A Development of a Robust Machine for Removing Irregular Noise with the Intelligent System of Auto-Encoder for Image Classification of Coastal Waste
Currently, the seashore is threatened by the environment of climate change and increasing coastal waste. The past environmental groups used a large amount of manpower to manage the coast to maintain the seashore environment. The computational time cost and efficiency are not ideal for the vast area...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Environments |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3298/9/9/114 |
| _version_ | 1797488613272846336 |
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| author | Shiuan Wan Tsu Chiang Lei |
| author_facet | Shiuan Wan Tsu Chiang Lei |
| author_sort | Shiuan Wan |
| collection | DOAJ |
| description | Currently, the seashore is threatened by the environment of climate change and increasing coastal waste. The past environmental groups used a large amount of manpower to manage the coast to maintain the seashore environment. The computational time cost and efficiency are not ideal for the vast area of the seashore. With the progress of GIS (Geographic Information System) technology, the ability of remote sensing technology can capture a wide range of data in a short period. This research is based on the application of remote sensing technology combined with machine learning to display the observation of our seashore. However, in the process of image classification, the seashore wastes are small, which required the use of high-resolution image data. Thus, how to remove the noise becomes a crucial issue in developing an image classifier machine. The difficulties include how to adjust the value of parameters for removing/avoiding noises. First, the texture information and vegetation indices were employed as ancillary information in our image classification. On the other hand, auto-encoder is a very good tool to denoise a given image; hence, it is used to transform high-resolution images by considering ancillary information to extract attributes. Multi-layer perceptron (MLP) and support vector machine (SVM) were compared for classifier performance in a parallel study. The overall accuracy is about 85.5% and 83.9% for MLP and SVM, respectively. If the AE is applied for preprocessing, the overall accuracy is increased by about 10–12%. |
| first_indexed | 2024-03-10T00:05:45Z |
| format | Article |
| id | doaj.art-df25daae7deb4028a355f915cf4db9f4 |
| institution | Directory Open Access Journal |
| issn | 2076-3298 |
| language | English |
| last_indexed | 2024-03-10T00:05:45Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Environments |
| spelling | doaj.art-df25daae7deb4028a355f915cf4db9f42023-11-23T16:09:49ZengMDPI AGEnvironments2076-32982022-09-019911410.3390/environments9090114A Development of a Robust Machine for Removing Irregular Noise with the Intelligent System of Auto-Encoder for Image Classification of Coastal WasteShiuan Wan0Tsu Chiang Lei1Department of Information Technology, Ling Tung University, Taichung 40851, TaiwanDepartment of Urban Planning and Spatial Information, Feng Chia University, Taichung 40724, TaiwanCurrently, the seashore is threatened by the environment of climate change and increasing coastal waste. The past environmental groups used a large amount of manpower to manage the coast to maintain the seashore environment. The computational time cost and efficiency are not ideal for the vast area of the seashore. With the progress of GIS (Geographic Information System) technology, the ability of remote sensing technology can capture a wide range of data in a short period. This research is based on the application of remote sensing technology combined with machine learning to display the observation of our seashore. However, in the process of image classification, the seashore wastes are small, which required the use of high-resolution image data. Thus, how to remove the noise becomes a crucial issue in developing an image classifier machine. The difficulties include how to adjust the value of parameters for removing/avoiding noises. First, the texture information and vegetation indices were employed as ancillary information in our image classification. On the other hand, auto-encoder is a very good tool to denoise a given image; hence, it is used to transform high-resolution images by considering ancillary information to extract attributes. Multi-layer perceptron (MLP) and support vector machine (SVM) were compared for classifier performance in a parallel study. The overall accuracy is about 85.5% and 83.9% for MLP and SVM, respectively. If the AE is applied for preprocessing, the overall accuracy is increased by about 10–12%.https://www.mdpi.com/2076-3298/9/9/114coastal wasteimage classificationauto-encoderimage denoise |
| spellingShingle | Shiuan Wan Tsu Chiang Lei A Development of a Robust Machine for Removing Irregular Noise with the Intelligent System of Auto-Encoder for Image Classification of Coastal Waste Environments coastal waste image classification auto-encoder image denoise |
| title | A Development of a Robust Machine for Removing Irregular Noise with the Intelligent System of Auto-Encoder for Image Classification of Coastal Waste |
| title_full | A Development of a Robust Machine for Removing Irregular Noise with the Intelligent System of Auto-Encoder for Image Classification of Coastal Waste |
| title_fullStr | A Development of a Robust Machine for Removing Irregular Noise with the Intelligent System of Auto-Encoder for Image Classification of Coastal Waste |
| title_full_unstemmed | A Development of a Robust Machine for Removing Irregular Noise with the Intelligent System of Auto-Encoder for Image Classification of Coastal Waste |
| title_short | A Development of a Robust Machine for Removing Irregular Noise with the Intelligent System of Auto-Encoder for Image Classification of Coastal Waste |
| title_sort | development of a robust machine for removing irregular noise with the intelligent system of auto encoder for image classification of coastal waste |
| topic | coastal waste image classification auto-encoder image denoise |
| url | https://www.mdpi.com/2076-3298/9/9/114 |
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