Extending Deep Convolutional Neural Networks From 3-Color To Full Multispectral Remote Sensing Imagery

We are currently experiencing a deluge of high-resolution electroptical (HR-EO) remote sensing images which can be leveraged for a diverse set of applications, ranging from environmental monitoring to defense and security applications. One of the most challenging application domains is the use of machine learning techniques, such as computer vision, for the enhancement and automation of geospatial big data analytics. In this work, we present techniques for extending deep convolutional neural networks (DCNN) from 3-band color imagery to 4-band and 8-band multispectral remote sensing imagery. Performance comparisons are conducted between DCNN for 3-, 4-, and 8-band imagery data using the Functional Map of the World dataset. In particular, we investigate five distinct DCNN architectures for classification, and show that utilizing more input channels of the imagery typically has a positive impact on classification metrics such as F1-score and raw accuracy. Herein, we evaluated two methods for initializing the DCNN convolutional filters with 4- or 8-band inputs via transfer learning from networks trained on 3-band (RGB) images and show these methods are superior to random initialization. Our findings indicate that additional spectral bands have varied benefits, depending on the image class, where challenging classes saw minimal improvements. We detail these findings along with insights into the implications for multispectral DCNN in particular geospatial big data analytics use-cases.