期刊
JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
卷 57, 期 3, 页码 423-446出版社
SPRINGER
DOI: 10.1007/s10844-021-00656-7
关键词
Saliency detection; Remote sensing; Imagery data analysis; Earth's observation
资金
- Universita degli Studi di Bari Aldo Moro within the CRUICARE Agreement
Saliency detection in hyperspectral imaging, mimicking the natural visual attention mechanism, has room for improvement despite existing models. An ensemble learning methodology leveraging spectral information from multiple images shows promising results in enhancing saliency detection performance.
Saliency detection mimics the natural visual attention mechanism that identifies an imagery region to be salient when it attracts visual attention more than the background. This image analysis task covers many important applications in several fields such as military science, ocean research, resources exploration, disaster and land-use monitoring tasks. Despite hundreds of models have been proposed for saliency detection in colour images, there is still a large room for improving saliency detection performances in hyperspectral imaging analysis. In the present study, an ensemble learning methodology for saliency detection in hyperspectral imagery datasets is presented. It enhances saliency assignments yielded through a robust colour-based technique with new saliency information extracted by taking advantage of the abundance of spectral information on multiple hyperspectral images. The experiments performed with the proposed methodology provide encouraging results, also compared to several competitors.
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