Hao Chen

Zipeng Qi, Hao Chen, Chenyang Liu et al.

The mainstream CNN-based remote sensing (RS) image semantic segmentation approaches typically rely on massive labeled training data. Such a paradigm struggles with the problem of RS multi-view scene segmentation with limited labeled views due to the lack of considering 3D information within the scene. In this paper, we propose ''Implicit Ray-Transformer (IRT)'' based on Implicit Neural Representation (INR), for RS scene semantic segmentation with sparse labels (such as 4-6 labels per 100 images). We explore a new way of introducing multi-view 3D structure priors to the task for accurate and view-consistent semantic segmentation. The proposed method includes a two-stage learning process. In the first stage, we optimize a neural field to encode the color and 3D structure of the remote sensing scene based on multi-view images. In the second stage, we design a Ray Transformer to leverage the relations between the neural field 3D features and 2D texture features for learning better semantic representations. Different from previous methods that only consider 3D prior or 2D features, we incorporate additional 2D texture information and 3D prior by broadcasting CNN features to different point features along the sampled ray. To verify the effectiveness of the proposed method, we construct a challenging dataset containing six synthetic sub-datasets collected from the Carla platform and three real sub-datasets from Google Maps. Experiments show that the proposed method outperforms the CNN-based methods and the state-of-the-art INR-based segmentation methods in quantitative and qualitative metrics.

Hao Chen, Mario Valerio Giuffrida, Peter Doerner et al.

Several imaging applications (vessels, retina, plant roots, road networks from satellites) require the accurate segmentation of thin structures for subsequent analysis. Discontinuities (gaps) in the extracted foreground may hinder down-stream image-based analysis of biomarkers, organ structure and topology. In this paper, we propose a general post-processing technique to recover such gaps in large-scale segmentation masks. We cast this problem as a blind inpainting task, where the regions of missing lines in the segmentation masks are not known to the algorithm, which we solve with an adversarially trained neural network. One challenge of using large images is the memory capacity of current GPUs. The typical approach of dividing a large image into smaller patches to train the network does not guarantee global coherence of the reconstructed image that preserves structure and topology. We use adversarial training and reinforcement learning (Policy Gradient) to endow the model with both global context and local details. We evaluate our method in several datasets in medical imaging, plant science, and remote sensing. Our experiments demonstrate that our model produces the most realistic and complete inpainted results, outperforming other approaches. In a dedicated study on plant roots we find that our approach is also comparable to human performance. Implementation available at \url{https://github.com/Hhhhhhhhhhao/Thin-Structure-Inpainting}.