论文标题
不断发展的深度卷积神经网络，用于高光谱图像
Evolving Deep Convolutional Neural Networks for Hyperspectral Image Denoising
论文作者
论文摘要
高光谱图像（HSIS）容易受到导致信息丢失的各种噪声因子的影响，噪声限制了随后的HSIS对象检测和分类任务。近年来，基于学习的方法证明了它们在降级HSIS方面的优势优势。不幸的是，大多数方法都是基于广泛的专业知识手动设计的，而这些专业知识不一定可供感兴趣的用户使用。在本文中，我们提出了一种新型算法，以自动构建一个最佳的卷积神经网络（CNN），以有效地转化HSIS。尤其是，提出的算法着重于CNN的连接权重的架构和初始化。该算法的实验已精心设计，并与最新的同伴竞争对手进行了比较，实验结果证明了所提出算法的竞争性能在不同的评估指标，视觉评估和计算复杂性方面的竞争性能。
Hyperspectral images (HSIs) are susceptible to various noise factors leading to the loss of information, and the noise restricts the subsequent HSIs object detection and classification tasks. In recent years, learning-based methods have demonstrated their superior strengths in denoising the HSIs. Unfortunately, most of the methods are manually designed based on the extensive expertise that is not necessarily available to the users interested. In this paper, we propose a novel algorithm to automatically build an optimal Convolutional Neural Network (CNN) to effectively denoise HSIs. Particularly, the proposed algorithm focuses on the architectures and the initialization of the connection weights of the CNN. The experiments of the proposed algorithm have been well-designed and compared against the state-of-the-art peer competitors, and the experimental results demonstrate the competitive performance of the proposed algorithm in terms of the different evaluation metrics, visual assessments, and the computational complexity.
