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Davoudabadi M, Mohtadi A. Classification of Grey Streaming Data Using Integrated Data Envelopment Analysis and Multi-Attribute Decision Making: Application to Vegetation Clusterin. jor 2026; 23 (1)
URL: http://jamlu.lahijan.iau.ir/article-1-2325-en.html
URL: http://jamlu.lahijan.iau.ir/article-1-2325-en.html
Remote Sensing, Hikmat Institute of Higher Education, Qom, Iran , mohtadi1375@gmail.com
Abstract: (73 Views)
This study proposes a novel method for classifying grey streaming data by integrating Data Envelopment Analysis (DEA) with Multi-Attribute Decision Making (MADM) techniques. Streaming data are inherently dynamic and continuously evolving, and the presence of grey uncertainty further increases the complexity of their analysis. In this research, Landsat satellite imagery was used as a representative example of streaming data. Pixel brightness values were first normalized into fractional grey numbers within the interval [0, 1], and the Normalized Difference Vegetation Index (NDVI) was computed to provide an initial unsupervised separation between vegetated and non-vegetated areas. To determine an optimal threshold between these two classes, a permutation-based MADM algorithm was employed.
The proposed model was first evaluated using simulated data and then applied to real satellite imagery from a region in Tehran, Iran. Validation based on randomly selected reference points using Google Earth images demonstrated that the method achieved an overall vegetation-classification accuracy of approximately 80%. The main contribution of this research lies in developing a hybrid DEA–MADM framework capable of handling continuous and uncertain grey data, thereby expanding the analytical capacity of operations research techniques within remote sensing applications.
The proposed model was first evaluated using simulated data and then applied to real satellite imagery from a region in Tehran, Iran. Validation based on randomly selected reference points using Google Earth images demonstrated that the method achieved an overall vegetation-classification accuracy of approximately 80%. The main contribution of this research lies in developing a hybrid DEA–MADM framework capable of handling continuous and uncertain grey data, thereby expanding the analytical capacity of operations research techniques within remote sensing applications.
Keywords: Data Envelopment Analysis (DEA), Grey Streaming Data, Multi-Attribute Decision Making (MADM).
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