In certain geographic locations like South Asia, the landscape changes dramatically between dry and wet seasons. The main factor responsible for this variation is the flora that transforms the landscape between seasons. These transformations can affect the performance of deep learning models trained to analyse satellite images, especially if there are domain shifts between training and testing data distributions. The current work shows that an architecture which employs a Gabor convolutional layer as the first layer of a deep network input focuses on more salient parts of the image than one which uses a standard convolutional layer meaning that removing colour information is less damaging than for the standard network. Further we show that the proposed architecture is robust in the presence of domain shifts due to seasonal data variations.
History
Publication status
Published
File Version
Accepted version
Journal
IEEE International Symposium on Geoscience and Remote Sensing (IGARSS) proceedings