Unsupervised feature learning for illumination robustness

The illumination conditions of a scene create intra-class variability in outdoor visual data, degrading the performance of high-level algorithms. Using only the image, and with hyper-spectral data as a case study, this paper proposes a deep learning approach to learn illumination invariant features from the data in an unsupervised manner. The proposed approach incorporates a similarity measure, the Spectral Angle, that is relatively insensitive to brightness into the cost function of a Stacked Auto-Encoder so that an illumination invariant mapping is learned from the input data to the hidden layer. Experiments using synthetic and real imagery show that this novel feature learning approach produces a more illumination invariant representation of the data, improving the results of a high-level algorithm (clustering) under such conditions.