Semantic Segmentation of Landsat Satellite Imagery

Herlawati, Herlawati; Handayanto, Rahmadya Trias; Atika, Prima Dina; Sugiyatno, Sugiyatno; Rasim, Rasim; Mugiarso, Mugiarso; Hendharsetiawan, Andy Achmad; Jaja, Jaja; Purwanti, Santi

doi:10.1109/icic56845.2022.10006917

Cited by 2 publications

(4 citation statements)

References 21 publications

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“…Satellite image segmentation have received significant attention in recent years. Models developed for it have been used in numerous applications, such as surficial materials mapping [7], machine-learning-based computer vision [8], change detection threshold techniques [9], contextual pattern recognition for object detection [10][11][12], statistical segmentation [13,14], fusion detection with spectral and thermal feature combination [15], and texture synthesis [16], among many others. The segmentation of a satellite images is based on probabilistic modelling, which is applicable to a wide range of image structures [17].…”

Section: Current Research Statusmentioning

confidence: 99%

“…These are used to define categories of each evaluated cell. The 30-resolution remote sensing imagery corresponds to the following land cover types in the Sudd region [123] (10) and Water areas. These land cover types were used for landscape analysis.…”

Section: Classificationmentioning

confidence: 99%

“…The computed areas by land cover classes and their changes by years are summarised in Table 2. The classes signify the following land cover types: (1) Cropland; (2) Herbaceous coverage; 473 areas (3) Forest; (4) Mosaic tree canopies; (5) Shrubland; (6) Grassland; (7) Flooded and inundated; 474 areas; (8) Bare areas; (9) Built-up areas; (10) Water areas. These land cover types were used 475 times for landscape analysis.…”

Section: Detection Of Segmented Areasmentioning

confidence: 99%

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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: Current Research Statusmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

Section: Detection Of Segmented Areasmentioning

confidence: 99%

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

Lemenkova

2023

Analytics

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“…In this study, Residual Networks (ResNet) backbones, specifically ResNet50 and ResNet101, were utilized. These backbones incorporate convolutional residual blocks, which are capable of effectively processing a large number of layers (Herlawati, 2022). The U-net algorithm, originally designed for biomedical image segmentation, has been widely employed in numerous studies.…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification of Selected Corine Classes Using Deep Learning Based Semantic Segmentation

Demir,

Musaoglu

2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In this study, deep learning-based semantic segmentation is used to automatically generate CORINE land cover (CLC) Level 2 classes for a test region in Türkiye. This is accomplished by utilizing new datasets and models created from a pilot region in Italy, which exhibits similar land use/land cover (LU/LC) characteristics to the test region in Canakkale/Türkiye. The training and validation datasets for Italy were generated by employing Sentinel-2 images from various months and different band combinations, along with CLC 2018 vector data for labelling. Different datasets were created to investigate the impact of patch sizes (128 and 256 pixels) and seasonal changes in LU/LC. For the semantic segmentation task, the U-Net architecture was selected as the primary deep learning model. Furthermore, the U-Net architecture was used in conjunction with ResNet50 and ResNet101 for transfer learning, enabling the replacement of the encoder section of the U-Net. These models were tested in the Italy region, and the best-performing ones were subsequently applied to the Canakkale test region to automatically generate CLC 2018. The results were compared with published CLC 2018 Level 2 data for the same region, and the accuracy was assessed using the Intersection over Union (IoU) metric. The findings were presented both visually and statistically.

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Semantic Segmentation of Landsat Satellite Imagery

Cited by 2 publications

References 21 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

Automatic Classification of Selected Corine Classes Using Deep Learning Based Semantic Segmentation

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