我们提出了一个简单的深度学习架构，该架构结合了Inception，ResNet和Xception网络的元素。四个新的数据集分别用于大型和小型训练样本的分类。..

IRX-1D: A Simple Deep Learning Architecture for Remote Sensing Classifications

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.