  Satellite imagery allows a plethora of applications ranging from weather
forecasting to land surveying. The rapid development of computer vision systems
could open new horizons to the utilization of satellite data due to the
abundance of large volumes of data. However, current state-of-the-art computer
vision systems mainly cater to applications that mainly involve natural images.
While useful, those images exhibit a different distribution from satellite
images in addition to having more spectral channels. This allows the use of
pretrained deep learning models only in a subset of spectral channels that are
equivalent to natural images thus discarding valuable information from other
spectral channels. This calls for research effort to optimize deep learning
models for satellite imagery to enable the assessment of their utility in the
domain of remote sensing. Tensorflow tool allows for rapid prototyping and
testing of deep learning models, however, its built-in image generator is
designed to handle a maximum of four spectral channels. This manuscript
introduces an open-source tool that allows the implementation of image
augmentation for hyperspectral images in Tensorflow. Given how accessible and
easy-to-use Tensorflow is, this tool would provide many researchers with the
means to implement, test, and deploy deep learning models for remote sensing
applications.
