On the Importance of 3D Surface Information for Remote Sensing Classification Tasks

There has been a surge in remote sensing machine learning applications that operate on data from active or passive sensors as well as multi-sensor combinations (Ma et al. (2019)). Despite this surge, however, there has been relatively little study on the comparative value of 3D surface information for machine learning classification tasks. Adding 3D surface information to RGB imagery can provide crucial geometric information for semantic classes such as buildings, and can thus improve out-of-sample predictive performance. In this paper, we examine in-sample and out-of-sample classification performance of Fully Convolutional Neural Networks (FCNNs) and Support Vector Machines (SVMs) trained with and without 3D normalized digital surface model (nDSM) information. We assess classification performance using multispectral imagery from the International Society for Photogrammetry and Remote Sensing (ISPRS) 2D Semantic Labeling contest and the United States Special Operations Command (USSOCOM) Urban 3D Challenge. We find that providing RGB classifiers with additional 3D nDSM information results in little increase in in-sample classification performance, suggesting that spectral information alone may be sufficient for the given classification tasks. However, we observe that providing these RGB classifiers with additional nDSM information leads to significant gains in out-of-sample predictive performance. Specifically, we observe an average improvement in out-of-sample all-class accuracy of 14.4% on the ISPRS dataset and an average improvement in out-of-sample F1 score of 8.6% on the USSOCOM dataset. In addition, the experiments establish that nDSM information is critical in machine learning and classification settings that face training sample scarcity

Support Vector Machines for Photometric Redshift Estimation from Broadband Photometry

Photometric redshifts have been regarded as efficient and effective measures for studying the statistical properties of galaxies and their evolution. In this paper, we introduce SVM_Light, a freely available software package using support vector machines (SVM) for photometric redshift estimation. This technique shows its superiorities in accuracy and efficiency. It can be applied to huge volumes of datasets, and its efficiency is acceptable. When a large representative training set is available, the results of this method are superior to the best ones obtained from template fitting. The method is used on a sample of 73,899 galaxies from the Sloan Digital Sky Survey Data Release 5. When applied to processed data sets, the RMS error in estimating redshifts is less than 0.03. The performances of various kernel functions and different parameter sets have been compared. Parameter selection and uniform data have also been discussed. Finally the strengths and weaknesses of the approach are summarized