New cellular automata applications on multi-temporal and spatial analysis of NOAA-AVHRR images.

被引：0
|
作者：
Ruiz, JAM [1 ]
Moreno, CC [1 ]
Martínez, MC [1 ]
Raya, JTL [1 ]
Martínez, JB [1 ]
机构：
[1] Joint Res Ctr, Space Applicat Inst, I-21020 Ispra, VA, Italy
来源：
IMAGE AND SIGNAL PROCESSING FOR REMOTE SENSING V | 1999年 / 3871卷
关键词：
Cellular Automata (CA); multi-temporal analysis; spatial analysis; spectral analysis; Remote Sensing; NOAA-AVHRR GAC data; burn scar detection; land use/cover change detection;
D O I：
10.1117/12.373267
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：
Since 1940, since Ulam and von Neumann conceived the CA (Cellular Automata) until now, they have been applied to the study of general phenomenological aspects of the world as a way of understanding the behaviour of complex systems. The basic idea has been not to try to describe a complex system from "above", but simulating this system by interaction of cells following easy rules. In this paper we propose to use the Cellular Automata paradigm for Remote Sensing applications on multi-spectral, multitemporal and spatial data analysis. In a first approach, we are designing two new applications which are being implemented in the Space Applications Institute (SAI) in the Global Vegetation Monitoring (GVM) unit in order to process daily NOAA-AVHRR data for detecting burnt surfaces and also land use/cover changes at global scare. The results obtained (burn scar maps and land use/cover changes maps) at global scale from daily NOAA-AVHRR images GAC - 8 km are presented.
引用
收藏
页码：296 / 304
页数：9
相关论文
共 50 条
  • [21] Deriving urban dynamic evolution rules from self-adaptive cellular automata with multi-temporal remote sensing images
    He, Yingqing
    Ai, Bin
    Yao, Yao
    Zhong, Fajun
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2015, 38 : 164 - 174
  • [22] Mapping vegetation-soil-climate complexes in southern Africa using temporal Fourier analysis of NOAA-AVHRR NDVI data
    Azzali, S
    Menenti, M
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2000, 21 (05) : 973 - 996
  • [23] Multi-set multi-temporal canonical analysis of psoriasis images
    Gomez, DD
    Maletti, G
    Nielsen, AA
    Ersboll, B
    2004 2ND IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: MACRO TO NANO, VOLS 1 and 2, 2004, : 1151 - 1154
  • [24] Remotely Sensed Rice Yield Prediction Using Multi-Temporal NDVI Data Derived from NOAA's-AVHRR
    Huang, Jingfeng
    Wang, Xiuzhen
    Li, Xinxing
    Tian, Hanqin
    Pan, Zhuokun
    PLOS ONE, 2013, 8 (08):
  • [25] Urban Growth Modeling Using Cellular Automata with Multi-Temporal Remote Sensing Images Calibrated by the Artificial Bee Colony Optimization Algorithm
    Naghibi, Fereydoun
    Delavar, Mahmoud Reza
    Pijanowski, Bryan
    SENSORS, 2016, 16 (12)
  • [26] Analyzing urban sprawl spatial fragmentation using multi-temporal satellite images
    Tang, Junmei
    Wang, Le
    Yao, Zhijun
    GISCIENCE & REMOTE SENSING, 2006, 43 (03) : 218 - 232
  • [27] Wildlife impact on cultivated lands: A multi-temporal spatial analysis
    Mauri, Luca
    Masin, Roberta
    Tarolli, Paolo
    AGRICULTURAL SYSTEMS, 2020, 184
  • [28] A new algorithm for forest fire detection method with statistical analysis using NOAA AVHRR images
    Kalpoma, Kazi A.
    Kudoh, Jun-Ichi
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2006, 27 (18) : 3867 - 3880
  • [29] Multi-temporal analysis of PAL images for the study of vegetation in South America
    Julien, Y.
    Sobrino, J. A.
    Morales, L.
    REVISTA DE TELEDETECCION, 2007, (27): : 17 - 26
  • [30] Analysis on Identifying Coherence Target Points of Multi-Temporal SAR Images
    Shi, Hongyun
    Yang, Songlin
    INTERNATIONAL CONFERENCE ON REMOTE SENSING AND WIRELESS COMMUNICATIONS (RSWC 2014), 2014, : 441 - 446
12345