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Volume 37 #2

Contents

  1. Problems and prospects of new methods of light stimulation in visual rehabilitation
  2. Compression in the auditory system
  3. The response of adult adzuki been borer Ostrinia scapulalis to light stimuli in a wind tunnel
  4. Analysis of the gaze fixations in performance of mental recall of the route on a city map. Effects of landmark type and gender
  5. Electroretinographic examinations of the crew members of the 8-month international experiment SIRIUS 20/21
  6. Forward masking of the auditory evoked potentials in a dolphin at monaural and dichotic auditory stimulation: implications for the preceding effect and biosonar
  7. Classification of crops by NDVI time series of reduced dimensionality

Classification of crops by NDVI time series of reduced dimensionality

© 2023 M. A. Pavlova, D. S. Sidorchuk, D. A. Bocharov

Institute for Information Transmission Problems. A.A. Kharkevich RAS 127051, Moscow, Bolshoi Karetny lane, 19, Russia

Received 10 Mar 2023

The paper considers the problem of classification of agricultural crops. As is known, to solve this problem, it is much more efficient to use not instantaneous remote sensing data or calculated vegetation indices, but their historical series. Time series formed by index values for a fixed spatial point at different dates are characterized by a high level of missing values, caused primarily by cloudiness on some dates. A study of known methods of time series approximation has been carried out. The question of whether reducing the dimensionality of the approximated time series can improve the quality of crops classification is also investigated. In the experimental part of the work, NDVI time series calculated from the Sentinel-2 multispectral satellite data were used. The classification of corn, sunflower, wheat and soybeans was studied. The paper shows that UMAP usage for dimensionality reduction leads to 1.5 times increase of classification quality in terms of average the F1-measure compared to using the original dimension data. A new crop classification method based on cubic spline approximation of NDVI time series, extraction of features of low dimension by the UMAP algorithm and their classification by the k nearest neighbors method is proposed.

Key words: Remote sensing, crop classification, time series, NDVI, time series fitting, feature extraction, dimensionality reduction, UMAP

DOI: 10.31857/S023500922302004X  EDN: QSZZFT

Cite: Pavlova M. A., Sidorchuk D. S., Bocharov D. A. Klassifikatsiya selskokhozyaistvennykh kultur na osnove analiza vremennykh ryadov vegetatsionnogo indeksa c ponizheniem ikh razmernosti [Classification of crops by ndvi time series of reduced dimensionality]. Sensornye sistemy [Sensory systems]. 2023. V. 37(2). P. 171–180 (in Russian). doi: 10.31857/S023500922302004X

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