亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Information-theoretic feature selection with segmentation-based folded principal component analysis (PCA) for hyperspectral image classification

主成分分析 模式识别(心理学) 高光谱成像 人工智能 相互信息 特征选择 熵(时间箭头) 数学 计算机科学 核主成分分析 核方法 支持向量机 量子力学 物理
作者
Md. Palash Uddin,Md. Al Mamun,Masud Ibn Afjal,Md. Ali Hossain
出处
期刊:International Journal of Remote Sensing [Taylor & Francis]
卷期号:42 (1): 286-321 被引量:78
标识
DOI:10.1080/01431161.2020.1807650
摘要

Hyperspectral image (HSI) usually holds information of land cover classes as a set of many contiguous narrow spectral wavelength bands. For its efficient thematic mapping or classification, band (feature) reduction strategies through Feature Extraction (FE) and/or Feature Selection (FS) methods for finding the intrinsic bands’ information are typically applied. Principal Component Analysis (PCA) is a frequently employed unsupervised linear FE method whereas cumulative-variance accumulation is used for selecting top features from PCA data. However, PCA can fail to extract intrinsic structure of HSI due to global variance consideration and domination by visible and near infrared bands while cumulative-variance accumulation has no capability to exploit non-linear relationships among the transformed features produced by PCA-based FE methods. Consequently, we propose an information theoretic normalized Mutual Information (nMI)-based minimum Redundancy Maximum Relevance (mRMR) non-linear measure to select the intrinsic features from the transformed space of our previously proposed Segmented-Folded-PCA (Seg-Fol-PCA) and Spectrally Segmented-Folded-PCA (SSeg-Fol-PCA) FE methods. We extensively analyse the effectiveness of the proposed unsupervised FE and supervised FS combinations Seg-Fol-PCA-mRMR and SSeg-Fol-PCA-mRMR with that of PCA-based existing linear and non-linear state-of-the-art methods. In addition, cumulative variance-based top features pick-up strategy is considered with all FE methods and Renyi quadratic entropy-based FS is used with Kernel Entropy Component Analysis (Ker-ECA). The experimental results illustrate that SSeg-Fol-PCA-mRMR and Seg-Fol-PCA-mRMR obtain highest classification result e.g. 95.39% and 95.03% respectively for agricultural Indian Pines HSI, and 96.58% and 95.30% respectively for urban Washington DC Mall HSI while the classification accuracies using all original features of the HSIs are 70.28% and 91.90% respectively. Moreover, the proposed SSeg-Fol-PCA-mRMR and Seg-Fol-PCA-mRMR outperform all investigated combinations of FE and FS using the real HSI datasets.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
4秒前
Hello应助儒雅致远采纳,获得10
11秒前
正在获取昵称中...完成签到,获得积分10
25秒前
28秒前
爆米花应助xiongdi521采纳,获得10
37秒前
49秒前
xiongdi521发布了新的文献求助10
52秒前
xiongdi521完成签到,获得积分10
56秒前
mmyhn发布了新的文献求助10
1分钟前
1分钟前
1分钟前
Liiiiiiiiii发布了新的文献求助10
1分钟前
三水完成签到 ,获得积分20
2分钟前
小净完成签到 ,获得积分20
2分钟前
cccttt完成签到,获得积分10
2分钟前
mmyhn发布了新的文献求助10
2分钟前
Echo完成签到,获得积分10
2分钟前
无花果应助zzx采纳,获得10
2分钟前
可爱的香菇完成签到 ,获得积分10
2分钟前
2分钟前
dovejingling完成签到,获得积分10
2分钟前
lulu发布了新的文献求助20
2分钟前
Jasper应助科研通管家采纳,获得10
2分钟前
顾矜应助科研通管家采纳,获得10
2分钟前
2分钟前
李沐唅完成签到 ,获得积分10
3分钟前
核桃发布了新的文献求助30
3分钟前
3分钟前
阿凯完成签到 ,获得积分10
3分钟前
zzx发布了新的文献求助10
3分钟前
zzx完成签到,获得积分10
3分钟前
小泉完成签到 ,获得积分10
3分钟前
星辰大海应助高兴的忆曼采纳,获得10
4分钟前
英姑应助核桃采纳,获得10
4分钟前
科研通AI5应助核桃采纳,获得10
4分钟前
科研通AI5应助核桃采纳,获得10
4分钟前
可爱的函函应助核桃采纳,获得10
4分钟前
Liufgui应助核桃采纳,获得10
4分钟前
在水一方应助核桃采纳,获得10
4分钟前
隐形曼青应助科研通管家采纳,获得10
4分钟前
高分求助中
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3990045
求助须知:如何正确求助?哪些是违规求助? 3532108
关于积分的说明 11256334
捐赠科研通 3270943
什么是DOI,文献DOI怎么找? 1805146
邀请新用户注册赠送积分活动 882270
科研通“疑难数据库(出版商)”最低求助积分说明 809228