A SPATIO-TEMPORAL AUTOCORRELATION CHANGE DETECTION APPROACH USING HYPER-TEMPORAL SATELLITE DATA

被引:1
|
作者
Kleynhans, W. [1 ,2 ]
Salmon, B. P. [1 ,2 ]
Wessels, K. J. [2 ]
Olivier, J. C. [3 ]
机构
[1] Univ Pretoria, Dept Elect Elect & Comp Engn, ZA-0002 Pretoria, South Africa
[2] CSIR, Meraka Inst, Remote Sensing Res Unit, Pretoria, South Africa
[3] Univ Tasmania, Sch Engn, Hobart, Tas 7001, Australia
关键词
MODIS TIME-SERIES; HUMAN-SETTLEMENTS; IMAGES;
D O I
10.1109/IGARSS.2013.6723573
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
There has been recent developments in the use of hypertemporal satellite time series data for land cover change detection and classification in South Africa and in particular, the monitoring of human settlement expansion is of relevance as it is the most pervasive form of land-cover change in the country. In this paper, a spatio-temporal change detection method is proposed that is applicable over large regions. This is achieved by adjusting the change threshold based on the change properties of a neighbourhood of pixels. Results indicate that the addition of spatial information increase change detection accuracy when compared to a pixel based approach.
引用
收藏
页码:3459 / 3462
页数:4
相关论文
共 50 条
  • [11] Spatio-Temporal Change Detection Using Granger Sequence Pattern
    Pavasant, Nat
    Numao, Masayuki
    Fukui, Ken-ichi
    [J]. PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2020, : 5202 - 5203
  • [12] A solution for change detection in spatio-temporal database
    Wang, Huibing
    Tang, Xinming
    Shi, Shaoyu
    [J]. GEOINFORMATICS 2007: GEOSPATIAL INFORMATION SCIENCE, PTS 1 AND 2, 2007, 6753
  • [13] SPATIO-TEMPORAL STATISTICAL SEQUENTIAL ANALYSIS FOR TEMPERATURE CHANGE DETECTION IN SATELLITE IMAGERY
    Alfergani, Husam
    Bouaynaya, Nidhal
    Nazari, Rouzbeh
    [J]. IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2020, : 2917 - 2920
  • [14] UNSUPERVISED DEEP LEARNING APPROACH TO ANALYZE SPATIO-TEMPORAL CHANGE IN SATELLITE IMAGERY
    Nukavarapu, Nivedita
    Yang, Jiue-An
    Jankowska, Marta M.
    [J]. IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2023, : 2496 - 2499
  • [15] A Spatio-Temporal Model for Forest Fire Detection Using HJ-IRS Satellite Data
    Lin, Lei
    Meng, Yu
    Yue, Anzhi
    Yuan, Yuan
    Liu, Xiaoyi
    Chen, Jingbo
    Zhang, Mengmeng
    Chen, Jiansheng
    [J]. REMOTE SENSING, 2016, 8 (05):
  • [16] Adaptive spatio-temporal models for satellite ecological data
    Carlo Grillenzoni
    [J]. Journal of Agricultural, Biological, and Environmental Statistics, 2004, 9 : 158 - 180
  • [17] Adaptive spatio-temporal models for satellite ecological data
    Grillenzoni, C
    [J]. JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS, 2004, 9 (02) : 158 - 180
  • [18] A Statistical Approach for Shadow Detection using Spatio-Temporal Contexts
    Liu, Yiyang
    Adjeroh, Don
    [J]. 2010 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, 2010, : 3457 - 3460
  • [19] Approach of the spatio-temporal prediction using vectorial geographic data
    MezzadriCenteno, T
    SaintJoan, D
    Desachy, J
    Vidal, F
    [J]. REMOTE SENSING FOR GEOGRAPHY, GEOLOGY, LAND PLANNING, AND CULTURAL HERITAGE, 1996, 2960 : 96 - 103
  • [20] Spatio-temporal Outlier Detection in Precipitation Data
    Wu, Elizabeth
    Liu, Wei
    Chawla, Sanjay
    [J]. KNOWLEDGE DISCOVERY FROM SENSOR DATA, 2010, 5840 : 115 - 133