Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, Vol. 16, No. 4, pp. 77-85
Rapid detection of target areas of agricultural vegetation using the orthogonal projection method
A.V. Gerus
1 , O.Yu. Panova
1 , V.P. Savorskiy
1, 2 1 V.A. Kotelnikov Institute of Radioengineering and Electronics RAS, Fryazino Branch, Fryazino, Moscow region, Russia
2 Space Research Institute RAS, Moscow, Russia
Accepted: 17.07.2019
DOI: 10.21046/2070-7401-2019-16-4-77-85
A simple multispectral method, which is intended to use in recognition of objects that do not have a specific shape, is proposed. The method is based on three main ideas. The first is that not the original spectra of reflection coefficient (RC) are considered, but the normalized RC spectra, which significantly reduces their variability that interferes with the recognition. The second is the use of a special calibration, in which all the original values of RC spectra are divided by the average of RC spectrum values of the object of interest, which reduces the distortion of the spectra by the atmosphere. The third is the calculation of the scalar projections of the normalized RC spectrum of the signal under investigation with filters composed of orthogonal projections to the average RC spectrum of the object being searched for and possible hypotheses of other objects. For an object of interest, the ratio of the calculated scalar with “its” filter to the product with any “alien” must be greater than 1. This method was tested to identify fields sown with technical hemp, in the presence of spring fields. Seven fields with hemp and nine spring fields were investigated. The method showed a fairly reliable recognition of hemp fields in May and absolute recognizability in July and August. The results turned out to be much better than when using the method of least squares, which is the basis of most other methods of recognition of such objects. This method can be applied to the analysis of objects in real time.
Keywords: orthogonal projection, variability, extended multidimensional space, technical hemp, spring cereals, remote sensing data, visible range, IR range
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