التفاصيل البيبلوغرافية
| العنوان: |
Advanced machine learning techniques for satellite image processing. |
| المؤلفون: |
Kumaraswamy, Eelandula, Kommabatla, Mahender, Reddy, I. Rajasri, Karre, Ravikiran, Kasanagottu, Srinivas, Ramu, Moola |
| المصدر: |
AIP Conference Proceedings; 2024, Vol. 2971 Issue 1, p1-7, 7p |
| مصطلحات موضوعية: |
REMOTE-sensing images, IMAGE processing, MACHINE learning, IMAGE analysis, REMOTE sensing, HAZARD mitigation, EARTHQUAKE resistant design, DIGITAL image processing, WILDFIRE prevention |
| مستخلص: |
Satellite images mainly utilized in the events of a natural disaster management, identifying geographical information, viz land cover classes namely, buildings, roads, vegetation, water, agriculture land, crop types, plants, bare ground, cities, atmosphere conditions. Machine Learning (ML) approaches have been utilized effectively to develop a model for classification, detection, and segmentation tasks. Therefore, Satellite image processing and analysis purpose, ML techniques plays vital role and remotely sensed data become essential while training the model. The aim of this study is to investigate the various of ML techniques in satellite image analysis. However, to predict the various events in advance across the globe, it is necessary to focus more on remote sensed data and data processing techniques for accurate classification. Even though remote sensing quality has been increased and artificial intelligence solutions are equally increased. This paper addressed various types of advanced ML techniques utilized in the classification and assessment of satellite images and used to track the earthquakes, faulting, landslides, floodings, wildfire, and hazards associated with the stated activities. Still there is a gap and interference in the approaches and it is important to fill the gap by thorough review of recent classification approaches. In this connection it is necessary to look in depth to the state-of-the-art ML techniques of satellite image processing. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
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