Next-Generation Artificial Intelligence Techniques for Satellite Data Processing

Sisodiya, Neha; Dube, Nitant; Thakkar, Priyank

doi:10.1007/978-3-030-24178-0_11

Cited by 15 publications

(20 citation statements)

References 57 publications

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“…Сүүлийн үед эдгээр асуудлуудыг компьютерын тусламжтайгаар хүний сэтгэлгээний зарчимд тулгуурлан шийдвэрлэх төрөл бүрийн оролдлого ихээхэн хийгдэж байгаа юм [4] Өөрөөр хэлбэл, техник, технологийн хөгжилтэй уялдан олон хэмжээст, шугаман бус аргын хэрэглээ нэмэгдсэнээр ЗТС-ын салбарт томоохон ахиц дэвшил гарч, дараагийн шинэ чиглэл болох дүрс мэдээг боловсруулах хиймэл оюуны технологиуд, ялангуяа машин сургалтын аргуудын хэрэглээ эрчимтэй нэмэгдэж байна.…”

Section: оршилunclassified

Land cover classification using machine-learning method and vegetation indices

Enkhtuya

Amarsaikhan

Ulziibat

et al. 2022

Mong. J. Geogr. Geoecol.

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Machine-learning offers the potential for effective and efficient classification of remotely sensed imagery. The strength of the machine-learning include the capacity to handle data of high dimensionality and to map classes with very complex characteristics. This study aimed to apply the machine-learning method for an improved land cover classification. For this purpose, multispectral Sentinel-2 data along with 3 vegetation indices (NDVI -normalized difference vegetation index, TSAVI-transformed soil adjusted vegetation index, and MTVI-modified triangular vegetation index) were acquired in July 2021 around Zuunburen soum, Selenge province, and analysed to classify and compare land cover using random forest (RF) and support vector machine (SVM) techniques. For the actual classifications, three vegetation indices, NDVI, TSAVI, and MTVI, which were derived from the visible and infrared bands of Sentinel-2a, were used. As the result, the land cover was classified into 5 classes including forest, cropland, grass, soil, and water, and the overall accuracy of the machine-learning method was above 87%. Газрын бүрхэвчийг машин сургалттай ангиллын арга болон ургамлын индексүүд ашиглан ангилах нь ХУРААНГУЙ Машин сургалттай ангиллын арга нь зайнаас тандсан мэдээг илүү үр дүнтэй, нарийвчлалтай сайтай ангилах боломжийг олгодог. Уг ангиллын давуу тал нь их хэмжээний өгөгдөлтэй ажиллах, маш нарийн төвөгтэй шинж чанар бүхий ангиудыг ангилахад оршино. Энэхүү судалгаа нь Сэлэнгэ аймгийн Зүүнбүрэн сумын нутгийн Sentinel-2 хиймэл дагуулын олон бүсчлэлийн мэдээнд санамсаргүй форест, тулах векторын зэрэг машин сургалттай ангиллын аргуудыг ашиглан, газрын бүрхэвчийн ангилал хийж, харьцуулсан дүгнэлт хийх зорилготой бөгөөд ангилалд NDVI, TSAVI, MTVI гэсэн 3 төрлийн ургамлын индексийг ашигласан болно. Дүн шинжилгээнд, газрын бүрхэвчийг ус, ой, тариалан, ногоон ургамал, хөрс гэсэн 5 ангид хуваан ангилсан ба эцсийн үр дүнгээс харахад машин сургалттай аргуудын ерөнхий нарийвчлал 87%-иас дээш байлаа. Түлхүүр үгс: Газрын бүрхэвчийн ангилал, Машин сургалт, Санамсаргүй форест, Тулах векторын арга

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Section: оршилunclassified

Land cover classification using machine-learning method and vegetation indices

Enkhtuya

Amarsaikhan

Ulziibat

et al. 2022

Mong. J. Geogr. Geoecol.

