Effective spatial-spectral pixel description is of crucial significance for the classification of hyperspectral remote sensing images. Attribute profiles are considered as one of the most prominent approaches in this regard, since they can capture efficiently arbitrary geometric and spectral properties. Lately though, the advent of deep learning in its various forms has also led to remarkable classification performances by operating directly on hyperspectral input. In this letter, we explore the collaboration potential of these two powerful feature extraction approaches. Specifically, we propose a new strategy for hyperspectral image classification, where attribute filtered images are stacked and provided as input to convolutional neural networks. Our experiments with two real hyperspectral remote sensing data sets show that the proposed strategy leads to a performance improvement, as opposed to using each of the involved approaches individually.