Friday, May 13, 2016

Follow-up to 'Making Hyperspectral Imaging Mainstream'

Following up on this morning hyperspectral approach, here are some feedback from my proposal about Making Hyperspectral Imaging Mainstream?

Do you remember it ? No, well go read it, I'll wait....  Ximea, the maker of hyperspectral cameras, is still pondering the issue but I got a few very good interactions out of that idea. Here they are:

From the blog comment section:
Harrison Knoll said...
This is a great idea! We here at Aerial Agriculture have been collecting hyperspectral data and will be following your progress. Let us know if there is anything you need! ~Harrison
Someone from Movidius  said...
Great idea - Movidius would be very supportive
Alex St. John said...
Agreed, hyperspectral space is the place for next-level analysis!

Also from Aerial Agriculture here, and am interested to follow up on this kind of project and continue building.


Indir Jaganjac 

Igor, see these hyperspectral images of natural scenes at Manchaster University site: Scenes were illuminated by direct sunlight in clear or almost clear sky. Estimated reflectance spectra (effective spectral reflectances) at each pixel in each of scenes images can be downloaded ((1017x1338x33 Matlab array). Hyperspectral imaging system that was used to acquire scene reflectances was based on low-noise Peltier-cooled digital camera providing spatial resolution of 1344x1024 pixels (Hamamatsu, model C4742-95-12ER) with fast tunable liquid-crystal filter. 

Kyle Forbes 
Experienced Software and Data Leader
That's why I started, leveraging machine learning with hyper spectral and other spatial data to address information challenges in agriculture. 

Very interesting !

All hyperspectral related blog entries are under the hyperspectral tag.

In other news, here is: Image-level Classification in Hyperspectral Images using Feature Descriptors, with Application to Face Recognition  by Vivek Sharma, Luc Van Gool

In this paper, we proposed a novel pipeline for image-level classification in the hyperspectral images. By doing this, we show that the discriminative spectral information at image-level features lead to significantly improved performance in a face recognition task. We also explored the potential of traditional feature descriptors in the hyperspectral images. From our evaluations, we observe that SIFT features outperform the state-of-the-art hyperspectral face recognition methods, and also the other descriptors. With the increasing deployment of hyperspectral sensors in a multitude of applications, we believe that our approach can effectively exploit the spectral information in hyperspectral images, thus beneficial to more accurate classification.
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