.png){kind=icon}
返回
Spatial-spectral ant colony optimization for hyperspectral image classification
.png){kind=icon}
PRE
AI
DOI：10.1080/01431161.2018.1430403.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Hyperspectral satellite images contain a lot of information in terms of spectral behaviour of objects and this information can be extracted by several mechanisms including image classification. Traditional spectral information-based methods of hyperspectral image classification are generally followed by spatial information-driven post-processing techniques such as relaxation labelling and Markov Random Field. Spectral or spatial information alone may lead to different results depending upon scene captured. An algorithm which can incorporate influence of both spectral and spatial features is needed to address this problem. In this article, an ant colony optimisation-based hyperspectral image classification technique is proposed. This method exploits both spatial and spectral features. Five standard hyperspectral data sets have been used to validate the proposed method and comparisons with other approaches have been carried out. It was observed that the proposed method yielded a significant improvement in classification accuracy. For the instance, nearly 10% increase in accuracy was observed when compared to Support Vector Machine for Indian pines, Botswana, and Salinas images.
Keyword:
EXTRACTION
FRAMEWORK
MACHINE
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
IF:
2.6
论文数:
1.2W
被引数:
2.7W
机构
引用论文
Initial State Encoding via Reverse Quantum Annealing and H-Gain Features通过逆向量子退火和H-Gain特征的初始状态编码
OAAI
.png){kind=icon}
OAAI分享收藏Contextual and Hierarchical Classification of Satellite Images Based on Cellular Automata基于元胞自动机的卫星图像上下文和层次分类
PREAI
分享收藏分享收藏分享收藏分享收藏Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles
PREAI
分享收藏
查看更多内容