Enhancement of AWiFS Spatial Resolution with SVM Learning
| Autor: | C. V. Rao, V. Kamakshi Prasad, K. S. R. Radhika |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
010504 meteorology & atmospheric sciences
Computer science business.industry 0211 other engineering and technologies 02 engineering and technology Sensor fusion 01 natural sciences Image (mathematics) Support vector machine High temporal resolution Computer vision Artificial intelligence Image sensor business Image resolution 021101 geological & geomatics engineering 0105 earth and related environmental sciences |
| Zdroj: | 2016 IEEE 6th International Conference on Advanced Computing (IACC). |
| DOI: | 10.1109/iacc.2016.42 |
| Popis: | The ability of space borne sensors is limited to acquire images having wide swath with high temporal resolution and high spatial resolutions simultaneously. Fine details of the earth's surface features can be captured using high resolution (HR) remote sensors in remote sensing (RS) data collection. Data fusion from two or more sensors can be used to derive enough information to satisfy observation needs for a specified application. This work aims to predict a HR image for the corresponding low resolution (LR) image using a LR-HR image pair. Quality Assessment is performed for original HR and predicted HR images. Simulation results proved the strength of the proposed method of HR image prediction. |
| Databáze: | OpenAIRE |
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