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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 6, pp. 138-150

Development of the index paradigm in remote sensing of soil cover

I.M. Mikhailenko 1 , V.N. Timoshin 1 
1 Agrophysical Research Institute, Saint Petersburg, Russia
Accepted: 22.11.2022
DOI: 10.21046/2070-7401-2022-19-6-138-150
The aim of the work is the systematic analysis and generalization of the conventional index paradigm of using Earth remote sensing data to assess the state of the soil and vegetation cover. It has been established that the scalar form and the lack of a mathematical basis do not allow the use of conventional vegetation and the similar indices for estimating the vectors of quantitative indicators of the soil and vegetation cover. At the same time, for making many types of management decisions in agriculture, it is important to build index images which reflect such qualitative indicators as types of cultivated and weed plants, the presence of plant diseases, damage of crops and soils, physical and chemical stresses. In terms of informational content, the evaluation of such qualitative states is a procedure for recognizing patterns or classes of soil-and-vegetation complex objects. The subjective empirical approach in choosing the spectral composition of the indices of their combinations, which is currently used, does not currently allow for sufficient reliability of such procedures. Therefore, the purpose of the study present is to formalize the process, which enables to completely exclude the empirical approach of constructing indices and automate the entire procedure for their formation for any number and types of recognizable objects. The basis of formalization is the algorithms for evaluating and selecting the information content of features, followed by the construction of index models, which are linear decision rules for class recognition. The attributes of the classes are the spectral subranges into which the entire spectrum of remote sensing data is divided. The number of informative features is selected from the condition for ensuring the required reliability of recognition of all observed objects (classes).
Keywords: index models, pattern recognition, informative features, algorithms, informative features, model identification
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