Parallel implementation of the informative areas generation method in the spatial spectrum domain

This paper proposes a parallel implementation of the image informative segments extraction method. The images are segmented in the spatial spectrum domain. The median energy in each selected segment is viewed upon as an area. For purposes of time savings, a parallel implementation of the algorithm for calculating the areas is developed. The developed approach to the parallel algorithm implementation is tested on a high performance multicore computing system. The experiments have shown that the parallel implementation of the method allows us to obtain a three-fold speedup, which is a good result.This work was partially supported by the Ministry of Education and Science of the Russian Federation under the Program of increasing SSAU's competitiveness among the world’s leading scientific and educational centers in the years 2013-2020; the Russian Foundation for Basic Research grants (# 15-29- 03823, # 15-29- 07077, # 16-41- 630761; # 16-29- 11698, # 17-01-00972); and the ONIT RAS program # 6 “Bioinformatics, modern information technologies and mathematical methods in medicine” 2017

Brightness normalization for Blurred Image Matching

Blurred Image Matching (BIM) is based on image pre-processing and Blob detection. BIM methods has been designed to function with images presenting a strong level of noise of different kinds. The technique shows an excellent robustness, speed and unique features when compared to existing methods. This article investigates the process BIM is based on, proposes a new way to improve the range of noise the technique can process with a good range of success by adding image normalization. Moreover, the article investigates the technique’s performances when confronted to different parameters, thus suggestion an ideal brightness for the blob detection to perform at the best of its capacities

Food vulnerability analysis in the central dry zone of Myanmar

The central dry zone of Myanmar is the most water stressed and also one of the most food insecure regions in the country. In the Dry Zone, the total population is 10.1 million people in 54 townships, in which approximately 43 % of people live below the poverty line and 40 – 50 % of the rural population is landless. Agriculture is the most important economic sector in Myanmar as it is essential for the national food security and a major source of livelihood of the people. In this region the adverse effects of climate change such as a late or early onset of the monsoon season, longer dry spells, erratic rainfall, increasing temperatures, heavy rains, stronger typhoons, extreme spatial-temporal variability of rainfall, high intensities, limited rainfall events in the growing season, heat stress, drought, flooding, sea water intrusion, land degradation, desertification, deforestation, and other natural disasters are believed to be major constraints to food security. Theses extreme climatic events are likely to increase in frequency and magnitude, leading to serious drought periods and extreme floods. Food insecurity is an important thing that must be reviewed because it affects the lives of many people. For food vulnerability, we use the following indicators: slope, precipitation, vegetation, soil, erosion, land degradation and harvest failure in ArcGIS software. The erosion is influenced by rainfall and slope, while land degradation is directly related to vegetation, drainage and soil. In the meantime, the harvest failure can be generated by rainfall and flood potential zones. The results show that around 45 % of the area studied comes under a very high erosion danger level, 70 % are in the average harvest failure zone, 59 % are in the intermediate land degradation area, and overall around 45 % of the studied area comes under the insecure food vulnerability zone. Our analysis shows that an increase in the alluvial farming by 1745.33 km2 since 1988 has helped reduce the insecure food vulnerability. The food vulnerability map is also relevant to increased population and low income areas. This paper is helpful for identifying the areas of food needs in central dry zone of Myanmar.This work was financially supported by the Russian Science Foundation (RSF), grant no. 14-31-00014 “Establishment of a Laboratory of Advanced Technology for Earth Remote Sensing”

Image storage optimization and feature calculation on Netezza Database system

The storage of images in databases has long been a delicate matter but comes now more and more in use. This kind of data is in most DBMS limited in storage size to 64kb, making necessary to divide large objects into fragments; moreover, programming languages also present limitations in data retrieval. In this paper, we experimented the retrieval, reconstitution, feature calculation of pictures using different fragment size and determined the optimal one. In this work, the server used was a Netezza model from the N2001 line. In this work, we present the architecture of the database, our research and bring recommendation about the optimal fragmentation size to store images into a database

Comparison of hyperspectral and multi-spectral imagery to building a spectral library and land cover classification performanc

The main aim of this research work is to compare k-nearest neighbor algorithm (KNN) supervised classification with migrating means clustering unsupervised classification (MMC) method on the performance of hyperspectral and multispectral data for spectral land cover classes and develop their spectral library in Samara, Russia. Accuracy assessment of the derived thematic maps was based on the analysis of the classification confusion matrix statistics computed for each classified map, using for consistency the same set of validation points. We were analyzed and compared Earth Observing-1 (EO-1) Hyperion hyperspectral data to Landsat 8 Operational Land Imager (OLI) and Advance Land Imager (ALI) multispectral data. Hyperspectral imagers, currently available on airborne platforms, provide increased spectral resolution over existing space based sensors that can document detailed information on the distribution of land cover classes, sometimes species level. Results indicate that KNN (95, 94, 88 overall accuracy and .91, .89, .85 kappa coefficient for Hyp, ALI, OLI respectively) shows better results than unsupervised classification (93, 90, 84 overall accuracy and .89, .87, .81 kappa coefficient for Hyp, ALI, OLI respectively). Development of spectral library for land cover classes is a key component needed to facilitate advance analytical techniques to monitor land cover changes. Different land cover classes in Samara were sampled to create a common spectral library for mapping landscape from remotely sensed data. The development of these libraries provides a physical basis for interpretation that is less subject to conditions of specific data sets, to facilitate a global approach to the application of hyperspectral imagers to mapping landscape. In addition, it is demonstrated that the hyperspectral satellite image provides more accurate classification results than those extracted from the multispectral satellite image. The higher classification accuracy by KNN supervised was attributed principally to the ability of this classifier to identify optimal separating classes with low generalization error, thus producing the best possible classes’ separation.This work was partially supported by the Ministry of education and science of the Russian Federation; by the Russian Foundation for Basic Research grants (# 16-41-630761; # 16-29-11698, # 17-01-00972)

