Detection of fallow lands overgrowth from satellite images based on wavelet analysis

To improve the efficiency of agricultural lands condition and use monitoring, method for detecting fallow lands overgrowth with tree and shrub vegetation using multi-temporal multispectral satellite images of high spatial resolution is proposed. The task of localization of overgrowth is solved on the basis of multi-temporal satellite images wavelet analysis. This technique allows to operationally and automatically detect areas of fallow lands overgrowth with tree and shrub vegetation. The proposed methodology was tested using satellite images obtained from Quickbird, GeoEye and WorldView satellites. For each image, a fourth-order wavelet decomposition was performed in terms of the Daubeshi wavelet functions. Comparing the different levels of the images wavelet transform, it is possible to divide the areas without tree and shrub vegetation, and where overgrowth was depicted. The method proposed by the authors for detecting changes based on wavelet analysis has shown that this method practically does not depend on the influence of shooting conditions and, accordingly, is more effective at detecting changes. When solving the problem of effective involvement in the turnover of agricultural land, the question arises of determining the location and area of unused land plots subject to tree and shrub vegetation overgrowth. The most operational and reliable data for solving this problem are multispectral images obtained by space survey systems. However, automating the process of detecting changes based on multi-time satellite images is difficult task, in addition, visual analysis is often used when performing production work, which requires a lot of labor costs. Thus, the search for new approaches for automated recognition of terrain objects images and identification of changes in multispectral satellite images at different times that would allow for prompt and effective the detecting of unused agricultural land and overgrowth long fallow is an urgent task. Modern automated methods of decryption make it possible to recognize various objects with sufficient reliability. However, when analyzing multispectral images of different times, the spectral characteristics of the land lots may change, since they significantly depend on the shooting conditions and the object status. Therefore, algorithms in which the main decryption feature is the object spectral brightness cannot serve as a sufficient decryption feature [1; 2]. Many algorithms have been developed for detecting changes from * Corresponding author: lge_21@mail.ru https://doi.org/10.1051/e3sconf/202133301003 E3S Web of Conferences 333, 01003 (2021) RPERS 2021 © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/). satellite images, but there is no universal method, as a rule, the algorithms are adapted for certain type of data (survey systems) and changes categories. In addition, different objects classes when recognized from satellite images often do not mutually exclude each other, which make it difficult to perform Change Detection procedures [1; 3]. One of the solutions to the problem is the use of structural features of multispectral images of reference plots in satellite images. It is known that structural features are more stable, characterize the internal properties of object images directly related to its structure and practically does not change over time [4; 5; 6], which means that algorithms based on these properties are more efficient than using spectral properties of images. Such features are obtained by Fourier transformation, wavelet transformation, by transformation using specially specified basis functions of various types (for example, spherical functions) [7; 8; 9]. The paper presents the results of researches of the proposed technique for the rapid detection of fallow lands overgrowth with woody and shrubby vegetation on multi-time multispectral satellite images based on the wavelet transform. The purpose of this work is to assess the possibility of using wavelet analysis when processing multi-time images to localize the overgrowth of agricultural land plots with tree and shrub. The paper provides an example of using wavelet functions in which decomposition is performed for different scales of the functions under study. When applying the wavelet decomposition, various levels of decompositions are analyzed in order to identify changes. In general, the wavelet transform of the function f(t) can be written in the following form [9]:     1/2 1 ( , ) ( ) | | t b W f a b f t dt a a 