Automatic Pixelwise Object Labeling for Aerial Imagery Using Stacked U-Nets

Abstract

Automation of objects labeling in aerial imagery is a computer vision task with numerous practical applications. Fields like energy exploration require an automated method to process a continuous stream of imagery on a daily basis. In this paper we propose a pipeline to tackle this problem using a stack of convolutional neural networks (U-Net architecture) arranged end-to-end. Each network works as post-processor to the previous one. Our model outperforms current state-of-the-art on two different datasets: Inria Aerial Image Labeling dataset and Massachusetts Buildings dataset each with different characteristics such as spatial resolution, object shapes and scales. Moreover, we experimentally validate computation time savings by processing sub-sampled images and later upsampling pixelwise labeling. These savings come at a negligible degradation in segmentation quality. Though the conducted experiments in this paper cover only aerial imagery, the technique presented is general and can handle other types of images.

References in corpus (3)

• Stacked Hourglass Networks for Human Pose Estimation
• Satellite Imagery Feature Detection using Deep Convolutional Neural Network: A Kaggle Competition
• A Local-Global Approach to Semantic Segmentation in Aerial Images

Cited by in corpus (3)

• MAP-Net: Multi Attending Path Neural Network for Building Footprint Extraction from Remote Sensed Imagery
• Progressively Growing Generative Adversarial Networks for High Resolution Semantic Segmentation of Satellite Images
• Contextual Pyramid Attention Network for Building Segmentation in Aerial Imagery