Performance evaluation of sentinel-2 and landsat 8 OLI data for land cover/use classification using a comparison between machine learning algorithms
With the development of remote sensing algorithms and increased access to satellite data, generating up-to-date, accurate land use/land cover (LULC) maps has become increasingly feasible for evaluating and managing changes in land cover as created by changes to ecosystem and land use. The main objec...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI
2021
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| Online Access: | http://eprints.utm.my/95756/1/AnuarAhmad2021_PerformanceEvaluationofSentinel2andLandsat8.pdf |
| _version_ | 1796866085939576832 |
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| author | Ghayour, Laleh Neshat, Aminreza Paryani, Sina Shahabi, Himan Shirzadi, Ataollah Chen, Wei Al-Ansari, Nadhir Geertsema, Marten Amiri, Mehdi Pourmehdi Gholamnia, Mehdi Dou, Jie Ahmad, Anuar |
| author_facet | Ghayour, Laleh Neshat, Aminreza Paryani, Sina Shahabi, Himan Shirzadi, Ataollah Chen, Wei Al-Ansari, Nadhir Geertsema, Marten Amiri, Mehdi Pourmehdi Gholamnia, Mehdi Dou, Jie Ahmad, Anuar |
| author_sort | Ghayour, Laleh |
| collection | ePrints |
| description | With the development of remote sensing algorithms and increased access to satellite data, generating up-to-date, accurate land use/land cover (LULC) maps has become increasingly feasible for evaluating and managing changes in land cover as created by changes to ecosystem and land use. The main objective of our study is to evaluate the performance of Support Vector Machine (SVM), Artificial Neural Network (ANN), Maximum Likelihood Classification (MLC), Minimum Distance (MD), and Mahalanobis (MH) algorithms and compare them in order to generate a LULC map using data from Sentinel 2 and Landsat 8 satellites. Further, we also investigate the effect of a penalty parameter on SVM results. Our study uses different kernel functions and hidden layers for SVM and ANN algorithms, respectively. We generated the training and validation datasets from Google Earth images and GPS data prior to pre-processing satellite data. In the next phase, we classified the images using training data and algorithms. Ultimately, to evaluate outcomes, we used the validation data to generate a confusion matrix of the classified images. Our results showed that with optimal tuning parameters, the SVM classifier yielded the highest overall accuracy (OA) of 94%, performing better for both satellite data compared to other methods. In addition, for our scenes, Sentinel 2 date was slightly more accurate compared to Landsat 8. The parametric algorithms MD and MLC provided the lowest accuracy of 80.85% and 74.68% for the data from Sentinel 2 and Landsat 8. In contrast, our evaluation using the SVM tuning parameters showed that the linear kernel with the penalty parameter 150 for Sentinel 2 and the penalty parameter 200 for Landsat 8 yielded the highest accuracies. Further, ANN classification showed that increasing the hidden layers drastically reduces classification accuracy for both datasets, reducing zero for three hidden layers. |
| first_indexed | 2024-03-05T21:06:54Z |
| format | Article |
| id | utm.eprints-95756 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T21:06:54Z |
| publishDate | 2021 |
| publisher | MDPI |
| record_format | dspace |
| spelling | utm.eprints-957562022-05-31T13:18:46Z http://eprints.utm.my/95756/ Performance evaluation of sentinel-2 and landsat 8 OLI data for land cover/use classification using a comparison between machine learning algorithms Ghayour, Laleh Neshat, Aminreza Paryani, Sina Shahabi, Himan Shirzadi, Ataollah Chen, Wei Al-Ansari, Nadhir Geertsema, Marten Amiri, Mehdi Pourmehdi Gholamnia, Mehdi Dou, Jie Ahmad, Anuar G70.39-70.6 Remote sensing With the development of remote sensing algorithms and increased access to satellite data, generating up-to-date, accurate land use/land cover (LULC) maps has