Physically-Constrained Transfer Learning through Shared Abundance Space
for Hyperspectral Image Classification
- URL: http://arxiv.org/abs/2008.08563v2
- Date: Sun, 30 Aug 2020 11:05:13 GMT
- Title: Physically-Constrained Transfer Learning through Shared Abundance Space
for Hyperspectral Image Classification
- Authors: Ying Qu, Razieh Kaviani Baghbaderani, Wei Li, Lianru Gao, Hairong Qi
- Abstract summary: We propose a new transfer learning scheme to bridge the gap between the source and target domains.
The proposed method is referred to as physically-constrained transfer learning through shared abundance space.
- Score: 14.840925517957258
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Hyperspectral image (HSI) classification is one of the most active research
topics and has achieved promising results boosted by the recent development of
deep learning. However, most state-of-the-art approaches tend to perform poorly
when the training and testing images are on different domains, e.g., source
domain and target domain, respectively, due to the spectral variability caused
by different acquisition conditions. Transfer learning-based methods address
this problem by pre-training in the source domain and fine-tuning on the target
domain. Nonetheless, a considerable amount of data on the target domain has to
be labeled and non-negligible computational resources are required to retrain
the whole network. In this paper, we propose a new transfer learning scheme to
bridge the gap between the source and target domains by projecting the HSI data
from the source and target domains into a shared abundance space based on their
own physical characteristics. In this way, the domain discrepancy would be
largely reduced such that the model trained on the source domain could be
applied on the target domain without extra efforts for data labeling or network
retraining. The proposed method is referred to as physically-constrained
transfer learning through shared abundance space (PCTL-SAS). Extensive
experimental results demonstrate the superiority of the proposed method as
compared to the state-of-the-art. The success of this endeavor would largely
facilitate the deployment of HSI classification for real-world sensing
scenarios.
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