Details
| Original language | English |
|---|---|
| Pages (from-to) | 271-279 |
| Number of pages | 9 |
| Journal | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Volume | 5 |
| Issue number | 3 |
| Publication status | Published - 17 May 2022 |
| Event | 2022 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission III - Nice, France Duration: 6 Jun 2022 → 11 Jun 2022 |
Abstract
With the availability of large amounts of satellite image time series (SITS), the identification of different materials of the Earth's surface is possible with a high temporal resolution. One of the basic tasks is the pixel-wise classification of land cover, i.e.The task of identifying the physical material of the Earth's surface in an image. Fully convolutional neural networks (FCN) are successfully used for this task. In this paper, we investigate different FCN variants, using different methods for the computation of spatial, spectral, and temporal features. We investigate the impact of 3D convolutions in the spatial-Temporal as well as in the spatial-spectral dimensions in comparison to 2D convolutions in the spatial dimensions only. Additionally, we introduce a new method to generate multitemporal input patches by using time intervals instead of fixed acquisition dates. We then choose the image that is closest in time to the middle of the corresponding time interval, which makes our approach more flexible with respect to the requirements for the acquisition of new data. Using these multi-Temporal input patches, generated from Sentinel-2 images, we improve the classification of land cover by 4% in the mean F1-score and 1.3% in the overall accuracy compared to a classification using mono-Temporal input patches. Furthermore, the usage of 3D convolutions instead of 2D convolutions improves the classification performance by a small amount of 0.4% in the mean F1-score and 1.2% in the overall accuracy.
Keywords
- 3D-CNN, FCN, land cover classification, Multi-Temporal images, Remote sensing
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. 5, No. 3, 17.05.2022, p. 271-279.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Investigating 2d and 3d convolutions for multitemporal land cover classification using remote sensing images
AU - Voelsen, M.
AU - Teimouri, M.
AU - Rottensteiner, F.
AU - Heipke, C.
PY - 2022/5/17
Y1 - 2022/5/17
N2 - With the availability of large amounts of satellite image time series (SITS), the identification of different materials of the Earth's surface is possible with a high temporal resolution. One of the basic tasks is the pixel-wise classification of land cover, i.e.The task of identifying the physical material of the Earth's surface in an image. Fully convolutional neural networks (FCN) are successfully used for this task. In this paper, we investigate different FCN variants, using different methods for the computation of spatial, spectral, and temporal features. We investigate the impact of 3D convolutions in the spatial-Temporal as well as in the spatial-spectral dimensions in comparison to 2D convolutions in the spatial dimensions only. Additionally, we introduce a new method to generate multitemporal input patches by using time intervals instead of fixed acquisition dates. We then choose the image that is closest in time to the middle of the corresponding time interval, which makes our approach more flexible with respect to the requirements for the acquisition of new data. Using these multi-Temporal input patches, generated from Sentinel-2 images, we improve the classification of land cover by 4% in the mean F1-score and 1.3% in the overall accuracy compared to a classification using mono-Temporal input patches. Furthermore, the usage of 3D convolutions instead of 2D convolutions improves the classification performance by a small amount of 0.4% in the mean F1-score and 1.2% in the overall accuracy.
AB - With the availability of large amounts of satellite image time series (SITS), the identification of different materials of the Earth's surface is possible with a high temporal resolution. One of the basic tasks is the pixel-wise classification of land cover, i.e.The task of identifying the physical material of the Earth's surface in an image. Fully convolutional neural networks (FCN) are successfully used for this task. In this paper, we investigate different FCN variants, using different methods for the computation of spatial, spectral, and temporal features. We investigate the impact of 3D convolutions in the spatial-Temporal as well as in the spatial-spectral dimensions in comparison to 2D convolutions in the spatial dimensions only. Additionally, we introduce a new method to generate multitemporal input patches by using time intervals instead of fixed acquisition dates. We then choose the image that is closest in time to the middle of the corresponding time interval, which makes our approach more flexible with respect to the requirements for the acquisition of new data. Using these multi-Temporal input patches, generated from Sentinel-2 images, we improve the classification of land cover by 4% in the mean F1-score and 1.3% in the overall accuracy compared to a classification using mono-Temporal input patches. Furthermore, the usage of 3D convolutions instead of 2D convolutions improves the classification performance by a small amount of 0.4% in the mean F1-score and 1.2% in the overall accuracy.
KW - 3D-CNN
KW - FCN
KW - land cover classification
KW - Multi-Temporal images
KW - Remote sensing
UR - http://www.scopus.com/inward/record.url?scp=85132043839&partnerID=8YFLogxK
U2 - 10.5194/isprs-Annals-V-3-2022-271-2022
DO - 10.5194/isprs-Annals-V-3-2022-271-2022
M3 - Conference article
AN - SCOPUS:85132043839
VL - 5
SP - 271
EP - 279
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 - 3
T2 - 2022 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission III
Y2 - 6 June 2022 through 11 June 2022
ER -