Details
| Original language | English |
|---|---|
| Pages (from-to) | 961-970 |
| Number of pages | 10 |
| Journal | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Volume | 10 |
| Issue number | 1 |
| Publication status | Published - 5 Dec 2023 |
| Event | ISPRS Geospatial Week 2023 - Kairo, Egypt Duration: 2 Sept 2023 → 7 Sept 2023 |
Abstract
Deforestation is considered one of the main causes of global warming and biodiversity reduction. Therefore, early detection of deforestation processes is of paramount importance to preserve environmental resources. Currently, there is plenty of research focused on detecting deforestation from satellite imagery using Convolutional Neural Networks (CNNs). Although these works yield remarkable results, most of them employ pairs of images and detect changes which occurred between the two image acquisition epochs only. Furthermore, these models tend to produce poor results when applied to new data in real-world scenarios. In this regard, an interesting research topic deals with the generalization capacity of the classifiers. CNN-based approaches combined with time series data can be a suitable framework to obtain classifiers that generalize better to new data. Image time series contain complementary information, representing different imaging conditions over time. This work addresses the transferability for detecting deforestation in different areas of the Amazon region, using Sentinel-2 time series and reference maps from PRODES project, which are not required to be synchronized. The results indicate that the classifier with time series data brings a substantial improvement in accuracy by taking advantage of the temporal information.
Keywords
- Convolutional neural networks, Deforestation detection, Time series, Transferability
ASJC Scopus subject areas
- Physics and Astronomy(all)
- Instrumentation
- Environmental Science(all)
- Environmental Science (miscellaneous)
- Earth and Planetary Sciences(all)
- Earth and Planetary Sciences (miscellaneous)
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In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 10, No. 1, 05.12.2023, p. 961-970.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Unsing Time Series Image Data To Improve The Generalization Capabilities Of A CNN
T2 - ISPRS Geospatial Week 2023
AU - Ortega, M. X.
AU - Wittich, D.
AU - Rottensteiner, F.
AU - Heipke, C.
AU - Feitosa, R. Q.
N1 - Funding Information: This work was conducted in the scope of ForstCARe project, supported by and the Federal Ministry for Economic Affairs and Climate Action, Germany (Bundesministerium fur Wirtschaft und Klimaschutz, Funding codes 50EE2017A and 50EE2017B). The authors would also like to acknowledge the support provided by the DAAD (Deutscher Akademischer Austauschdienst) through the funding program 57588368.
PY - 2023/12/5
Y1 - 2023/12/5
N2 - Deforestation is considered one of the main causes of global warming and biodiversity reduction. Therefore, early detection of deforestation processes is of paramount importance to preserve environmental resources. Currently, there is plenty of research focused on detecting deforestation from satellite imagery using Convolutional Neural Networks (CNNs). Although these works yield remarkable results, most of them employ pairs of images and detect changes which occurred between the two image acquisition epochs only. Furthermore, these models tend to produce poor results when applied to new data in real-world scenarios. In this regard, an interesting research topic deals with the generalization capacity of the classifiers. CNN-based approaches combined with time series data can be a suitable framework to obtain classifiers that generalize better to new data. Image time series contain complementary information, representing different imaging conditions over time. This work addresses the transferability for detecting deforestation in different areas of the Amazon region, using Sentinel-2 time series and reference maps from PRODES project, which are not required to be synchronized. The results indicate that the classifier with time series data brings a substantial improvement in accuracy by taking advantage of the temporal information.
AB - Deforestation is considered one of the main causes of global warming and biodiversity reduction. Therefore, early detection of deforestation processes is of paramount importance to preserve environmental resources. Currently, there is plenty of research focused on detecting deforestation from satellite imagery using Convolutional Neural Networks (CNNs). Although these works yield remarkable results, most of them employ pairs of images and detect changes which occurred between the two image acquisition epochs only. Furthermore, these models tend to produce poor results when applied to new data in real-world scenarios. In this regard, an interesting research topic deals with the generalization capacity of the classifiers. CNN-based approaches combined with time series data can be a suitable framework to obtain classifiers that generalize better to new data. Image time series contain complementary information, representing different imaging conditions over time. This work addresses the transferability for detecting deforestation in different areas of the Amazon region, using Sentinel-2 time series and reference maps from PRODES project, which are not required to be synchronized. The results indicate that the classifier with time series data brings a substantial improvement in accuracy by taking advantage of the temporal information.
KW - Convolutional neural networks
KW - Deforestation detection
KW - Time series
KW - Transferability
UR - http://www.scopus.com/inward/record.url?scp=85182995319&partnerID=8YFLogxK
U2 - 10.5194/isprs-annals-X-1-W1-2023-961-2023
DO - 10.5194/isprs-annals-X-1-W1-2023-961-2023
M3 - Conference article
AN - SCOPUS:85182995319
VL - 10
SP - 961
EP - 970
JO - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
JF - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
SN - 2194-9042
IS - 1
Y2 - 2 September 2023 through 7 September 2023
ER -