Efficient dimension reduction of hyperspectral images for big data remote sensing applications

高光谱成像 计算机科学 维数(图论) 还原(数学) 遥感 数据缩减 降维 计算机视觉 人工智能 地质学 数据挖掘 数学 几何学 纯数学
作者
Beatriz P. Garcia-Salgado,Volodymyr Ponomaryov,Sergiy Sadovnychiy,Rogelio Reyes-Reyes
出处
期刊:Journal of Applied Remote Sensing [SPIE]
卷期号:14 (03): 1-1 被引量:4
标识
DOI:10.1117/1.jrs.14.032611
摘要

A large amount of remote sensing data can be easily acquired due to the increase in the advances in sensor’s technologies. The sensors can generate high-dimensional data in a lower time producing problems related to big data such as management and organization. Since the acquired data is characterized by a large dimension and lack of structure, the information analysis becomes harder. Therefore, an organization stage should structure the data reducing the dimension while maintaining the main properties to enable further analysis. The feature extraction and selection methods can achieve this task. Consequently, we aim to explore various pixel-wise feature extraction and selection algorithms to manage the organization stage of big data for hyperspectral images. Our work covers the comparison between feature vectors computed using the discrete Fourier transform, discrete cosine transform (DCT), and stationary wavelet transform. Moreover, spectral angle mapper, Jeffries–Matusita distance, spectral information divergence, and linear discriminant analysis (LDA) were implemented as feature selectors. Feature extraction and selection methods were combined and evaluated in terms of algorithm complexity, reduction efficiency, and classification accuracy with the aid of a support vector machine and a maximum likelihood classifier. The analysis shows that some linear transformations can perform better in natural landscapes and others in urban images. Furthermore, the study found that the combination of DCT and LDA, which achieves high classification rates with an efficient dimension reduction, can be suitable for the organization stage of a big data remote sensing application of hyperspectral images.
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
胡萝卜发布了新的文献求助10
刚刚
哈哈哈发布了新的文献求助10
1秒前
1秒前
汉堡包应助果粒多采纳,获得10
2秒前
6秒前
华仔发布了新的文献求助20
6秒前
6秒前
科研通AI2S应助杜杜采纳,获得10
8秒前
量子星尘发布了新的文献求助10
9秒前
JK发布了新的文献求助10
9秒前
打打应助顺利一德采纳,获得10
10秒前
法外狂徒完成签到,获得积分10
11秒前
Orange应助十九岁的时差采纳,获得10
11秒前
科研通AI2S应助steam采纳,获得10
13秒前
潇湘雪月发布了新的文献求助10
13秒前
14秒前
青青子衿完成签到,获得积分10
14秒前
14秒前
14秒前
16秒前
crazy发布了新的文献求助10
19秒前
杜杜发布了新的文献求助10
20秒前
嗯嗯发布了新的文献求助10
21秒前
老大蒂亚戈完成签到,获得积分10
23秒前
宝安完成签到,获得积分10
27秒前
JamesPei应助动听的老鼠采纳,获得10
27秒前
27秒前
杨可言完成签到,获得积分10
27秒前
28秒前
28秒前
29秒前
Hello应助子非鱼采纳,获得10
30秒前
31秒前
33秒前
mzhmhy发布了新的文献求助10
35秒前
李健的粉丝团团长应助ASA采纳,获得30
36秒前
Choi完成签到,获得积分0
36秒前
无辜如容发布了新的文献求助10
36秒前
123完成签到,获得积分10
37秒前
38秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
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 3989263
求助须知:如何正确求助?哪些是违规求助? 3531418
关于积分的说明 11253814
捐赠科研通 3270066
什么是DOI,文献DOI怎么找? 1804884
邀请新用户注册赠送积分活动 882084
科研通“疑难数据库(出版商)”最低求助积分说明 809136