A study of various classification techniques used for very high-resolution remote sensing [VHRRS] images

被引：2
|
作者：
Vijayalakshmi, S. [1 ]
Kumar, Magesh [1 ]
Arun, M. [2 ]
机构：
[1] Saveetha Inst Med & Tech Sci, Saveetha Sch Engn, Dept CSE, Chennai 602105, Tamil Nadu, India
[2] Dr MGR Educ & Res Inst, Dept CSE, Chennai, Tamil Nadu, India
来源：
MATERIALS TODAY-PROCEEDINGS | 2021年 / 37卷
关键词：
Image classification; Machine learning; Remote sensing; Accuracy; Assessment metrics;
D O I：
10.1016/j.matpr.2020.08.703
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Image classification the most predominant applications in wide range of domains like image processing, computer vision etc. This paper analyses various image classification algorithm based on various machine learning technology and also most commonly used accuracy assessment metrics has been addressed. A wide range of methods increases its accuracy in image classification have attained higher range. Even though still enhancement is still undergoing to attain newer accuracy scorer. This paper provides a study of various image classification algorithms supported in machine learning technology. This study also gives brief information of essential accuracy testing metrics required for image classification. (C) 2020 Elsevier Ltd. All rights reserved.
引用
收藏
页码：2947 / 2951
页数：5
相关论文
共 50 条
  • [1] Scene classification of high-resolution remote sensing images based on IMFNet
    Zhang, Xin
    Wang, Yongcheng
    Zhang, Ning
    Xu, Dongdong
    Chen, Bo
    Ben, Guangli
    Wang, Xue
    JOURNAL OF APPLIED REMOTE SENSING, 2019, 13 (04)
  • [2] Pansharpening for Cloud-Contaminated Very High-Resolution Remote Sensing Images
    Meng, Xiangchao
    Shen, Huanfeng
    Yuan, Qiangqiang
    Li, Huifang
    Zhang, Liangpei
    Sun, Weiwei
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2019, 57 (05): : 2840 - 2854
  • [3] Fusion of shallow and deep features for classification of high-resolution remote sensing images
    Gao, Lang
    Tian, Tian
    Sun, Xiao
    Li, Hang
    MIPPR 2017: MULTISPECTRAL IMAGE ACQUISITION, PROCESSING, AND ANALYSIS, 2018, 10607
  • [4] HISTOGRAM BASED ATTRIBUTE PROFILES FOR CLASSIFICATION OF VERY HIGH RESOLUTION REMOTE SENSING IMAGES
    Demir, Beguem
    Bruzzone, Lorenzo
    2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2015, : 2393 - 2396
  • [5] Bag of words KAZE (BoWK) with two-step classification for high-resolution remote sensing images
    Muhammad, Usman
    Wang, Weiqiang
    Hadid, Abdenour
    Pervez, Shahbaz
    IET COMPUTER VISION, 2019, 13 (04) : 395 - 403
  • [6] A comparative study of threshold selection methods for change detection from very high-resolution remote sensing images
    Huaqiao Xing
    Linye Zhu
    Bingyao Chen
    Chang Liu
    Jingge Niu
    Xuehan Li
    Yongyu Feng
    Wenbo Fang
    Earth Science Informatics, 2022, 15 : 369 - 381
  • [7] A comparative study of threshold selection methods for change detection from very high-resolution remote sensing images
    Xing, Huaqiao
    Zhu, Linye
    Chen, Bingyao
    Liu, Chang
    Niu, Jingge
    Li, Xuehan
    Feng, Yongyu
    Fang, Wenbo
    EARTH SCIENCE INFORMATICS, 2022, 15 (01) : 369 - 381
  • [8] Land Use Classification Using High-Resolution Remote Sensing Images Based on Structural Topic Model
    Shao, Hua
    Li, Yang
    Ding, Yuan
    Zhuang, Qifeng
    Chen, Yicong
    IEEE ACCESS, 2020, 8 : 215943 - 215955
  • [9] Histogram-Based Attribute Profiles for Classification of Very High Resolution Remote Sensing Images
    Demir, Beguem
    Bruzzone, Lorenzo
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2016, 54 (04): : 2096 - 2107
  • [10] FPGA-Based Implementation of a CNN Architecture for the On-Board Processing of Very High-Resolution Remote Sensing Images
    Neris, Romen
    Rodriguez, Adrian
    Guerra, Raul
    Lopez, Sebastian
    Sarmiento, Roberto
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2022, 15 : 3740 - 3750
12345