Self-Constructing Graph Convolutional Networks for Semantic Labeling

Graph Neural Networks (GNNs) have received increasing attention in many fields. However, due to the lack of prior graphs, their use for semantic labeling has been limited. Here, we propose a novel architecture called the Self-Constructing Graph (SCG), which makes use of learnable latent variables to generate embeddings and to self-construct the underlying graphs directly from the input features without relying on manually built prior knowledge graphs. SCG can automatically obtain optimized non-local context graphs from complex-shaped objects in aerial imagery. We optimize SCG via an adaptive diagonal enhancement method and a variational lower bound that consists of a customized graph reconstruction term and a Kullback-Leibler divergence regularization term. We demonstrate the effectiveness and flexibility of the proposed SCG on the publicly available ISPRS Vaihingen dataset and our model SCG-Net achieves competitive results in terms of F1-score with much fewer parameters and at a lower computational cost compared to related pure-CNN based work. Our code will be made public soon.Comment: IGARSS-2020, code at: github.com/samleoqh/MSCG-Ne

DFPENet-geology: A Deep Learning Framework for High Precision Recognition and Segmentation of Co-seismic Landslides

The following lists two main reasons for withdrawal for the public. 1. There are some problems in the method and results, and there is a lot of room for improvement. In terms of method, "Pre-trained Datasets (PD)" represents selecting a small amount from the online test set, which easily causes the model to overfit the online test set and could not obtain robust performance. More importantly, the proposed DFPENet has a high redundancy by combining the Attention Gate Mechanism and Gate Convolution Networks, and we need to revisit the section of geological feature fusion, in terms of results, we need to further improve and refine. 2. arXiv is an open-access repository of electronic preprints without peer reviews. However, for our own research, we need experts to provide comments on my work whether negative or positive. I then would use their comments to significantly improve this manuscript. Therefore, we finally decided to withdraw this manuscript in arXiv, and we will update to arXiv with the final accepted manuscript to facilitate more researchers to use our proposed comprehensive and general scheme to recognize and segment seismic landslides more efficiently.Comment: 1. There are some problems in the method and results, and there is a lot of room for improvement. Overall, the proposed DFPENet has a high redundancy by combining the Attention Gate Mechanism and Gate Convolution Networks, and we need to further improve and refine the results. 2. For our own research, we need experts to provide comments on my work whether negative or positiv

Deep Neural Network Architectures and Learning Methodologies for Classification and Application in 3D Reconstruction

In this work we explore two different scenarios of 3D reconstruction. The first, urban scenes, is approached using a deep learning network trained to identify structurally important classes within aerial imagery of cities. The network was trained using data taken from ISPRS benchmark dataset of the city of Vaihingen. Using the segmented maps generated by the network we can proceed to more accurately reconstruct the scenes by a process of clustering and then class specific model generation. The second scenario is that of underwater scenes. We use two separate networks to first identify caustics and then remove them from a scene. Data was generated synthetically as real world datasets for this subject are extremely hard to produce. Using the generated caustic free image we can then reconstruct the scene with more precision and accuracy through a process of structure from motion. We investigate different deep learning architectures and parameters for both scenarios. Our results are evaluated to be efficient and effective by comparing them with online benchmarks and alternative reconstruction attempts. We conclude by discussing the limitations of problem specific datasets and our potential research into the generation of datasets through the use of Generative-Adverserial-Networks