Integrating Convolutional Neural Network and Gated Recurrent Unit for Hyperspectral Image Spectral-Spatial Classification

摘要：

　　In this paper,we propose a novel deep learning framework for hyperspectral image(HSI)spectral-spatial classification.This framework mainly consists of two components: convolutional neural network(CNN)and gated recurrent unit(GRU).CNN is used to automatically extract the high-level spatial features of each band,which are then fed into a fusion network based on GRUs.This fusion network combines feature-level fusion and decision-level fusion together in an end-to-end manner,thus sufficiently fusing the complementary information from different spectral bands.To demonstrate the effectiveness of the proposed method,we compare it with several state-of-the-art deep learning methods on two real HSIs.Experimental results show that the proposed method can achieve better performance than comparison methods.

关键词： Hyperspectral image classification Convolutional neural network Gated recurrent unit Spectral-spatial fusion

作者: Feng Zhou Renlong Hang Qingshan Liu Xiaotong Yuan

作者单位: Jiangsu Key Laboratory of Big Data Analysis Technology,School of Information and Control,Nanjing University of Information Science and Technology,Nanjing 210044,China

会议类型: 国际会议

会议名称: 中国模式识别与计算机视觉大会(PRCV2018)

会议地点: 广州

会议语种:英文

页码: 409-420

在线出版日期: 2018-11-23（万方平台首次上网日期，不代表论文的发表时间）