| Περίληψη: | Multispectral imaging and the derived spectral analysis of the natural scenes constitutes a remarkable tool for revealing and capturing beneficial information for a variety of applications e.g., precision agriculture, medical imaging and autonomous-driving. Contrary to mainstream RGB cameras that able to capture information derived only from three spectral bands limited to the visible electromagnetic spectrum, multispectral cameras can provide much more detailed spectral resolution, utilizing the underlying information that lies inside the visible and the near infrared spectrum. However, the high spectral resolution is accompanied with several key limitations such as, the cost of the multispectral cameras is very high and they exhibit various mobility limitations due to their weight and the need
for special hardware equipment. Considering these practical drawbacks, we develop two shallow learning domain adaptation methods based on the coupled dictionary and coupled autoencoders learning in order to estimate spectral information using only RGB signals derived from a commercial RGB camera. We argue that this approach is very attractive and cost-effective alternative, especially in real-field applications. Extensive
experiments with real data demonstrate the effectiveness and applicability of the proposed method in the precision agriculture
domain. To this end, we calculate one of the most widely used vegetation indices, the normalized difference vegetation index
(NVDI), which may be used for plant health monitoring.
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