Hyperspectral Classification Based on 3D Asymmetric Inception Network with Data Fusion Transfer Learning
This work addresses the problem of overfitting in hyperspectral image classification for remote sensing applications, though it appears incremental as it builds on existing CNN methods with specific adaptations.
The paper tackled hyperspectral image classification by proposing a 3D asymmetric inception network (AINet) with data fusion transfer learning to overcome overfitting from limited training samples, achieving state-of-the-art results on benchmarks like Pavia University, Indian Pines, and Kennedy Space Center.
Hyperspectral image(HSI) classification has been improved with convolutional neural network(CNN) in very recent years. Being different from the RGB datasets, different HSI datasets are generally captured by various remote sensors and have different spectral configurations. Moreover, each HSI dataset only contains very limited training samples and thus it is prone to overfitting when using deep CNNs. In this paper, we first deliver a 3D asymmetric inception network, AINet, to overcome the overfitting problem. With the emphasis on spectral signatures over spatial contexts of HSI data, AINet can convey and classify the features effectively. In addition, the proposed data fusion transfer learning strategy is beneficial in boosting the classification performance. Extensive experiments show that the proposed approach beat all of the state-of-art methods on several HSI benchmarks, including Pavia University, Indian Pines and Kennedy Space Center(KSC). Code can be found at: https://github.com/UniLauX/AINet.