高光谱成像
计算机科学
人工智能
稳健性(进化)
瓶颈
深度学习
上下文图像分类
土地覆盖
特征(语言学)
模式识别(心理学)
卷积神经网络
人工神经网络
遥感
地理
图像(数学)
土地利用
工程类
基因
哲学
土木工程
嵌入式系统
生物化学
化学
语言学
作者
Naftaly Muriuki Wambugu,Yiping Chen,Zhenlong Xiao,Kun Tan,Mingqiang Wei,Xiaoxue Liu,Jonathan Li
出处
期刊:International journal of applied earth observation and geoinformation
日期:2021-12-01
卷期号:105: 102603-102603
被引量:39
标识
DOI:10.1016/j.jag.2021.102603
摘要
Over the years, advances in sensor technologies have enhanced spatial, temporal, spectral, and radiometric resolutions, thus significantly improving the size, resolution, and quality of imagery. These vast developments have inspired improvement in various hyperspectral images (HSI) classification applications such as land cover mapping, vegetation classification, urban monitoring, and understanding which are essential for better utilization of Earth’s resources. HSI classification requires superior algorithms with greater accuracy, less computational complexity, and robustness to extract rich, spectral-spatial information. Deep convolution neural networks (DCCNs) have revolutionized image classification experience, with robust architectures being proposed from time to time. However, insufficient training samples have been earmarked as a significant bottleneck for supervised HSI classification and have not been fully explored in literature. To stimulate further research, this paper reviews current methods that handle labeled data insufficiency and the current feature learning methods for HSI classification using DCNNs. It also presents various methods’ results on the three most popular public HSI datasets, together with intuitive observations motivating future research by the hyperspectral community.
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