论文标题
SRL-SOA：使用稀疏的1D手术自动编码器进行高光谱图像带选择的自我代表学习
SRL-SOA: Self-Representation Learning with Sparse 1D-Operational Autoencoder for Hyperspectral Image Band Selection
论文作者
论文摘要
考虑到其对计算复杂性和准确性的影响，高光谱图像（HSI）数据处理中的频带选择是一项重要的任务。在这项工作中，我们为乐队选择问题提出了一个新颖的框架：具有稀疏1D运行自动编码器（SOA）的自我代理学习（SRL）。所提出的SLR-SOA方法引入了一种新型的自动编码器模型SOA，该模型旨在学习一个稀疏表示数据的表示域。此外，该网络由非线性神经元模型组成1D术层。因此，神经元（过滤器）的学习能力通过浅层建筑大大提高。使用紧凑的体系结构在自动编码器中尤为重要，因为它们的身份映射目标倾向于轻松地拟合。总体而言，我们表明，考虑到达到的土地覆盖分类精度，提出的SRL-SOA频段选择方法比两个HSI数据（包括印度松树和盐业）的方法优于包括印度松树和Salinas-A的竞争方法。 SRL-SOA方法的软件实现在https://github.com/meteahishali/srl-soa上公开共享。
The band selection in the hyperspectral image (HSI) data processing is an important task considering its effect on the computational complexity and accuracy. In this work, we propose a novel framework for the band selection problem: Self-Representation Learning (SRL) with Sparse 1D-Operational Autoencoder (SOA). The proposed SLR-SOA approach introduces a novel autoencoder model, SOA, that is designed to learn a representation domain where the data are sparsely represented. Moreover, the network composes of 1D-operational layers with the non-linear neuron model. Hence, the learning capability of neurons (filters) is greatly improved with shallow architectures. Using compact architectures is especially crucial in autoencoders as they tend to overfit easily because of their identity mapping objective. Overall, we show that the proposed SRL-SOA band selection approach outperforms the competing methods over two HSI data including Indian Pines and Salinas-A considering the achieved land cover classification accuracies. The software implementation of the SRL-SOA approach is shared publicly at https://github.com/meteahishali/SRL-SOA.
