Satellite Image Categorization Using Scalable Deep Learning
Detecting and classifying objects from satellite images are crucial for many applications, ranging from marine monitoring to land planning, ecology to warfare, etc. Spatial and temporal information-rich satellite images are exploited in a variety of manners to solve many real-world remote sensing pr...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/8/5108 |
| _version_ | 1797606486262677504 |
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| author | Samabia Tehsin Sumaira Kausar Amina Jameel Mamoona Humayun Deemah Khalaf Almofarreh |
| author_facet | Samabia Tehsin Sumaira Kausar Amina Jameel Mamoona Humayun Deemah Khalaf Almofarreh |
| author_sort | Samabia Tehsin |
| collection | DOAJ |
| description | Detecting and classifying objects from satellite images are crucial for many applications, ranging from marine monitoring to land planning, ecology to warfare, etc. Spatial and temporal information-rich satellite images are exploited in a variety of manners to solve many real-world remote sensing problems. Satellite image classification has many associated challenges. These challenges include data availability, the quality of data, the quantity of data, and data distribution. These challenges make the analysis of satellite images more challenging. A convolutional neural network architecture with a scaling method is proposed for the classification of satellite images. The scaling method can evenly scale all dimensions of depth, width, and resolution using a compound coefficient. It can be used as a preliminary task in urban planning, satellite surveillance, monitoring, etc. It can also be helpful in geo-information and maritime monitoring systems. The proposed methodology is based on an end-to-end, scalable satellite image interpretation. It uses spatial information from satellite images to categorize these into four categories. The proposed method gives encouraging and promising results on a challenging dataset with a high inter-class similarity and intra-class variation. The proposed method shows 99.64% accuracy on the RSI-CB256 dataset. |
| first_indexed | 2024-03-11T05:15:52Z |
| format | Article |
| id | doaj.art-4f964c2383a64981bb704f2da8d02151 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T05:15:52Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4f964c2383a64981bb704f2da8d021512023-11-17T18:13:46ZengMDPI AGApplied Sciences2076-34172023-04-01138510810.3390/app13085108Satellite Image Categorization Using Scalable Deep LearningSamabia Tehsin0Sumaira Kausar1Amina Jameel2Mamoona Humayun3Deemah Khalaf Almofarreh4Department of Computer Science, Bahria University, Karachi 75260, PakistanDepartment of Computer Science, Bahria University, Islamabad 44220, PakistanDepartment of Computer Engineering, Bahria University, Islamabad 44220, PakistanDepartment of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi ArabiaDepartment of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi ArabiaDetecting and classifying objects from satellite images are crucial for many applications, ranging from marine monitoring to land planning, ecology to warfare, etc. Spatial and temporal information-rich satellite images are exploited in a variety of manners to solve many real-world remote sensing problems. Satellite image classification has many associated challenges. These challenges include data availability, the quality of data, the quantity of data, and data distribution. These challenges make the analysis of satellite images more challenging. A convolutional neural network architecture with a scaling method is proposed for the classification of satellite images. The scaling method can evenly scale all dimensions of depth, width, and resolution using a compound coefficient. It can be used as a preliminary task in urban planning, satellite surveillance, monitoring, etc. It can also be helpful in geo-information and maritime monitoring systems. The proposed methodology is based on an end-to-end, scalable satellite image interpretation. It uses spatial information from satellite images to categorize these into four categories. The proposed method gives encouraging and promising results on a challenging dataset with a high inter-class similarity and intra-class variation. The proposed method shows 99.64% accuracy on the RSI-CB256 dataset.https://www.mdpi.com/2076-3417/13/8/5108maritime monitoringsatellite imagerydeep learningremote sensingimage classification |
| spellingShingle | Samabia Tehsin Sumaira Kausar Amina Jameel Mamoona Humayun Deemah Khalaf Almofarreh Satellite Image Categorization Using Scalable Deep Learning Applied Sciences maritime monitoring satellite imagery deep learning remote sensing image classification |
| title | Satellite Image Categorization Using Scalable Deep Learning |
| title_full | Satellite Image Categorization Using Scalable Deep Learning |
| title_fullStr | Satellite Image Categorization Using Scalable Deep Learning |
| title_full_unstemmed | Satellite Image Categorization Using Scalable Deep Learning |
| title_short | Satellite Image Categorization Using Scalable Deep Learning |
| title_sort | satellite image categorization using scalable deep learning |
| topic | maritime monitoring satellite imagery deep learning remote sensing image classification |
| url | https://www.mdpi.com/2076-3417/13/8/5108 |
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