Towards automated selection of data fusion techniques

2014 ◽  
Author(s):  
Krithika Venkataramani ◽  
Shashwat Mishra ◽  
Lovish Kumar
Keyword(s):  
Data Fusion ◽  
Selection Of

scholarly journals Comparison of LSS-IV and LISS-III+LISS-IV merged data for classification of crops

2014 ◽  
Vol II-8 ◽  
pp. 101-105 ◽  
Author(s):  
R. Hebbar ◽  
M. V. R. Sesha Sai
Keyword(s):  
Image Quality ◽  
Data Fusion ◽  
Classification Accuracy ◽  
Spatial Information ◽  
Principal Component ◽  
Pass Filter ◽  
Simultaneous Acquisition ◽  
Liss Iii ◽  
Image Quality Index ◽  
Selection Of

Resourcesat-1 satellite with its unique capability of simultaneous acquisition of multispectral images at different spatial resolutions (AWiFS, LISS-III and LISS-IV MX / Mono) has immense potential for crop inventory. The present study was carried for selection of suitable LISS-IV MX band for data fusion and its evaluation for delineation different crops in a multi-cropped area. Image fusion techniques namely intensity hue saturation (IHS), principal component analysis (PCA), brovey, high pass filter (HPF) and wavelet methods were used for merging LISS-III and LISS-IV Mono data. The merged products were evaluated visually and through universal image quality index, ERGAS and classification accuracy. The study revealed that red band of LISS-IV MX data was found to be optimal band for merging with LISS-III data in terms of maintaining both spectral and spatial information and thus, closely matching with multispectral LISS-IVMX data. Among the five data fusion techniques, wavelet method was found to be superior in retaining image quality and higher classification accuracy compared to commonly used methods of IHS, PCA and Brovey. The study indicated that LISS-IV data in mono mode with wider swath of 70 km could be exploited in place of 24km LISS-IVMX data by selection of appropriate fusion techniques by acquiring monochromatic data in the red band.

On Selection of Data Fusion Schemes for Structural Damage Evaluation

2009 ◽  
Vol 8 (3) ◽  
pp. 223-241 ◽  
Author(s):  
Zhongqing Su ◽  
Xiaoming Wang ◽  
Li Cheng ◽  
Long Yu ◽  
Zhiping Chen
Keyword(s):  
Data Fusion ◽  
Structural Damage ◽  
Damage Evaluation ◽  
Selection Of

scholarly journals On the Selection of the Best Retrieval Result Per Query –An Alternative Approach to Data Fusion–

2009 ◽  
pp. 111-121 ◽  
Author(s):  
Antonio Juárez-González ◽  
Manuel Montes-y-Gómez ◽  
Luis Villaseñor-Pineda ◽  
Daniel Ortíz-Arroyo
Keyword(s):  
Data Fusion ◽  
Alternative Approach ◽  
Retrieval Result ◽  
Selection Of

scholarly journals A Decision Fusion Method Based on Classification Models for Water Quality Monitoring

2021 ◽  
Author(s):  
Mohamed LADJAL ◽  
Mohamed BOUAMAR ◽  
Youcef BRIK ◽  
Mohamed DJERIOUI
Keyword(s):  
Water Quality ◽  
Data Fusion ◽  
Quality Assessment ◽  
Water Quality Assessment ◽  
Decision Fusion ◽  
Support Vector ◽  
Features Selection ◽  
Fusion Method ◽  
Data Set ◽  
Selection Of

Abstract Monitoring of water quality is one of the world's main intentions of countries. In this paper we present the use of Principal Component Analysis (PCA) combined with Support Vector Machines (SVM) and Artificial Neural Network (ANN) based on Decision Templates combination data fusion method. SVM and ANN are employed in classification stage. Decision Templates is applied to increase accuracy of the water quality classification compared to others combination data fusion methods. This work concerned the water quality assessment from Tilesdit dam (Algeria) that it permitted us to acquire additional knowledge and information about study area and to obtain an intelligent monitoring system. The Multi-Layer Perceptron network (MLP) and the One-Against-All strategy for SVM method are have been widely used. The training step is performed in this paper using these techniques to classify water quality from various physicochemical parameters such as temperature, pH, electrical conductivity and turbidity, etc. Eight of them were collected in the period 2009-2018 from the study area. The selection of the excellent parameters of the used models can be improving the performance of classification process. In order to assess their results, an experiment step using collected data set corresponding to the accuracy and running time of training and test phases, and robustness, is carried out. Various scenarios are examined in comparative study to obtain the most results of decision step with and without features selection of the input data. The combination by Decision Templates of two classifiers enhanced expressively the results of the proposed monitoring framework that had prove a considerable ability in surface water quality assessment.

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