become increasingly feasible for evaluating and managing changes in land cover as created by changes to ecosystem and land use. The main objective of our study is to evaluate the performance of Support Vector Machine (SVM), Artificial Neural Network (ANN), Maximum Likelihood Classification (MLC), Minimum Distance (MD), and Mahalanobis (MH) algorithms and compare them in order to generate a LULC map using data from Sentinel 2 and Landsat 8 satellites. Further, we also investigate the effect of a penalty parameter on SVM results. Our study uses different kernel functions and hidden layers for SVM and ANN algorithms, respectively. We generated the training and validation datasets from Google Earth images and GPS data prior to pre-processing satellite data. In the next phase, we classified the images using training data and algorithms. Ultimately, to evaluate outcomes, we used the validation data to generate a confusion matrix of the classified images. Our results showed that with optimal tuning parameters, the SVM classifier yielded the highest overall accuracy (OA) of 94%, performing better for both satellite data compared to other methods. In addition, for our scenes, Sentinel 2 date was slightly more accurate compared to Landsat 8. The parametric algorithms MD and MLC provided the lowest accuracy of 80.85% and 74.68% for the data from Sentinel 2 and Landsat 8. In contrast, our evaluation using the SVM tuning parameters showed that the linear kernel with the penalty parameter 150 for Sentinel 2 and the penalty parameter 200 for Landsat 8 yielded the highest accuracies. Further, ANN classification showed that increasing the hidden layers drastically reduces classification accuracy for both datasets, reducing zero for three hidden layers. MDPI 2021-04-01 Article PeerReviewed application/pdf en http://eprints.utm.my/95756/1/AnuarAhmad2021_PerformanceEvaluationofSentinel2andLandsat8.pdf Ghayour, Laleh and Neshat, Aminreza and Paryani, Sina and Shahabi, Himan and Shirzadi, Ataollah and Chen, Wei and Al-Ansari, Nadhir and Geertsema, Marten and Amiri, Mehdi Pourmehdi and Gholamnia, Mehdi and Dou, Jie and Ahmad, Anuar (2021) Performance evaluation of sentinel-2 and landsat 8 OLI data for land cover/use classification using a comparison between machine learning algorithms. Remote Sensing, 13 (7). pp. 1-21. ISSN 2072-4292 http://dx.doi.org/10.3390/rs13071349 DOI:10.3390/rs13071349 |
| spellingShingle | G70.39-70.6 Remote sensing Ghayour, Laleh Neshat, Aminreza Paryani, Sina Shahabi, Himan Shirzadi, Ataollah Chen, Wei Al-Ansari, Nadhir Geertsema, Marten Amiri, Mehdi Pourmehdi Gholamnia, Mehdi Dou, Jie Ahmad, Anuar Performance evaluation of sentinel-2 and landsat 8 OLI data for land cover/use classification using a comparison between machine learning algorithms |
| title | Performance evaluation of sentinel-2 and landsat 8 OLI data for land cover/use classification using a comparison between machine learning algorithms |
| title_full | Performance evaluation of sentinel-2 and landsat 8 OLI data for land cover/use classification using a comparison between machine learning algorithms |
| title_fullStr | Performance evaluation of sentinel-2 and landsat 8 OLI data for land cover/use classification using a comparison between machine learning algorithms |
| title_full_unstemmed | Performance evaluation of sentinel-2 and landsat 8 OLI data for land cover/use classification using a comparison between machine learning algorithms |
| title_short | Performance evaluation of sentinel-2 and landsat 8 OLI data for land cover/use classification using a comparison between machine learning algorithms |
| title_sort | performance evaluation of sentinel 2 and landsat 8 oli data for land cover use classification using a comparison between machine learning algorithms |
| topic | G70.39-70.6 Remote sensing |
| url | http://eprints.utm.my/95756/1/AnuarAhmad2021_PerformanceEvaluationofSentinel2andLandsat8.pdf |
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