IRX-1D: A Simple Deep Learning Architecture for Remote Sensing Classifications
This is an incremental improvement for remote sensing applications, particularly when training data is limited.
The authors tackled remote sensing classification by proposing IRX-1D, a simple deep learning architecture combining Inception, ResNet, and Xception elements, achieving improved or comparable accuracy to existing methods with small training samples on four new datasets.
We proposes a simple deep learning architecture combining elements of Inception, ResNet and Xception networks. Four new datasets were used for classification with both small and large training samples. Results in terms of classification accuracy suggests improved performance by proposed architecture in comparison to Bayesian optimised 2D-CNN with small training samples. Comparison of results using small training sample with Indiana Pines hyperspectral dataset suggests comparable or better performance by proposed architecture than nine reported works using different deep learning architectures. In spite of achieving high classification accuracy with limited training samples, comparison of classified image suggests different land cover classes are assigned to same area when compared with the classified image provided by the model trained using large training samples with all datasets.