Abstract
Abstract. National mapping agencies (NMAs) have to acquire nation-wide Digital Terrain Models on a regular basis as part of their obligations to provide up-to-date data. Point clouds from Airborne Laser Scanning (ALS) are an important data source for this task; recently, NMAs also started deriving Dense Image Matching (DIM) point clouds from aerial images. As a result, NMAs have both point cloud data sources available, which they can exploit for their purposes. In this study, we investigate the potential of transfer learning from ALS to DIM data, so the time consuming step of data labelling can be reduced. Due to their specific individual measurement techniques, both point clouds have various distinct properties such as RGB or intensity values, which are often exploited for classification of either ALS or DIM point clouds. However, those features also hinder transfer learning between these two point cloud types, since they do not exist in the other point cloud type. As the mere 3D point is available in both point cloud types, we focus on transfer learning from an ALS to a DIM point cloud using exclusively the point coordinates. We are tackling the issue of different point densities by rasterizing the point cloud into a 2D grid and take important height features as input for classification. We train an encoder-decoder convolutional neural network with labelled ALS data as a baseline and then fine-tune this baseline with an increasing amount of labelled DIM data. We also train the same network exclusively on all available DIM data as reference to compare our results. We show that only 10% of labelled DIM data increase the classification results notably, which is especially relevant for practical applications.
Highlights
For remote sensing products such as digital terrain models (DTMs), digital surface models (DSMs) or 3D-city models, classifying a point clouds is a crucial step in the processing chain
We focused on transfer learning from Airborne Laser Scanning (ALS) to Dense Image Matching (DIM) point cloud data
We restricted the approach to exclusively using the geometry of the points, since they are part of both point cloud types, and we projected the point clouds into a 2D grid to deal with different point densities
Summary
For remote sensing products such as digital terrain models (DTMs), digital surface models (DSMs) or 3D-city models, classifying a point clouds is a crucial step in the processing chain. Classification is often achieved using supervised learning. To this end, training data with ground truth information has to be provided. NMAs often acquire ALS and DIM in regular update cycles, but due to limited capacities, training a classifier from scratch is often not feasible, as it requires a huge amount of training samples. A possible solution to this problem is transfer learning. The core idea of transfer learning is utilizing an already existing classification model by adapting the weights to new and unknown datasets
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