A Histogram Thresholding Improvement to Mask R-CNN for Scalable Segmentation of New and Old Rural Buildings

TL;DR

This paper introduces HTMask R-CNN, a novel framework that improves rural building segmentation in remote sensing images by using a histogram thresholding method, enabling effective mapping with limited training data.

Contribution

The study presents a new Mask R-CNN based framework that classifies rural buildings into new and old using a dynamic grayscale threshold, reducing the need for extensive training data.

Findings

Achieves higher mAP than orthodox Mask R-CNN.

Converges with limited training samples.

Enables scalable rural building mapping in China.

Abstract

Mapping new and old buildings are of great significance for understanding socio-economic development in rural areas. In recent years, deep neural networks have achieved remarkable building segmentation results in high-resolution remote sensing images. However, the scarce training data and the varying geographical environments have posed challenges for scalable building segmentation. This study proposes a novel framework based on Mask R-CNN, named HTMask R-CNN, to extract new and old rural buildings even when the label is scarce. The framework adopts the result of single-object instance segmentation from the orthodox Mask R-CNN. Further, it classifies the rural buildings into new and old ones based on a dynamic grayscale threshold inferred from the result of a two-object instance segmentation task where training data is scarce. We found that the framework can extract more buildings and achieve a much higher mean Average Precision (mAP) than the orthodox Mask R-CNN model. We tested the novel framework's performance with increasing training data and found that it converged even when the training samples were limited. This framework's main contribution is to allow scalable segmentation by using significantly fewer training samples than traditional machine learning practices. That makes mapping China's new and old rural buildings viable.