论文标题
IRX-1D：用于遥感分类的简单深度学习体系结构
IRX-1D: A Simple Deep Learning Architecture for Remote Sensing Classifications
论文作者
论文摘要
我们提出了一个简单的深度学习体系结构，结合了Inception，Resnet和Xception网络的要素。四个新数据集用于与小型和大型培训样本的分类。与分类精度有关的结果表明，与贝叶斯优化的2D-CNN相比，提出的体系结构的性能提高了性能。使用小型培训样本与印第安纳松树高光谱数据集的结果比较表明，与使用不同深度学习架构的九种报道的作品相比，提议的架构可比性或更好的性能。尽管通过有限的训练样本来实现高分类的精度，但分类图像的比较表明，与使用大型培训样品一起使用所有数据集的大型培训样本进行了训练的模型，将不同的土地覆盖类别分配给同一区域。
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.