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“…However, there is a high probability of observing changes or inconsistencies in characteristics (spatial, spectral, temporal, or multidata sources) of multispectral/remote sensing data. e traditional machine learning approaches efficiently work on static data assumptions and have limitations in a dynamic environment [26]. e static nature causes performance degradation in terms of performance accuracy or makes them ineffective.…”

Section: Complexitymentioning

confidence: 99%

Adaptive CNN Ensemble for Complex Multispectral Image Analysis

et al. 2020

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Multispectral image classification has long been the domain of static learning with nonstationary input data assumption. The prevalence of Industrial Revolution 4.0 has led to the emergence to perform real-time analysis (classification) in an online learning scenario. Due to the complexities (spatial, spectral, dynamic data sources, and temporal inconsistencies) in online and time-series multispectral image analysis, there is a high occurrence probability in variations of spectral bands from an input stream, which deteriorates the classification performance (in terms of accuracy) or makes them ineffective. To highlight this critical issue, firstly, this study formulates the problem of new spectral band arrival as virtual concept drift. Secondly, an adaptive convolutional neural network (CNN) ensemble framework is proposed and evaluated for a new spectral band adaptation. The adaptive CNN ensemble framework consists of five (05) modules, including dynamic ensemble classifier (DEC) module. DEC uses the weighted voting ensemble approach using multiple optimized CNN instances. DEC module can increase dynamically after new spectral band arrival. The proposed ensemble approach in the DEC module (individual spectral band handling by the individual classifier of the ensemble) contributes the diversity to the ensemble system in the simple yet effective manner. The results have shown the effectiveness and proven the diversity of the proposed framework to adapt the new spectral band during online image classification. Moreover, the extensive training dataset, proper regularization, optimized hyperparameters (model and training), and more appropriate CNN architecture significantly contributed to retaining the performance accuracy.

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“…Satellite monitoring of the state of grain crops provides objective and regular monitoring of the development of crops, as well as assessment of crop productivity and the intended use of agricultural land [16], [17]. However, for the effective functioning of the monitoring system, it is necessary to improve the methods of processing satellite data [18] in order to more accurately assess the condition of crops and identify cultivated areas within the framework of regional monitoring. Such a task is not only of interest from a theoretical point of view, relating to digitalization and the introduction of digital technologies in agriculture [19], [20], but also becomes a practical issue for improving methods for monitoring the condition of crops.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Disease and Pest Dynamics in Steppe Crop Using Structured Data

Tussupov,

Abdikerimova,

Ismailova

et al. 2024

IEEE Access

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In this study, an analysis of the spectral brightness coefficients (SBC) of agricultural crops in the steppe regions of Kazakhstan was carried out using remote field and space measurements during the growing season. The possibility of assessing structural ch anges in plants using near-infrared data was explored. An electronic database of corrected spectral data of SBC for the studied crops has been created. The results obtained confirm that the dynamics of SBC values reflect morphophysiological changes in crops during their growing season. Analysis of the seasonal dynamics of the spectral brightness of pests and diseases makes it possible to diagnose their species composition and the physiological state of plants. Spectrophotometric information obtained from sa tellite data of various crops can be used to build simulation models that provide scientifically based forecasts of the dynamics of the spatial distribution of the species composition of vegetation cover. The study highlights the potential of using subtle spectral differences to study the spatial distribution of different vegetation types and their ecological status. The results of the analysis of satellite information from Sentinel confirm the prospects of its use for assessing the morphophysiological state of agricultural crops using ground-based spectrometric calibration, as well as for predicting yields.INDEX TERMS Structured data, crops, spectral brightness coefficients, diseases and pests, data verification

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Next-Generation Artificial Intelligence Techniques for Satellite Data Processing

Cited by 15 publications

References 57 publications

Land cover classification using machine-learning method and vegetation indices

Land cover classification using machine-learning method and vegetation indices

Adaptive CNN Ensemble for Complex Multispectral Image Analysis

Analyzing Disease and Pest Dynamics in Steppe Crop Using Structured Data

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