Addressing Overfitting on Pointcloud Classification using Atrous XCRF

Abstract

Advances in techniques for automated classification of pointcloud data introduce great opportunities for many new and existing applications. However, with a limited number of labeled points, automated classification by a machine learning model is prone to overfitting and poor generalization. The present paper addresses this problem by inducing controlled noise (on a trained model) generated by invoking conditional random field similarity penalties using nearby features. The method is called Atrous XCRF and works by forcing a trained model to respect the similarity penalties provided by unlabeled data. In a benchmark study carried out using the ISPRS 3D labeling dataset, our technique achieves 84.97% in term of overall accuracy, and 71.05% in term of F1 score. The result is on par with the current best model for the benchmark dataset and has the highest value in term of F1 score.

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• Density-Aware Convolutional Networks with Context Encoding for Airborne LiDAR Point Cloud Classification
• Better Modelling Out-of-Distribution Regression on Distributed Acoustic Sensor Data Using Anchored Hidden State Mixup
• Airborne LiDAR Point Cloud Classification with Graph Attention Convolution Neural Network
• LGENet: Local and Global Encoder Network for Semantic Segmentation of Airborne Laser Scanning Point Clouds