A MORPHOLOGICAL-LONG SHORT TERM MEMORY NETWORK APPLIED TO CROP CLASSIFICATION

Abstract

The combination of Convolutional Neural Networks (CNN) with Long Short Term Memory (LSTM) networks in the form of CNN-LSTMs, is one of the currently widely used temporal data series processing approaches. It harnesses the CNN's feature extraction ability along with the LSTM's capacity to account for sequential dependencies. Mathematical morphology on the other hand is known for its spatial analysis potential. In this study, we explore the combination of morphological neural networks (MNNs) with LSTMs, in the form of MNN-LSTMs, and apply it to the problem of crop classification from multi-spectral/temporal remote sensing images. The explored method is tested with two real datasets, where it exhibits either superior or comparable performance to CNN-LSTMs and other state-of-the-art alternative approaches.