Investigating the Ability to Identify New Constructions in Urban Areas Using Images from Unmanned Aerial Vehicles, Google Earth, and Sentinel-2

Aliabad, Fahime Arabi; Malamiri, Hamid Reza Ghafarian; Shojaei, Saeed; Sarsangi, Alireza; Ferreira, Carla Sofia Santos; Kalantari, Zahra

doi:10.3390/rs14133227

Cited by 7 publications

(4 citation statements)

References 44 publications

(46 reference statements)

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“…The object-oriented methods used in this research, from the most to the least accurate, include SVM, RF, KNN, and Bayes. Therefore, the SVM method was used to classify aerial images in this research [21]. In the present study, the DSM image and NDVI were used to increase the accuracy of aerial image classification.…”

Section: Comparison Of Object-oriented Classification Methodsmentioning

confidence: 99%

“…In this way, it is possible to effectively identify new constructions with the lowest cost and time for image processing. The full description of this method is provided in the study by Aliabad et al [21].…”

Section: Identification Of New Construction In Garden Areasmentioning

confidence: 99%

“…Compared to satellite images, the images prepared using UAVs have advantages that include better spatial resolution, the ability to take pictures whenever needed, and the ability to take pictures in any weather conditions, such as clouds [18,19]. Through the fusion of satellite images and UAV images, it is possible to achieve images with high spatial and spectral resolution [20,21].…”

Section: Introductionmentioning

confidence: 99%

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Identifying and Monitoring Gardens in Urban Areas Using Aerial and Satellite Imagery

et al. 2023

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In dry regions, gardens and trees within the urban space are of considerable significance. These gardens are facing harsh weather conditions and environmental stresses; on the other hand, due to the high value of land in urban areas, they are constantly subject to destruction and land use change. Therefore, the identification and monitoring of gardens in urban areas in dry regions and their impact on the ecosystem are the aims of this study. The data utilized are aerial and Sentinel-2 images (2018–2022) for Yazd Township in Iran. Several satellite and aerial image fusion methods were employed and compared. The root mean square error (RMSE) of horizontal shortcut connections (HSC) and color normalization (CN) were the highest compared to other methods with values of 18.37 and 17.5, respectively, while the Ehlers method showed the highest accuracy with a RMSE value of 12.3. The normalized difference vegetation index (NDVI) was then calculated using the images with 15 cm spatial resolution retrieved from the fusion. Aerial images were classified by NDVI and digital surface model (DSM) using object-oriented methods. Different object-oriented classification methods were investigated, including support vector machine (SVM), Bayes, random forest (RF), and k-nearest neighbor (KNN). SVM showed the greatest accuracy with overall accuracy (OA) and kappa of 86.2 and 0.89, respectively, followed by RF with OA and kappa of 83.1 and 0.87, respectively. Separating the gardens using NDVI, DSM, and aerial images from 2018, the images were fused in 2022, and the current status of the gardens and associated changes were classified into completely dried, drying, acceptable, and desirable conditions. It was found that gardens with a small area were more prone to destruction, and 120 buildings were built in the existing gardens in the region during 2018–2022. Moreover, the monitoring of land surface temperature (LST) showed an increase of 14 °C in the areas that were changed from gardens to buildings.

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Section: Comparison Of Object-oriented Classification Methodsmentioning

confidence: 99%

Section: Identification Of New Construction In Garden Areasmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Identifying and Monitoring Gardens in Urban Areas Using Aerial and Satellite Imagery

et al. 2023

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“…(Sitanggang, 2023) which stated that the KNN method is more accurate than the SVM method in the classi cation. Arabi Aliabad et al (2022) consider KNN and SVM among object-oriented classi cation methods more accurate than others. Phiri et al (2020) used the results of 25 studies in classifying Sentinel-2 images and reported that the SVM and Bayes method has better accuracy for classi cation purposes.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the ability to measure morphological structures of plants obtained from tissue culture applying image processing techniques

Aliabad,

aliabad,

Habbab

et al. 2023

Preprint

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Biotechnological approaches, for instance, plant tissue culture, can be used to improve and accelerate the reproduction of plants. A single portion of a plant can produce many plants throughout the year in a relatively short period of laboratory conditions. Monitoring and recording plant morphological characteristics such as root length and shoot length in different conditions and stages are necessary for tissue culture. These features were measured using graph paper in a laboratory environment and sterile conditions. This research investigated the ability to use image processing techniques in determining the morphological features of plants obtained from tissue culture. In this context RGB images were prepared from the plants inside the glass, and different pixel-based and object-based classification methods were applied to an image as a control. The accuracy of these methods was evaluated using the kappa coefficient, and overall accuracy was obtained from Boolean logic. The results showed that among pixel-based classification methods, the maximum likelihood method with a kappa coefficient of 87% and overall accuracy of 89.4 was the most accurate, and the Spectral angle mapper method (SAM) method with a kappa coefficient of 58% and overall accuracy of 54.6 was the least accurate. Also, among object-based classification methods, Support Vector Machine (SVM), Naïve Bayes, and K-nearest neighbors algorithm (KNN) techniques, with a Kappa coefficient of 88% and overall accuracy of 90, can effectively distinguish the cultivation environment, plant, and root. Comparing the values of root length and shoot length estimated in the laboratory culture environment with the values obtained from image processing showed that the use of the SVM image classification method, which is capable of estimating root length and shoot length with RMSE 2.4, MAD 3.01 and R2 0.97, matches the results of manual measurements with even higher accuracy.

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Path selection by topographic analysis: vector re-classification versus raster fuzzification as spatial multi-criteria using cost-path

Jamali,

Esmailian,

Mokhtarisabet

et al. 2023

Spat. Inf. Res.

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Investigating the Ability to Identify New Constructions in Urban Areas Using Images from Unmanned Aerial Vehicles, Google Earth, and Sentinel-2

Cited by 7 publications

References 44 publications

Identifying and Monitoring Gardens in Urban Areas Using Aerial and Satellite Imagery

Identifying and Monitoring Gardens in Urban Areas Using Aerial and Satellite Imagery

Evaluation of the ability to measure morphological structures of plants obtained from tissue culture applying image processing techniques

Path selection by topographic analysis: vector re-classification versus raster fuzzification as spatial multi-criteria using cost-path

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