This is the official code for the paper:
End-to-End Learning of Polygons for Remote Sensing Image Classification
Nicolas Girard,
Yuliya Tarabalka
IGARSS 2018
[Paper] [Slides]
- Tensorflow 1.4
- Train the polygon Encoder-Decoder network. This is used to pre-train the weights of the Decoder part of PolyCNN. See the corresponding subdirectory.
- Download and setup the "Distributed Solar Photovoltaic Array Location and Extent Data Set for Remote Sensing Object Identification" dataset, see the corresponding subdirectory.
- Download the pre-trained InceptionV4 checkpoint, see the corresponding subdirectory.
- Train PolyCNN and run inference on the test set, see the corresponding subdirectory.
- Train the U-Net of unet_and_vectorization and run inference on the test set, see the corresponding subdirectory.
- Compare the two methods, see the corresponding subdirectory.
@inproceedings{girard:hal-01762446,
TITLE = {{End-to-End Learning of Polygons for Remote Sensing Image Classification}},
AUTHOR = {Girard, Nicolas and Tarabalka, Yuliya},
URL = {https://hal.inria.fr/hal-01762446},
BOOKTITLE = {{IEEE International Geoscience and Remote Sensing Symposium -- IGARSS 2018}},
ADDRESS = {Valencia, Spain},
YEAR = {2018},
MONTH = Jul,
KEYWORDS = {convolutional neural networks ; Index Terms- High-resolution aerial images ; polygon ; vectorial ; regression ; deep learning},
PDF = {https://hal.inria.fr/hal-01762446/file/girard.pdf},
HAL_ID = {hal-01762446},
HAL_VERSION = {v1},
}