.png){kind=icon}
返回
Beyond the Patchwise Classification: Spectral-Spatial Fully Convolutional Networks for Hyperspectral Image Classification
.png){kind=icon}
PRE
AI
DOI：10.1109/TBDATA.2019.2923243.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In recent years, patchwise classification methods are commonly adopted when dealing with the hyperspectral image (HSI) classification. Despite their promising results from the perspective of accuracy, the efficiency of these methods can hardly be ensured since there are redundant computations between adjacent patches. In this paper, we propose a spectral-spatial fully convolutional network for HSI classification with an end-to-end, pixel-to-pixel architecture. Compared with patchwise methods, the proposed framework can avoid the patch extraction and is more efficient. Since the training samples in HSIs are highly sparse, the training strategy in original fully convolutional networks is no longer feasible for HSIs. To solve this problem, we propose a novel mask matrix to assist the back-propagation in the training stage. Considering the importance of spectral and spatial features may vary for different objects and scenes, we combine both features with two weighting factors which can be adaptively learned during the network training. Besides, the dense conditional random field (CRF) is introduced into the framework to further balance the local and global information. Experiments on three benchmark HSI data sets demonstrate that the proposed method can yield competitive results with less time costs compared with patchwise methods.
Keyword:
Conditional random field
deep learning
fully convolutional network
hyperspectral image classification
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
I
IF:
5.7
论文数:
887
被引数:
3.0K
机构
引用论文
Electrochemically assisted micro localized grafting of aptamers in a microchannel engraved in fluorinated thermoplastic polymer Dyneon THV.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏EVALUATION OF FLEXIBLE AND INTERACTIVE TRADEOFF METHOD BASED ON NUMERICAL SIMULATION EXPERIMENTS基于数值模拟实验的柔性交互式权衡方法评价
OAAI
OAAI分享收藏Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images高光谱图像光谱-空间特征同步选择与提取
OAAI
OAAI分享收藏
查看更多内容