Binary coding based feature extraction in remote sensing high dimensional data

被引:27
|
作者
Imani, Maryam [1 ]
Ghassemian, Hassan [1 ]
机构
[1] Tarbiat Modares Univ, Fac Elect & Comp Engn, Tehran, Iran
关键词
Binary coding; Feature extraction; Classification; High dimensional data; Small sample size situation; WEIGHTED FEATURE-EXTRACTION; HYPERSPECTRAL-IMAGE; DISCRIMINANT-ANALYSIS; CLASSIFICATION; RELEVANCE;
D O I
10.1016/j.ins.2016.01.032
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
A binary coding based feature extraction (BCFE) method is proposed in this paper. In the BCFE method, the spectral signature of each pixel of hyperspectral image is partitioned into some equal segments. Then, the weighted mean of spectral bands in each segment is considered as an extracted feature. BCFE uses a new method for calculation of weights. In BCFE, the binary codes of class means are obtained. Then, the information contained in the binary values and the edges of class means is used for calculation of weight in each band. The experimental results on three real hyperspectral images show the better performance of BCFE compared to some popular and state-of-the-art feature extraction methods, from the accuracy and computation time point of views, in a small sample size situation. (C) 2016 Elsevier Inc. All rights reserved.
引用
收藏
页码:191 / 208
页数:18
相关论文
共 50 条
  • [11] High Spatial Resolution Remote Sensing Data Computing Pattern based on Feature Primitives
    Ming Dongping
    Zhou Wen
    Tan Tian
    Shen Zhanfeng
    Luo Jiancheng
    IEEE 12TH INT CONF UBIQUITOUS INTELLIGENCE & COMP/IEEE 12TH INT CONF ADV & TRUSTED COMP/IEEE 15TH INT CONF SCALABLE COMP & COMMUN/IEEE INT CONF CLOUD & BIG DATA COMP/IEEE INT CONF INTERNET PEOPLE AND ASSOCIATED SYMPOSIA/WORKSHOPS, 2015, : 468 - 475
  • [12] A strategy for feature extraction of high dimensional noisy data
    Bhushan, B
    Romagnoli, JA
    PROCEEDINGS OF THE 25TH IASTED INTERNATIONAL CONFERENCE ON MODELLING, IDENTIFICATION, AND CONTROL, 2006, : 441 - +
  • [13] Fusion Based Feature Extraction and Optimal Feature Selection in Remote Sensing Image Retrieval
    Vharkate, Minakshi N.
    Musande, Vijaya B.
    MULTIMEDIA TOOLS AND APPLICATIONS, 2022, 81 (22) : 31787 - 31814
  • [14] Fusion Based Feature Extraction and Optimal Feature Selection in Remote Sensing Image Retrieval
    Minakshi N. Vharkate
    Vijaya B. Musande
    Multimedia Tools and Applications, 2022, 81 : 31787 - 31814
  • [15] Diffusion-Based Inpainting for Coding Remote Sensing Data
    Amrani, Naoufal
    Serra-Sagrista, Joan
    Peter, Pascal
    Weickert, Joachim
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2017, 14 (08) : 1203 - 1207
  • [16] Effective Feature Extraction and Data Reduction in Remote Sensing Using Hyperspectral Imaging
    Ren, Jinchang
    Zabalza, Jaime
    Marshall, Stephen
    Zheng, Jiangbin
    IEEE SIGNAL PROCESSING MAGAZINE, 2014, 31 (04) : 149 - 154
  • [17] Remote sensing image fusion method based on depth feature extraction
    Xiao, Yunlong
    Guo, Xinyi
    Journal of Physics: Conference Series, 2024, 2863 (01):
  • [18] CLOUD DETECTION METHOD BASED ON FEATURE EXTRACTION IN REMOTE SENSING IMAGES
    Yu Changhui
    Yuan Yuan
    Miao Minjing
    Zhu Menglu
    8TH INTERNATIONAL SYMPOSIUM ON SPATIAL DATA QUALITY, 2013, 40-2 (w1): : 173 - 177
  • [19] Feature extraction from remote sensing data using kernel orthonormalized PLS
    Arenas-Garcia, Jeronimo
    Camps-Valls, Gustavo
    IGARSS: 2007 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-12: SENSING AND UNDERSTANDING OUR PLANET, 2007, : 258A - +
  • [20] Feature extraction and pattern classification of remote sensing data by a modular neural system
    Blonda, P
    laForgia, V
    Pasquariello, G
    Satalino, G
    OPTICAL ENGINEERING, 1996, 35 (02) : 536 - 542