Point-Based Weakly Supervised Deep Learning for Semantic Segmentation of Remote Sensing Images

Weakly supervised semantic segmentation methods can effectively alleviate the problem of high cost and difficult access to annotation in traditional methods. Among these approaches, point annotated semantic label not only offers a more affordable option but also provides accurate location and category information, playing an indispensable role in current research. However, point annotation labeling encounters challenges such as missing global and texture information, and limiting segmentation accuracy and efficiency while being susceptible to noise interference. For the above problems, a weakly supervised remote sensing image classification framework based on point annotated semantic label is proposed, which consists of three components: data augmentation, Pixel-Net, and iterative superpixel-based sample expansion (ISSE). First, the data augmentation method is used to generate a sufficient number of training samples. Subsequently, the weakly supervised network Pixel-Net is trained using point annotated semantic labels. PixelNet incorporates traditional image processing techniques such as edge detection and blurring into deep learning, enabling effective learning of edge and spectral semantic details while reducing the impact of noise on classification results. Finally, ISSE leverages contextual information from superpixels and pseudo-labels to enrich the valuable information in weakly supervised labels, thereby improving the model's classification performance. In the experiments, existing semantic segmentation methods and Pixel-Net are evaluated on the Vaihingen and Zurich Summer datasets, and the effectiveness of ISSE is verified. The results show that Pixel-Net achieves the best segmentation accuracy on both datasets, while ISSE can effectively utilize the existing point annotation labels to mitigate the effect of noise and thus improve the accuracy of weakly supervised semantic segmentation.