Hyperspectral image classification with convolutional networks trained with self-dual attribute profiles

Abstract

Attribute profiles are widely regarded among the most prominent spectral-spatial pixel description methods, providing high performance at a low computational cost. Following their success with computer vision applications, deep learning methods on the other hand are also being rapidly deployed and adapted into the remote sensing image analysis domain, where they already provide competitive description performances. The combination of attribute profiles with convolutional neural networks has recently taken place, showing that these powerful approaches can collaborate. In this paper we explore that direction one step further, by first feeding a convolutional neural network self-dual attribute profiles stacked as a tensor, and then by harvesting the ultimate layer's features for a supervised classification. Our preliminary experiments indicate that this approach leads to a performance improvement.