Spatiotemporal ecosystem health assessment comparison under the pressure-state-response framework

A spatiotemporal ecosystem health (EH) assessment study is necessary for sustainable development and proper management of natural resources. At present higher rate of human-socio-economic activities, industrialization, and misuse of land are major factors for ecosystem degradation. Therefore this research work used remote sensing (RS) and geographical information system (GIS) technology, under pressure-state-response (PSR) framework with analytic hierarchy process (AHP) weight method based on 29 indicators were analyzed for spatiotemporal EH assessment in Tatarstan and Samara states in Russia from 2010 to 2020. Results indicate continuous degradation of EH in Tatarstan state while in Samara state first decreased and later on an improved ecosystem health condition. This is one of the most innovative analyses work for real-time accurate ecosystem health assessment, mapping, and monitoring as well as protect fragile eco-environment with sustainable development, proper policy-making, and management at any scale and region.The research was supported by the Ministry of Science and Higher Education of the Russian Federation (Grant # 0777-2020-0017) and partially funded by RFBR, project number # 19-29-01135

Vegetation Drought Dynamics Analysis in European Russia

This research work deals with the spatial-temporal characteristics of the relationship between drought events (Standardized Precipitation Index [SPI]), land surface temperature (LSI) and vegetation indexes (VIs) in the spring-summer (May-August) over the European Russia (ER) from 2000 to 2018. We use Terra- MODIS - NDVI and LST product and TRMM for rainfall data. Statistical results indicate that year 2004, 2009 and 2015 were the most significant changing-point in mean annual rainfall values and VIs. Results indicate that vegetation area and VIs variate according to SPI values. Analysis results also indicate that low NDVI values (0.2-0.4) shift in high NDVI values (0.5-0.8) with high SPI values and vice-versa, also high LST values associate with low VIs values and vice-versa, with correlation coefficients 0.90, means high temperature show low vegetation. A correlation analysis of VIs, SPI and LST deficit shows that vegetation is closely related to rainfall and temperature, especially under the dry and wet conditions, and indicates that the use of this correlation can be a suitable near-real time monitoring of vegetation drought dynamics. All predictions and monitoring using satellite-derived VIs is a low cost and effective means of identifying longer-term changes as opposed to natural inter-annual variability in vegetation growth

Comparison in hyperspectral and multi-spectral remote sensing data for land cover classification in Samara, Russia

The main aim of this study is to evaluate k-nearest neighbor algorithm (KNN) supervised classification with migrating means clustering unsupervised classification (MMC) method on hyperspectral and multispectral imagery to discriminating land-cover classes. Accuracy assessment of the derived thematic maps was based on the analysis of the classification confusion matrix statistics computed for each classification map, using for consistency the same set of validation points. We used Earth Observing-1 (EO-1) Hyperion hyperspectral data to Landsat 8 Operational Land Imager (OLI) and Advance Land Imager (ALI) multispectral data. Results indicate that KNN (95, 94, 88 overall accuracy and .91, .89, .85 kappa coefficient for Hyp, ALI, OLI respectively) shows better results than unsupervised classification (93, 90, 84 overall accuracy and .89, .87, .81 kappa coefficient for Hyp, ALI, OLI respectively). In addition, it is demonstrated that the hyperspectral satellite image provides more accurate classification results than those extracted from the multispectral satellite image. The higher classification accuracy by KNN supervised was attributed principally to the ability of this classifier to identify optimal separating classes with low generalization error, thus producing the best possible classes’ separation.This work was supported by the Federal Agency of Scientific Organizations (agreement No 007-ГЗ/Ч3363/26 and science of the Russian Federation; by the Russian Foundation for Basic Research grants (# 16-41-630761; # 16-29-11698, # 17-01-00972)

Agricultural plant hyperspectral imaging dataset

Detailed automated analysis of crop images is critical to the development of smart agriculture and can significantly improve the quantity and quality of agricultural products. A hyperspectral camera potentially allows to extract more information about the observed object than a conventional one, so its use can help in solving problems that are difficult to solve with conventional methods. Often, predictive models that solve such problems require a large dataset for training. However, sufficiently large datasets of hyperspectral images of agricultural plants are not currently publicly available. Therefore, we present a new dataset of hyperspectral images of plants in this paper. This dataset can be accessed via URL https://pypi.org/project/HSI-Dataset-API/. It contains 385 hyperspectral images with a spatial resolution of 512 by 512 pixels and spectral resolution of 237 spectral bands. The images were captured in the summer of 2021 in Samara and Novocherkassk (Russia) using Offner based Imaging Hyperspectrometer of our own production. The article demonstrates the work of some basic approaches to the analysis of hyperspectral images using the dataset and states problems for further solving.This work was supported by the Ministry of Science and Higher Education of the Russian Federation under Grant 00600/2020/51896 agreement number 075-15-2022-319