclassifications based on the parcel size, optical data availability, and crops’ temporal profiles. Two fusion approaches were considered and compared in this study: feature stacking and decision fusion. To distinguish the most relevant feature subsets time- and variable-wise, grouped forward feature selection (gFFS) was used. gFFS allows focusing analysis and interpretation on feature sets of interest like spectral bands, vegetation indices (VIs), or data sensing time rather than on single features. This feature selection strategy leads to better interpretability of results while substantially reducing computational expenses. The results showed that, in contrast to most other studies, SAR datasets outperform optical datasets. Similar to most other studies, the optical-SAR combination outperformed single sensor predictions. No significant difference was recorded between feature stacking and decision fusion. Random Forest (RF) appears to be robust to high feature space dimensionality. The feature selection did not improve the accuracies even for the optical-SAR feature stack with 320 features. Nevertheless, the combination of RF feature importance and time- and variable-wise gFFS rankings in one visualization enhances interpretability and understanding of the features’ relevance for specific classification tasks. For example, by enabling the identification of features that have high RF feature importance values but are, in their information content, correlated with other features. This study contributes to the growing domain of interpretable machine learning.

elib-URL des Eintrags:https://elib.dlr.de/135845/
Dokumentart:Zeitschriftenbeitrag
Titel:Crop Type Classification Using Fusion of Sentinel-1 and Sentinel-2 Data: Assessing the Impact of Feature Selection, Optical Data Availability, and Parcel Sizes on the Accuracies
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Orynbaikyzy, AiymAiym.Orynbaikyzy (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Gessner, Ursulaursula.gessner (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Mack, Benjaminben8mack (at) gmail.comNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Conrad, Christopherchristopher.conrad (at) geo.uni-halle.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:27 August 2020
Erschienen in:Remote Sensing
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Ja
In SCOPUS:Ja
In ISI Web of Science:Ja
Band:12
DOI:10.3390/rs12172779
Seitenbereich:Seiten 1-24
Verlag:Multidisciplinary Digital Publishing Institute (MDPI)
Name der Reihe:Digital Agriculture with Remote Sensing
ISSN:2072-4292
Status:veröffentlicht
Stichwörter:optical-SAR synergy; crop mapping; group-wise forward feature selection; interpretable machine learning; decision fusion; feature stacking
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Erdbeobachtung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):R - Fernerkundung u. Geoforschung
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Deutsches Fernerkundungsdatenzentrum > Dynamik der Landoberfläche
Hinterlegt von: Orynbaikyzy, Aiym
Hinterlegt am:14 Sep 2020 10:12
Letzte Änderung:25 Okt 2023 08:43

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  • Crop Type Classification Using Fusion of Sentinel-1 and Sentinel-2 Data: Assessing the Impact of Feature Selection, Optical Data Availability, and Parcel Sizes on the Accuracies. (deposited 14 Sep 2020 10:12) [Gegenwärtig angezeigt]

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