A Study on Wetland Cover Map Formulation and Evaluation Using Unmanned Aerial Vehicle High-Resolution Images
Wetlands possess significant ecological value and play a crucial role in the environment. Recent advancements in remote exploration technology have enabled a quantitative analysis of wetlands through surveys on the type of cover present. However, the classification of complex cover types as land cov...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/7/8/536 |
| _version_ | 1797584998148079616 |
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| author | Tai Yang Lim Jiyun Kim Wheemoon Kim Wonkyong Song |
| author_facet | Tai Yang Lim Jiyun Kim Wheemoon Kim Wonkyong Song |
| author_sort | Tai Yang Lim |
| collection | DOAJ |
| description | Wetlands possess significant ecological value and play a crucial role in the environment. Recent advancements in remote exploration technology have enabled a quantitative analysis of wetlands through surveys on the type of cover present. However, the classification of complex cover types as land cover types in wetlands remains challenging, leading to ongoing studies aimed at addressing this issue. With the advent of high-resolution sensors in unmanned aerial vehicles (UAVs), researchers can now obtain detailed data and utilize them for their investigations. In this paper, we sought to establish an effective method for classifying centimeter-scale images using multispectral and hyperspectral techniques. Since there are numerous classes of land cover types, it is important to build and extract effective training data for each type. In addition, computer vision-based methods, especially those that combine deep learning and machine learning, are attracting considerable attention as high-accuracy methods. Collecting training data before classifying by cover type is an important factor that which requires effective data sampling. To obtain accurate detection results, a few data sampling techniques must be tested. In this study, we employed two data sampling methods (endmember and pixel sampling) to acquire data, after which their accuracy and detection outcomes were compared through classification using spectral angle mapper (SAM), support vector machine (SVM), and artificial neural network (ANN) approaches. Our findings confirmed the effectiveness of the pixel-based sampling method, demonstrating a notable difference of 38.62% compared to the endmember sampling method. Moreover, among the classification methods employed, the SAM technique exhibited the highest effectiveness, with approximately 10% disparity observed in multispectral data and 7.15% in hyperspectral data compared to the other models. Our findings provide insights into the accuracy and classification outcomes of different models based on the sampling method employed in spectral imagery. |
| first_indexed | 2024-03-11T00:00:29Z |
| format | Article |
| id | doaj.art-a72f81bc3c8f44048d017ae2b71d4bb2 |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-11T00:00:29Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-a72f81bc3c8f44048d017ae2b71d4bb22023-11-19T00:50:43ZengMDPI AGDrones2504-446X2023-08-017853610.3390/drones7080536A Study on Wetland Cover Map Formulation and Evaluation Using Unmanned Aerial Vehicle High-Resolution ImagesTai Yang Lim0Jiyun Kim1Wheemoon Kim2Wonkyong Song3Green & Landscape Architecture, Dankook University, Cheonan 31116, Republic of KoreaGreen & Landscape Architecture, Dankook University, Cheonan 31116, Republic of KoreaGreen & Landscape Architecture, Dankook University, Cheonan 31116, Republic of KoreaGreen & Landscape Architecture, Dankook University, Cheonan 31116, Republic of KoreaWetlands possess significant ecological value and play a crucial role in the environment. Recent advancements in remote exploration technology have enabled a quantitative analysis of wetlands through surveys on the type of cover present. However, the classification of complex cover types as land cover types in wetlands remains challenging, leading to ongoing studies aimed at addressing this issue. With the advent of high-resolution sensors in unmanned aerial vehicles (UAVs), researchers can now obtain detailed data and utilize them for their investigations. In this paper, we sought to establish an effective method for classifying centimeter-scale images using multispectral and hyperspectral techniques. Since there are numerous classes of land cover types, it is important to build and extract effective training data for each type. In addition, computer vision-based methods, especially those that combine deep learning and machine learning, are attracting considerable attention as high-accuracy methods. Collecting training data before classifying by cover type is an important factor that which requires effective data sampling. To obtain accurate detection results, a few data sampling techniques must be tested. In this study, we employed two data sampling methods (endmember and pixel sampling) to acquire data, after which their accuracy and detection outcomes were compared through classification using spectral angle mapper (SAM), support vector machine (SVM), and artificial neural network (ANN) approaches. Our findings confirmed the effectiveness of the pixel-based sampling method, demonstrating a notable difference of 38.62% compared to the endmember sampling method. Moreover, among the classification methods employed, the SAM technique exhibited the highest effectiveness, with approximately 10% disparity observed in multispectral data and 7.15% in hyperspectral data compared to the other models. Our findings provide insights into the accuracy and classification outcomes of different models based on the sampling method employed in spectral imagery.https://www.mdpi.com/2504-446X/7/8/536hyperspectralspectral angle mappersupport vector machineneural netdronewetland classification |
| spellingShingle | Tai Yang Lim Jiyun Kim Wheemoon Kim Wonkyong Song A Study on Wetland Cover Map Formulation and Evaluation Using Unmanned Aerial Vehicle High-Resolution Images Drones hyperspectral spectral angle mapper support vector machine neural net drone wetland classification |
| title | A Study on Wetland Cover Map Formulation and Evaluation Using Unmanned Aerial Vehicle High-Resolution Images |
| title_full | A Study on Wetland Cover Map Formulation and Evaluation Using Unmanned Aerial Vehicle High-Resolution Images |
| title_fullStr | A Study on Wetland Cover Map Formulation and Evaluation Using Unmanned Aerial Vehicle High-Resolution Images |
| title_full_unstemmed | A Study on Wetland Cover Map Formulation and Evaluation Using Unmanned Aerial Vehicle High-Resolution Images |
| title_short | A Study on Wetland Cover Map Formulation and Evaluation Using Unmanned Aerial Vehicle High-Resolution Images |
| title_sort | study on wetland cover map formulation and evaluation using unmanned aerial vehicle high resolution images |
| topic | hyperspectral spectral angle mapper support vector machine neural net drone wetland classification |
| url | https://www.mdpi.com/2504-446X/7/8/536 |
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