This dataset inclue multi-spectral acquisition of vegetation for the conception and evaluation of new DeepIndices over un-calibrated data. The images were acquired with the Airphen (Hyphen, Avignon, France) six-band multi-spectral camera configured using the 450/570/675/710/730/850 nm bands with a 10 nm FWHM. The dataset were taken on the site of INRAe in Montoldre (Allier, France, at 46°20'30.3"N 3°26'03.6"E) within the framework of the “RoSE challenge” founded by the French National Research Agency (ANR) and in Dijon (Burgundy, France, at 47°18'32.5"N 5°04'01.8"E) within the site of AgroSup Dijon. Images of bean and corn, containing various natural weeds (yarrows, amaranth, geranium, plantago, etc) and sowed ones (mustards, goosefoots, mayweed and ryegrass) with very distinct characteristics in terms of illumination (shadow, morning, evening, full sun, cloudy, rain, ...) were acquired in top-down view at 1.8 meter from the ground.

Due to the nature of the camera, a spectral band registration is required and performed with a registration method based on previous work (with a sub-pixel registration accuracy). The alignment is refined in two steps, with (i) a rough estimation of the affine correction and (ii) a perspective correction for the refinement and accuracy through the detection and matching of key points. The result shows that GFTT algorithm is the best key-point detector considering the 570 nm band as spectral reference for the registration. After the registration, all spectral images are cropped to 1200* 800 px and concatenated to channel-wise.

Spectral bands inherently have a high noise associated with the CCD sensor, which is a potential problem during normalization. To overcome this effect, 1% of the minimum and maximum signal is suppressed by calculating the quantiles, the signal is clipped on the given range and each band is rescaled in the interval [0,1] using min-max normalization