Analysis of the spectral properties of wheat growth in different vegetation periods

Yerzhanova, Akbota; Kassymova, Akmaral; Abdikerimova, Gulzira; Abdimomynova, Manshuk; Tashenova, Zhuldyz; Nurlybaeva, Elmira

doi:10.15587/1729-4061.2021.249278

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…This paper presents 6 methods of orthogonal transformation: Cosine [13], [14]; Hadamard order 2 n [15], [16]; Hadamard order 𝑛 = 𝑝 + 1, 𝑝 ≡ 3(𝑚𝑜𝑑 4) − a prime number, i.e. based on the Legendre symbol [17]; Haar [18], [19]; Slant [20], [21]; and Dobeshi -4 [22], [23].…”

Section: Methodsmentioning

confidence: 99%

Application of informative textural Law’s masks methods for processing space images

Yessenova

Abdikerimova

Murzabekova

et al. 2023

IJECE

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<span lang="EN-US">Image processing systems are currently used to solve many applied problems. The article is devoted to the identification of negative factors affecting the growth of grain in different periods of harvesting, using a program implemented in the MATLAB software environment, based on aerial photographs. The program is based on the Law’s textural mask method and successive clustering. This paper presents the algorithm of the program and shows the results of image processing by highlighting the uniformity of the image. To solve the problem, the spectral luminance coefficient (SBC), normalized difference vegetation index (NDVI), Law’s textural mask method, and clustering are used. This approach is general and has great potential for identifying objects and territories with different boundary properties on controlled aerial photographs using groups of images of the same surface taken at different vegetation periods. That is, the applicability of sets of Laws texture masks with original image enhancement for the analysis of experimental data on the identification of pest outbreaks is being investigated.</span>

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Section: Methodsmentioning

confidence: 99%

Application of informative textural Law’s masks methods for processing space images

Yessenova

Abdikerimova

Murzabekova

et al. 2023

IJECE

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“…The advantage of these methods is the simplicity of the computational procedure and algorithms. The work [12] shows the stages of clustering satellite images. Clustering was performed using vector textural features.…”

Section: Creating a Conceptual Model For Selecting The Most Appropria...mentioning

confidence: 99%

Segmentation of aerospace images by a non-standard approach using informative textural features

Yerzhanova

Abdikerimova

Alimova

et al. 2022

EEJET

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The article presents an analysis of a non-standard approach to the segmentation of textural areas in aerospace images. The question of the applicability of sets of textural features for the analysis of experimental data is being investigated to identify characteristic areas on aerospace images that in the future it will be possible to identify types of crops, weeds, diseases, and pests. The selection of suitable algorithms was carried out and appropriate software tools were created on Matlab 2021a and in the software package for statistical analysis Statistica 12. The main way to extract information is to decrypt images, which are the main carrier of information about the underlying surface. The main tasks of texture area analysis include selection and formation of features describing textural differences; selection and segmentation of textural areas; classification of textural areas; identification of an object by texture. To solve the tasks, spectral brightness coefficient (SBC), Normalized Difference Vegetation Index (NDVI), textural features of various crops and weeds. Much attention will be paid to the development of software tools that allow the selection of features describing textural differences for the segmentation of textural areas into subdomains. That is the question of the applicability of sets of textural features and other parameters for the analysis of experimental data to identify types of soils and soils, vegetation types, humidity, crop damage in aerospace images will be resolved. This approach is universal and has great potential for identifying objects using image clustering. To identify the boundaries of areas with different properties of the image under study, images of the same surface area taken at different times are considered.

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“…the multispectral instrument aboard Sentinel-2 and the operational ground-based thermal imager aboard Landsat-8). At present, the most significant volumes of medium-resolution space imagery data are provided by the LANDSAT-5/7/8 [3] and SENTINEL-2 [4] satellites.…”

Section: Introductionmentioning

confidence: 99%

Applying machine learning to improve a texture type image

Tussupov

Kozhabai

Bayegizova

et al. 2023

EEJET

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The paper is devoted to machine learning methods that focus on texture-type image enhancements, namely the improvement of objects in images. The aim of the study is to develop algorithms for improving images and to determine the accuracy of the considered models for improving a given type of images. Although currently used digital imaging systems usually provide high-quality images, external factors or even system limitations can cause images in many areas of science to be of low quality and resolution. Therefore, threshold values for image processing in a certain field of science are considered. The first step in image processing is image enhancement. The issues of signal image processing remain in the focus of attention of various specialists. Currently, along with the development of information technology, the automatic improvement of images used in any field of science is one of the urgent problems. Images were analyzed as objects: state license plates of cars, faces, sections of the field on satellite images. In this work, we propose to use the models of Super-Resolution Generative Adversarial Network (SRGAN), Extended Super-Resolution Generative Adversarial Networks (ERSGAN). For this, an experiment was conducted, the purpose of which was to retrain the trained ESRGAN model with three different architectures of the convolutional neural network, i.e. VGG19, MobileNet2V, ResNet152V2 to add perceptual loss (by pixels), also add more sharpness to the prediction of the test image, and compare the performance of each retrained model. As a result of the study, the use of convolutional neural networks to improve the image showed high accuracy, that is, on average ESRGAN+MobileNETV2 – 91 %, ESRGAN+VGG19 – 86 %, ESRGAN+ResNet152V2 – 96 %.

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Analysis of the spectral properties of wheat growth in different vegetation periods

Cited by 4 publications

References 7 publications

Application of informative textural Law’s masks methods for processing space images

Application of informative textural Law’s masks methods for processing space images

Segmentation of aerospace images by a non-standard approach using informative textural features

Applying machine learning to improve a texture type image

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