Preliminary research in using the technology of data mining to analyze remote sensing data

2003 ◽  
Author(s):  
Xiongfei Zhang ◽  
Xing Li ◽  
Xia Zhang ◽  
Qingxi Tong ◽  
Wei Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Preliminary Research

Application of the Parallel Spatial Association Rule in Remote Sensing Data Mining

2012 ◽  
Vol 500 ◽  
pp. 598-602
Author(s):  
Jun Ma ◽  
Dong Dong Zhang
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Association Rule ◽  
Spatial Association ◽  
Remote Sensing Data ◽  
Inner Product ◽  
Data Mining Algorithm ◽  
Sensing Data ◽  
The Common ◽  
Frequent Item Sets

Since the remote sensing data are multi-resources and massive, the common data mining algorithm cannot effectively discover the knowledge what people want to know. However, spatial association rule can solve the problem of inefficiency in remote sensing data mining. This paper gives an algorithm to compute the frequent item sets though a method like calculating vectors inner-product. And the algorithm will introduce pruning in the whole running. It reduces the time and resources consumption effectively

Estimation of area burned by forest fires in Mediterranean countries: A remote sensing data mining perspective

2011 ◽  
Vol 262 (8) ◽  
pp. 1597-1607 ◽  
Author(s):  
Carmen Quintano ◽  
Alfonso Fernández-Manso ◽  
Alfred Stein ◽  
Wietske Bijker
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Forest Fires ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Area Burned ◽  
Mediterranean Countries

scholarly journals Assessment of Phytoecological Variability by Red-Edge Spectral Indices and Soil-Landscape Relationships

2019 ◽  
Vol 11 (20) ◽  
pp. 2448 ◽  
Author(s):  
Helena S. K. Pinheiro ◽  
Theresa P. R. Barbosa ◽  
Mauro A. H. Antunes ◽  
Daniel Costa de Carvalho ◽  
Alexis R. Nummer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Mining ◽  
Remote Sensing Data ◽  
Parent Material ◽  
Mountainous Area ◽  
Kappa Index ◽  
Sensing Data ◽  
Taxonomic Level ◽  
Red Edge ◽  
Geological Conditions

There is a relation of vegetation physiognomies with soil and geological conditions that can be represented spatially with the support of remote sensing data. The goal of this research was to map vegetation physiognomies in a mountainous area by using Sentinel-2 Multispectral Instrument (MSI) data and morphometrical covariates through data mining techniques. The research was based on red-edge (RE) bands, and indices, to classify phytophysiognomies at two taxonomic levels. The input data was pixel sampled based on field sample sites. Data mining procedures comprised covariate selection and supervised classification through the Random Forest model. Results showed the potential of bands 3, 5, and 6 to map phytophysiognomies for both seasons, as well as Green Chlorophyll (CLg) and SAVI indices. NDVI indices were important, particularly those calculated with bands 6, 7, 8, and 8A, which were placed at the RE position. The model performance showed reasonable success to Kappa index 0.72 and 0.56 for the first and fifth taxonomic level, respectively. The model presented confusion between Broadleaved dwarf-forest, Parkland Savanna, and Bushy grassland. Savanna formations occurred variably in the area while Bushy grasslands strictly occur in certain landscape positions. Broadleaved forests presented the best performance (first taxonomic level), and among its variation (fifth level) the model could precisely capture the pattern for those on deep soils from gneiss parent material. The approach was thus useful to capture intrinsic soil-plant relationships and its relation with remote sensing data, showing potential to map phytophysiognomies in two distinct taxonomic levels in poorly accessible areas.

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