Satellite Imagery Classification with Lidar Data

This paper shows the potential of LIDAR for extracting buildings and other objects from medium resolution satellite imagery. To that end, the study integrated multispectral and LIDAR elevation data in a single imagery file and then classified it using the Support Vector Machine. To determine the method’s potential, the study used a SPOT5 satellite from an area situated southeast of Madrid, Spain. First, with the four multispectral bands and the panchromatic band of the SPOT5 image, a multispectral four bands pansharpening was performed with Principal Component Analysis. Once integrated, these four pansharpening images and LIDAR data, were treated as independent multiple band imagery to perform the classification. Using five classes, a sample of ground truth pixels was taken for training and testing. The study used 10% of the ground truth for training and the entire ground truth for testing the robustness of the classification with and without LIDAR data. To assess and compare the classification results numerically, confusion matrices and Receiver Operating Characteristic (ROC) were calculated for the five classes, for both classifications, with and without LIDAR. Generally, when using only multispectral imagery, some confusion among classes occurs; for instance, buildings with flat asphalt roofs represent a separate problem in classification, since they are extremely difficult to discern from roads. This is mostly solved when integrating LIDAR data to the multispectral imagery. In general, when adding LIDAR, the classification results show a more realistic and homogeneous distribution of geographic features than those obtained when using multispectral SPOT5 alone. * Corresponding author.