We present the CSIMBA project in which we are developing a hyperspectral instrument for a nanosat (12U cubesat) IOD mission. It builds upon the results of the CHIEM initiative where we developed a novel compact imager with a hyperspectral sensor using thin film interference filters directly deposited on a 12Mpixel CMOS 2D detector array. The spectral range covers 475 to 900nm and has a resolution of approximately 5nm. The instrument provides a 80 km swath and 20 m GSD for a 500 km satellite orbit. The longer term goal is to build up a constellation of hyperspectral nanosats, capable of enhanced vegetation monitoring of agricultural fields and the biodiversity status with a revisit rate of a few days.

Recent advances in hyperspectral imaging techniques using spectral filters deposited directly onto an image sensor chip, and suitable for RPAS platforms, are reported in this paper. New filter configurations for a compact spectral camera are described and a prototype of the compact hyperspectral payload developed for small RPAS systems, as well as the first data acquired with the new camera are presented. The spectral range of the payload was optimized for earth observations such as vegetation monitoring or water quality studies. Although the spatially variable filters have to date only been used in small satellite sensors, this technology has a clear potential for RPAS platforms.

We present a novel scale-invariant image feature detection algorithm (D-SIFER) using a newly proposed scale-space optimal 10th-order Gaussian derivative (GDO-10) filter, which reaches the jointly optimal Heisenberg's uncertainty of its impulse response in scale and space simultaneously (i.e., we minimize the maximum of the two moments). The D-SIFER algorithm using this filter leads to an outstanding quality of image feature detection, with a factor of three quality improvement over state-of-the-art scale-invariant feature transform (SIFT) and speeded up robust features (SURF) methods that use the second-order Gaussian derivative filters. To reach low computational complexity, we also present a technique approximating the GDO-10 filters with a fixed-length implementation, which is independent of the scale. The final approximation error remains far below the noise margin, providing constant time, low cost, but nevertheless high-quality feature detection and registration capabilities. D-SIFER is validated on a real-life hyperspectral image registration application, precisely aligning up to hundreds of successive narrowband color images, despite their strong artifacts (blurring, low-light noise) typically occurring in such delicate optical system setups.