Automatic cloud segmentation from INSAT‐3D satellite image via IKM and IFCM clustering

A, Pugazhenthi; Kumar, Lakshmi Sutha

doi:10.1049/iet-ipr.2018.5271

Cited by 16 publications

(3 citation statements)

References 19 publications

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“…Conventional spectral clustering techniques have revealed critical links between polygonal approximation and the definition of the segments in image partition. This is achieved using the embedded segmentation algorithms [47][48][49]. More complex cases reduce the level of the fragmentation through contouring segment carcasses derived from the upscaled colour texture features and adjusted to the level of fragmentation [50].…”

Section: Examples Of Tools and Softwarementioning

confidence: 99%

Image Segmentation of the Sudd Wetlands in South Sudan for Environmental Analytics by GRASS GIS Scripts

Lemenkova

2023

Analytics

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This paper presents the object detection algorithms GRASS GIS applied for Landsat 8-9 OLI/TIRS data. The study area includes the Sudd wetlands located in South Sudan. This study describes a programming method for the automated processing of satellite images for environmental analytics, applying the scripting algorithms of GRASS GIS. This study documents how the land cover changed and developed over time in South Sudan with varying climate and environmental settings, indicating the variations in landscape patterns. A set of modules was used to process satellite images by scripting language. It streamlines the geospatial processing tasks. The functionality of the modules of GRASS GIS to image processing is called within scripts as subprocesses which automate operations. The cutting-edge tools of GRASS GIS present a cost-effective solution to remote sensing data modelling and analysis. This is based on the discrimination of the spectral reflectance of pixels on the raster scenes. Scripting algorithms of remote sensing data processing based on the GRASS GIS syntax are run from the terminal, enabling to pass commands to the module. This ensures the automation and high speed of image processing. The algorithm challenge is that landscape patterns differ substantially, and there are nonlinear dynamics in land cover types due to environmental factors and climate effects. Time series analysis of several multispectral images demonstrated changes in land cover types over the study area of the Sudd, South Sudan affected by environmental degradation of landscapes. The map is generated for each Landsat image from 2015 to 2023 using 481 maximum-likelihood discriminant analysis approaches of classification. The methodology includes image segmentation by ‘i.segment’ module, image clustering and classification by ‘i.cluster’ and ‘i.maxlike’ modules, accuracy assessment by ‘r.kappa’ module, and computing NDVI and cartographic mapping implemented using GRASS GIS. The benefits of object detection techniques for image analysis are demonstrated with the reported effects of various threshold levels of segmentation. The segmentation was performed 371 times with 90% of the threshold and minsize = 5; the process was converged in 37 to 41 iterations. The following segments are defined for images: 4515 for 2015, 4813 for 2016, 4114 for 2017, 5090 for 2018, 6021 for 2019, 3187 for 2020, 2445 for 2022, and 5181 for 2023. The percent convergence is 98% for the processed images. Detecting variations in land cover patterns is possible using spaceborne datasets and advanced applications of scripting algorithms. The implications of cartographic approach for environmental landscape analysis are discussed. The algorithm for image processing is based on a set of GRASS GIS wrapper functions for automated image classification.

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Section: Examples Of Tools and Softwarementioning

confidence: 99%

Image Segmentation of the Sudd Wetlands in South Sudan for Environmental Analytics by GRASS GIS Scripts

Lemenkova

2023

Analytics

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“…Supervised classification analyses the attribute information of each known class and sets the classification rules to classify other unknown pixels [8,9]. Unsupervised classification first clusters pixels to some classes with similar attributes, and then labels the pixels of each class [10,11]. Currently, object-oriented extraction methods are some of the most commonly used building detection algorithms.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Rooftop Solar Photovoltaic Potential Based on High-Resolution Images and Digital Surface Models

Hu,

Liu,

Huang

et al. 2023

Buildings

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Buildings are important components of urban areas, and the construction of rooftop photovoltaic systems plays a critical role in the transition to renewable energy generation. With rooftop solar photovoltaics receiving increased attention, the problem of how to estimate rooftop photovoltaics is under discussion; building detection from remote sensing images is one way to address it. In this study, we presented an available approach to estimate a building’s rooftop solar photovoltaic potential. A rapid and accurate rooftop extraction method was developed using object-based image classification combining normalized difference vegetation index (NDVI) and digital surface models (DSMs), and a method for the identification of suitable rooftops for solar panel installation by analysing the geographical restrictions was proposed. The approach was validated using six scenes from Beijing that were taken using Chinese Gaofen-2 (GF-2) satellite imagery and Pleiades imagery. A total of 176 roofs in six scenarios were suitable for PV installation, and the estimated photovoltaic panel area was 205,827 m2. The rooftop photovoltaic potential was estimated to total 22,551 GWh. The results indicated that the rooftop photovoltaic potential estimation method performs well.

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“…The results of the study found that creative value and experiential value have a positive effect on customers' purchase intention, thus providing practical strategies and measures in the development of cultural and creative products. Literature [14] used hyperpixel clustering, K-means clustering, and azimuthal gradient histogram pyramid algorithm to achieve automatic segmentation and identification of diseased leaves. Hyperpixel clustering is used for classification, and K-means clustering is used to achieve segmentation of each hyperpixel.…”

Section: Introductionmentioning

confidence: 99%

Research on Design Concept and Cultural Inheritance System of Tourism Cultural and Creative Products in the Context of Deep Learning

2023

Applied Mathematics and Nonlinear Sciences

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In this paper, the design process of cultural and creative product design is first constructed in the context of deep learning and the categories of cultural idea factors are planned. The training of the network for the image extraction classification task is accelerated due to the deep learning model’s fitting ability. Secondly, the 3D virtual intelligence algorithm is applied to use the point cloud as the basis for reconstruction in order to generate a highly reproducible object surface model. Finally, the K-means algorithm is employed to obtain the final extracted color, which constitutes the technical foundation of the product design system. The final results show that the color matching degree reaches 8.91, while the image design level reaches 9.24, which illustrates the effectiveness of the tourism cultural and creative product design system under deep learning.

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Automatic cloud segmentation from INSAT‐3D satellite image via IKM and IFCM clustering

Cited by 16 publications

References 19 publications

Image Segmentation of the Sudd Wetlands in South Sudan for Environmental Analytics by GRASS GIS Scripts

Image Segmentation of the Sudd Wetlands in South Sudan for Environmental Analytics by GRASS GIS Scripts

Estimation of Rooftop Solar Photovoltaic Potential Based on High-Resolution Images and Digital Surface Models

Research on Design Concept and Cultural Inheritance System of Tourism Cultural and Creative Products in the Context of Deep Learning

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