Building Extraction From Very High-Resolution Remote Sensing Image With Few Data

Deep neural networks have shown remarkable progress in building extraction. However, their effectiveness is limited due to their dependence on data and dense annotations. It remains challenging to extract buildings with few annotated samples when dealing with variable shapes, sizes, and textures amid an insufficient number of labels. Motivated by the aforementioned challenge, this letter proposes a novel approach called meta rectification networks (MRNs) to extract completely new types of buildings using minimal data accurately. We employed a learning strategy for each specific building extraction task where few annotated building images are used to generate feature representations for differentiating between building and nonbuilding pixels. Our methodology involves matching each pixel to the learned features to detect unknown buildings effectively. Since the feature representations generated from few annotated building images have a large inductive bias, we design a pseudolabel rectification (PLR) mechanism to reduce this inductive bias and enhance the representational power of the feature representations. We evaluate the effectiveness of our model in five different regions. Our experiments show that our proposed model achieves mean-IoU values of 65.54% and overall accuracy of 74.46% while using only 1% of the training data. Our findings suggest that MRN can accurately detect buildings even when trained with limited data, thus providing an effective solution to overcome the constraint of insufficient annotations present in building-extraction problems.