Object Based and Pixel Based Classification Using Rapideye Satellite Imager of ETI-OSA, Lagos, Nigeria

Makinde, E. O.; Salami, Ayobami T.; Olaleye, J. B.; Okewusi, Oluwapelumi Comfort

doi:10.14311/gi.15.2.5

Cited by 26 publications

(21 citation statements)

References 5 publications

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“…The classification can be performed either by pixel-or object-based approaches. It has been widely demonstrated that object-based approaches can provide more accurate results when using high-and very high-resolution data [86][87][88]. In contrast, when the object's dimension is smaller than the pixel resolution (e.g., when a single-tree canopy is to be detected in images with 10 m spatial resolution), a pixel-based approach is preferable.…”

Section: Classificationmentioning

confidence: 99%

Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation

et al. 2021

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The sustainable management of natural heritage is presently considered a global strategic issue. Owing to the ever-growing availability of free data and software, remote sensing (RS) techniques have been primarily used to map, analyse, and monitor natural resources for conservation purposes. The need to adopt multi-scale and multi-temporal approaches to detect different phenological aspects of different vegetation types and species has also emerged. The time-series composite image approach allows for capturing much of the spectral variability, but presents some criticalities (e.g., time-consuming research, downloading data, and the required storage space). To overcome these issues, the Google Earth engine (GEE) has been proposed, a free cloud-based computational platform that allows users to access and process remotely sensed data at petabyte scales. The application was tested in a natural protected area in Calabria (South Italy), which is particularly representative of the Mediterranean mountain forest environment. In the research, random forest (RF), support vector machine (SVM), and classification and regression tree (CART) algorithms were used to perform supervised pixel-based classification based on the use of Sentinel-2 images. A process to select the best input image (seasonal composition strategies, statistical operators, band composition, and derived vegetation indices (VIs) information) for classification was implemented. A set of accuracy indicators, including overall accuracy (OA) and multi-class F-score (Fm), were computed to assess the results of the different classifications. GEE proved to be a reliable and powerful tool for the classification process. The best results (OA = 0.88 and Fm = 0.88) were achieved using RF with the summer image composite, adding three VIs (NDVI, EVI, and NBR) to the Sentinel-2 bands. SVM and RF produced OAs of 0.83 and 0.80, respectively.

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Section: Classificationmentioning

confidence: 99%

Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation

et al. 2021

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“…In comparison with pixelbased classification, object-based classification classifies the image based on objects instead of pixels. Although this technique has been introduced in the 1970s, its application in the remote sensing field started a decade ago (Makinde et al, 2016). Even though this technique has been generally used for high and very high-resolution imagery, it has also been successfully applied in middle-resolution imagery.…”

Section: Methodsmentioning

confidence: 99%

Monitoring Changes in the Prespa Lake Watershed Using Remote Sensing Data

Kaplan

2020

Eur. J. Geosc

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Surface water is one of the most vital Earth resources changing in time and space as a consequence of land use/land cover (LULC) changes, climate change, and other environmental factors. Timely monitoring of the water area and its surroundings is essential for policy and decision-making processes. Monitoring the land cover changes with conventional methods may consume significant time and resources. With the development of remote sensing, land cover monitoring has become practical and effective. The most practical way for monitoring the changes is with the classification of satellite imagery. The main aim of the study is to make a five-year land cover change in a watershed using remote sensing data. For this purpose, two satellite images, from RapidEye-3 and Sentinel-2 have been classified. As a study area, the Prespa Lake watershed has been selected. The Prespa Lake is situated in the Balkan Peninsula and is shared among Albania, North Macedonia, and Greece. For the purpose of the study, a semi-automated object-based classification model has been developed. The results from the classification showed significant accuracy of 93 – 96%. For future studies, it is recommended further investigation, such as using monthly satellite imagery and modeling precipitation and meteorological data.

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“…There are generally two approaches for the classification: pixel-based classification (PBC) and object-based classification (OBC). PBC analyses the spectral properties of every pixel within an area of interest, without considering contextual information, such as shape (Makinde, Salami, Olaleye, & Okewusi, 2016). This approach does not produce accurate results when analysing VHR (Toll, 1984;Xia, 2007), which is due to the high spectral variability in VHR imagery.…”

Section: Satellite Image Classification For Informal Settlement Detectionmentioning

confidence: 99%

Using open data to detect the structure and pattern of informal settlements: an outset to support inclusive SDGs’ achievement

2021

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The detection of informal settlements is the first step in planning and upgrading deprived areas in order to leave no one behind in SDGs. Very High-Resolution satellite images (VHR), have been extensively used for this purpose. However, as a cost-prohibitive data source, VHR might not be available to all, particularly nations that are home to many informal settlements. This study examines the application of open and freely available data sources to detect the structure and pattern of informal settlements. Here, in a case study of Jakarta, Indonesia, Medium Resolution satellite imagery (MR) derived from Landsat 8 (2020) was classified to detect these settlements. The classification was done using Random Forest (RF) classifier through two complementary approaches to develop the training set. In the first approach, available survey data sets (Jakarta's informal settlements map for 2015) and visual interpretation using High-Resolution Google Map imagery have been used to build the training set. Throughout the second round of classification, OpenStreetMap (OSM) layers were used as the complementary approach for training. Results from the validation test for the second round revealed better accuracy and precision in classification. The proposed method provides an opportunity to use open data for informal settlements detection, when: 1) more expensive high resolution data sources are not accessible; 2) the area of interest is not larger than a city; and 3) the physical characteristics of the settlements differ significantly from their surrounding formal area. The method presents the application of globally accessible data to help the achievement of resilience and SDGs in informal settlements.

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Object Based and Pixel Based Classification Using Rapideye Satellite Imager of ETI-OSA, Lagos, Nigeria

Cited by 26 publications

References 5 publications

Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation

Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation

Monitoring Changes in the Prespa Lake Watershed Using Remote Sensing Data

Using open data to detect the structure and pattern of informal settlements: an outset to support inclusive SDGs’ achievement

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