Deep Learning for Mapping Tropical Forests with TanDEM-X Bistatic InSAR Data
The TanDEM-X synthetic aperture radar (SAR) system allows for the recording of bistatic interferometric SAR (InSAR) acquisitions, which provide additional information to the common amplitude images acquired by monostatic SAR systems. More concretely, the volume decorrelation factor, which can be der...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-08-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/16/3981 |
| _version_ | 1827625610243997696 |
|---|---|
| author | Jose-Luis Bueso-Bello Daniel Carcereri Michele Martone Carolina González Philipp Posovszky Paola Rizzoli |
| author_facet | Jose-Luis Bueso-Bello Daniel Carcereri Michele Martone Carolina González Philipp Posovszky Paola Rizzoli |
| author_sort | Jose-Luis Bueso-Bello |
| collection | DOAJ |
| description | The TanDEM-X synthetic aperture radar (SAR) system allows for the recording of bistatic interferometric SAR (InSAR) acquisitions, which provide additional information to the common amplitude images acquired by monostatic SAR systems. More concretely, the volume decorrelation factor, which can be derived from the bistatic interferometric coherence, is a reliable indicator of the presence of vegetation and it was used as main input feature for the generation of the global TanDEM-X forest/non-forest map, by means of a clustering algorithm. In this work, we investigate the capabilities of deep Convolutional Neural Networks (CNNs) for mapping tropical forests at large-scale using TanDEM-X InSAR data. For this purpose, we rely on a U-Net architecture, which takes as input a set of feature maps selected on the basis of previous preparatory works. Moreover, we design an ad hoc training strategy, aimed at developing a robust model for global mapping purposes, which has to properly manage the large variety of different acquisition geometries characterizing the TanDEM-X global data set. In addition to detecting forest/non-forest areas, the CNN has also been trained to detect water surfaces, which are typically characterized by low values of coherence. By applying the proposed method on single TanDEM-X images, we achieved a significant performance improvement with respect to the baseline clustering approach, with an average <i>F</i>-score increase of 0.13. We then applied such a model for mapping the entire Amazon rainforest, as well as the other tropical forests in Central Africa and South-East Asia, in order to test its robustness and generalization capabilities, and we observed that forests are typically well detected as contour closed regions and that water classification is reliable, too. Finally, the generated maps show a great potential for mapping temporal changes occurring over forested areas and can be used for generating large-scale maps of deforestation. |
| first_indexed | 2024-03-09T12:40:03Z |
| format | Article |
| id | doaj.art-9df94c1f58644d67b553dc0a4b0bd30f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T12:40:03Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-9df94c1f58644d67b553dc0a4b0bd30f2023-11-30T22:19:40ZengMDPI AGRemote Sensing2072-42922022-08-011416398110.3390/rs14163981Deep Learning for Mapping Tropical Forests with TanDEM-X Bistatic InSAR DataJose-Luis Bueso-Bello0Daniel Carcereri1Michele Martone2Carolina González3Philipp Posovszky4Paola Rizzoli5Microwaves and Radar Institute, German Aerospace Center (DLR), 82234 Wessling, GermanyMicrowaves and Radar Institute, German Aerospace Center (DLR), 82234 Wessling, GermanyMicrowaves and Radar Institute, German Aerospace Center (DLR), 82234 Wessling, GermanyMicrowaves and Radar Institute, German Aerospace Center (DLR), 82234 Wessling, GermanyMicrowaves and Radar Institute, German Aerospace Center (DLR), 82234 Wessling, GermanyMicrowaves and Radar Institute, German Aerospace Center (DLR), 82234 Wessling, GermanyThe TanDEM-X synthetic aperture radar (SAR) system allows for the recording of bistatic interferometric SAR (InSAR) acquisitions, which provide additional information to the common amplitude images acquired by monostatic SAR systems. More concretely, the volume decorrelation factor, which can be derived from the bistatic interferometric coherence, is a reliable indicator of the presence of vegetation and it was used as main input feature for the generation of the global TanDEM-X forest/non-forest map, by means of a clustering algorithm. In this work, we investigate the capabilities of deep Convolutional Neural Networks (CNNs) for mapping tropical forests at large-scale using TanDEM-X InSAR data. For this purpose, we rely on a U-Net architecture, which takes as input a set of feature maps selected on the basis of previous preparatory works. Moreover, we design an ad hoc training strategy, aimed at developing a robust model for global mapping purposes, which has to properly manage the large variety of different acquisition geometries characterizing the TanDEM-X global data set. In addition to detecting forest/non-forest areas, the CNN has also been trained to detect water surfaces, which are typically characterized by low values of coherence. By applying the proposed method on single TanDEM-X images, we achieved a significant performance improvement with respect to the baseline clustering approach, with an average <i>F</i>-score increase of 0.13. We then applied such a model for mapping the entire Amazon rainforest, as well as the other tropical forests in Central Africa and South-East Asia, in order to test its robustness and generalization capabilities, and we observed that forests are typically well detected as contour closed regions and that water classification is reliable, too. Finally, the generated maps show a great potential for mapping temporal changes occurring over forested areas and can be used for generating large-scale maps of deforestation.https://www.mdpi.com/2072-4292/14/16/3981synthetic aperture radarforest mappingdeforestation monitoringdeep learningconvolutional neural networksTanDEM-X |
| spellingShingle | Jose-Luis Bueso-Bello Daniel Carcereri Michele Martone Carolina González Philipp Posovszky Paola Rizzoli Deep Learning for Mapping Tropical Forests with TanDEM-X Bistatic InSAR Data Remote Sensing synthetic aperture radar forest mapping deforestation monitoring deep learning convolutional neural networks TanDEM-X |
| title | Deep Learning for Mapping Tropical Forests with TanDEM-X Bistatic InSAR Data |
| title_full | Deep Learning for Mapping Tropical Forests with TanDEM-X Bistatic InSAR Data |
| title_fullStr | Deep Learning for Mapping Tropical Forests with TanDEM-X Bistatic InSAR Data |
| title_full_unstemmed | Deep Learning for Mapping Tropical Forests with TanDEM-X Bistatic InSAR Data |
| title_short | Deep Learning for Mapping Tropical Forests with TanDEM-X Bistatic InSAR Data |
| title_sort | deep learning for mapping tropical forests with tandem x bistatic insar data |
| topic | synthetic aperture radar forest mapping deforestation monitoring deep learning convolutional neural networks TanDEM-X |
| url | https://www.mdpi.com/2072-4292/14/16/3981 |
| work_keys_str_mv | AT joseluisbuesobello deeplearningformappingtropicalforestswithtandemxbistaticinsardata AT danielcarcereri deeplearningformappingtropicalforestswithtandemxbistaticinsardata AT michelemartone deeplearningformappingtropicalforestswithtandemxbistaticinsardata AT carolinagonzalez deeplearningformappingtropicalforestswithtandemxbistaticinsardata AT philippposovszky deeplearningformappingtropicalforestswithtandemxbistaticinsardata AT paolarizzoli deeplearningformappingtropicalforestswithtandemxbistaticinsardata |