Processing Attribute Profiles as Scale-series for Remote Sensing Image Classification

Attribute profiles (APs) are among the most prominent "shallow" spatial-spectral pixel description methods, providing multi-scale, flexible and efficient pixel descriptions, even with modest amounts of training data. In this paper, we investigate their collaboration with long short-term memory networks (LSTMs). Our hypothesis is that a profile can be viewed as a "scale-series" and LSTMs can exploit their sequential nature, akin to temporal series. Plus, feeding a deep network with input of already strong descriptive potential (such as APs) can help them produce advanced features more efficiently w.r.t. training from scratch. Moreover, contrary to the state-of-the-art, we report the results of experiments conducted with non-overlapping training and testing sets, highlighting a significant boost of performance through the combined use of APs with LSTMs.