Land-Use and Land-Cover Classification Using a Human Group-Based Particle Swarm Optimization Algorithm with an LSTM Classifier on Hybrid Pre-Processing Remote-Sensing Images
Land-use and land-cover (LULC) classification using remote sensing imagery plays a vital role in many environment modeling and land-use inventories. In this study, a hybrid feature optimization algorithm along with a deep learning classifier is proposed to improve the performance of LULC classificat...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/24/4135 |
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| author | Ganesh B. Rajendran Uma M. Kumarasamy Chiara Zarro Parameshachari B. Divakarachari Silvia L. Ullo |
| author_facet | Ganesh B. Rajendran Uma M. Kumarasamy Chiara Zarro Parameshachari B. Divakarachari Silvia L. Ullo |
| author_sort | Ganesh B. Rajendran |
| collection | DOAJ |
| description | Land-use and land-cover (LULC) classification using remote sensing imagery plays a vital role in many environment modeling and land-use inventories. In this study, a hybrid feature optimization algorithm along with a deep learning classifier is proposed to improve the performance of LULC classification, helping to predict wildlife habitat, deteriorating environmental quality, haphazard elements, etc. LULC classification is assessed using Sat 4, Sat 6 and Eurosat datasets. After the selection of remote-sensing images, normalization and histogram equalization methods are used to improve the quality of the images. Then, a hybrid optimization is accomplished by using the local Gabor binary pattern histogram sequence (LGBPHS), the histogram of oriented gradient (HOG) and Haralick texture features, for the feature extraction from the selected images. The benefits of this hybrid optimization are a high discriminative power and invariance to color and grayscale images. Next, a human group-based particle swarm optimization (PSO) algorithm is applied to select the optimal features, whose benefits are a fast convergence rate and ease of implementation. After selecting the optimal feature values, a long short-term memory (LSTM) network is utilized to classify the LULC classes. Experimental results showed that the human group-based PSO algorithm with a LSTM classifier effectively well differentiates the LULC classes in terms of classification accuracy, recall and precision. A maximum improvement of 6.03% on Sat 4 and 7.17% on Sat 6 in LULC classification is reached when the proposed human group-based PSO with LSTM is compared to individual LSTM, PSO with LSTM, and Human Group Optimization (HGO) with LSTM. Moreover, an improvement of 2.56% in accuracy is achieved, compared to the existing models, GoogleNet, Visual Geometric Group (VGG), AlexNet, ConvNet, when the proposed method is applied. |
| first_indexed | 2024-03-10T13:59:02Z |
| format | Article |
| id | doaj.art-56717fb92c8b48f18d1b7a50112eeac4 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T13:59:02Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-56717fb92c8b48f18d1b7a50112eeac42023-11-21T01:20:04ZengMDPI AGRemote Sensing2072-42922020-12-011224413510.3390/rs12244135Land-Use and Land-Cover Classification Using a Human Group-Based Particle Swarm Optimization Algorithm with an LSTM Classifier on Hybrid Pre-Processing Remote-Sensing ImagesGanesh B. Rajendran0Uma M. Kumarasamy1Chiara Zarro2Parameshachari B. Divakarachari3Silvia L. Ullo4Department of Electronics and Communication Engineering, SRM TRP Engineering College, Tiruchirappalli 621105, Tamil Nadu, IndiaDepartment of Electronics and Communication Engineering, SRM TRP Engineering College, Tiruchirappalli 621105, Tamil Nadu, IndiaEngineering Department, University of Sannio, 82100 Benevento, ItalyDepartment of Telecommunication Engineering, GSSS Institute of Engineering and Technology for Women, Mysuru 570016, Karnataka, IndiaEngineering Department, University of Sannio, 82100 Benevento, ItalyLand-use and land-cover (LULC) classification using remote sensing imagery plays a vital role in many environment modeling and land-use inventories. In this study, a hybrid feature optimization algorithm along with a deep learning classifier is proposed to improve the performance of LULC classification, helping to predict wildlife habitat, deteriorating environmental quality, haphazard elements, etc. LULC classification is assessed using Sat 4, Sat 6 and Eurosat datasets. After the selection of remote-sensing images, normalization and histogram equalization methods are used to improve the quality of the images. Then, a hybrid optimization is accomplished by using the local Gabor binary pattern histogram sequence (LGBPHS), the histogram of oriented gradient (HOG) and Haralick texture features, for the feature extraction from the selected images. The benefits of this hybrid optimization are a high discriminative power and invariance to color and grayscale images. Next, a human group-based particle swarm optimization (PSO) algorithm is applied to select the optimal features, whose benefits are a fast convergence rate and ease of implementation. After selecting the optimal feature values, a long short-term memory (LSTM) network is utilized to classify the LULC classes. Experimental results showed that the human group-based PSO algorithm with a LSTM classifier effectively well differentiates the LULC classes in terms of classification accuracy, recall and precision. A maximum improvement of 6.03% on Sat 4 and 7.17% on Sat 6 in LULC classification is reached when the proposed human group-based PSO with LSTM is compared to individual LSTM, PSO with LSTM, and Human Group Optimization (HGO) with LSTM. Moreover, an improvement of 2.56% in accuracy is achieved, compared to the existing models, GoogleNet, Visual Geometric Group (VGG), AlexNet, ConvNet, when the proposed method is applied.https://www.mdpi.com/2072-4292/12/24/4135land-use and land-cover classificationHaralick texture featurehistogram of oriented gradienthuman group optimizationlocal gabor binary pattern histogram sequencelong short term memory network |
| spellingShingle | Ganesh B. Rajendran Uma M. Kumarasamy Chiara Zarro Parameshachari B. Divakarachari Silvia L. Ullo Land-Use and Land-Cover Classification Using a Human Group-Based Particle Swarm Optimization Algorithm with an LSTM Classifier on Hybrid Pre-Processing Remote-Sensing Images Remote Sensing land-use and land-cover classification Haralick texture feature histogram of oriented gradient human group optimization local gabor binary pattern histogram sequence long short term memory network |
| title | Land-Use and Land-Cover Classification Using a Human Group-Based Particle Swarm Optimization Algorithm with an LSTM Classifier on Hybrid Pre-Processing Remote-Sensing Images |
| title_full | Land-Use and Land-Cover Classification Using a Human Group-Based Particle Swarm Optimization Algorithm with an LSTM Classifier on Hybrid Pre-Processing Remote-Sensing Images |
| title_fullStr | Land-Use and Land-Cover Classification Using a Human Group-Based Particle Swarm Optimization Algorithm with an LSTM Classifier on Hybrid Pre-Processing Remote-Sensing Images |
| title_full_unstemmed | Land-Use and Land-Cover Classification Using a Human Group-Based Particle Swarm Optimization Algorithm with an LSTM Classifier on Hybrid Pre-Processing Remote-Sensing Images |
| title_short | Land-Use and Land-Cover Classification Using a Human Group-Based Particle Swarm Optimization Algorithm with an LSTM Classifier on Hybrid Pre-Processing Remote-Sensing Images |
| title_sort | land use and land cover classification using a human group based particle swarm optimization algorithm with an lstm classifier on hybrid pre processing remote sensing images |
| topic | land-use and land-cover classification Haralick texture feature histogram of oriented gradient human group optimization local gabor binary pattern histogram sequence long short term memory network |
| url | https://www.mdpi.com/2072-4292/12/24/4135 |
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