We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
TPENAS: A Two-Phase Evolutionary Neural Architecture Search for Remote Sensing Image Classification.
- Authors
Ao, Lei; Feng, Kaiyuan; Sheng, Kai; Zhao, Hongyu; He, Xin; Chen, Zigang
- Abstract
The application of deep learning in remote sensing image classification has been paid more and more attention by industry and academia. However, manually designed remote sensing image classification models based on convolutional neural networks usually require sophisticated expert knowledge. Moreover, it is notoriously difficult to design a model with both high classification accuracy and few parameters. Recently, neural architecture search (NAS) has emerged as an effective method that can greatly reduce the heavy burden of manually designing models. However, it remains a challenge to search for a classification model with high classification accuracy and few parameters in the huge search space. To tackle this challenge, we propose TPENAS, a two-phase evolutionary neural architecture search framework, which optimizes the model using computational intelligence techniques in two search phases. In the first search phase, TPENAS searches for the optimal depth of the model. In the second search phase, TPENAS searches for the structure of the model from the perspective of the whole model. Experiments on three open benchmark datasets demonstrate that our proposed TPENAS outperforms the state-of-the-art baselines in both classification accuracy and reducing parameters.
- Subjects
IMAGE recognition (Computer vision); DEEP learning; COMPUTATIONAL intelligence; CONVOLUTIONAL neural networks; REMOTE sensing
- Publication
Remote Sensing, 2023, Vol 15, Issue 8, p2212
- ISSN
2072-4292
- Publication type
Article
- DOI
10.3390/rs15082212