Improved Classification of High Resolution Remote Sensing Imagery with Differential Morphological Profile Neural Network

Deep learning has proven to be an immensely powerful tool within the remote sensing space, with capabilities to perform tasks like classification, object detection, and segmentation in a wide range of modalities and spatial resolutions. The deep neural networks (DNN) extract visual features using numerous techniques, including convolutional and pooling layers, residual modules, inception modules, neural architecture search, and attention networks. Researchers have increasingly found that DNN are biased towards texture, and that this bias is a deficiency that when corrected, can boost classification and detection performance of deep learners. A morphology-based network utilizing the differential morphological profile (DMP) as a non-parametric feature extraction layer, known as DMPNet, was proposed with promising results when compared to VGG16. In this work, the original DMPNet is expanded with increased profile depth, as well as alternate convolutional phases, such as ResNet-18 and MobileNet. The resulting architecture shows an ability to increase shape information in the network, and with it, generalizability on high resolution remote sensing imagery.