How to learn long-range dependencies from 3D point clouds is a challenging problem in 3D point cloud analysis. Addressing this problem, we propose a global attention network for point cloud semantic segmentation, named as GA-Net, consisting of a point-independent global attention module and a point-dependent global attention module for obtaining contextual information of 3D point clouds in this paper. The point-independent global attention module simply shares a global attention map for all 3D points. In the point-dependent global attention module, for each point, a novel random cross attention block using only two randomly sampled subsets is exploited to learn the contextual information of all the points. Additionally, we design a novel point-adaptive aggregation block to replace linear skip connection for aggregating more discriminate features. Extensive experimental results on three 3D public datasets demonstrate that our method outperforms state-of-the-art methods in most cases.

Due to the all-weather and all-time imaging capability of synthetic aperture radar (SAR), SAR remote sensing analysis has attracted much attention recently. However, compared with the optical images, SAR images are more difficult to be interpreted. If an SAR image could be translated into its corresponding optical image, then the generated optical image would be helpful for assisting the interpretation. Addressing this issue, we investigate how to translate SAR images into optical ones in this work and propose a parallel generative adversarial model for SAR-to-optical image translation, called parallel generative adversarial network (Parallel-GAN), consisting of a backbone image translation subnetwork and an adjoint optical image reconstruction subnetwork. Under the proposed model, the backbone image translation subnetwork is designed to translate SAR images into optical ones, and simultaneously some of its intermediate layers are required to output similar latent features to those from the corresponding layers of the adjoint image reconstruction subnetwork. Thanks to the imposed hierarchical latent optical features, the proposed Parallel-GAN could achieve the SAR-to-optical image translation effectively. Extensive experimental results on three public datasets demonstrate that the proposed method outperforms ten state-of-the-art methods for SAR-to-optical image translation.

Single-view height estimation and semantic segmentation have received increasing attention in recent years and play an important role in the photogrammetry and remote sensing communities. The height information and semantic information of images are correlated, and some recent works have shown that multi-task learning methods can achieve complementation of task-related features and improve the prediction results of the multiple tasks. Although much progress has been made in recent works, how to effectively extract and fuse height features and semantic features is still an open issue. In this paper, a self- and cross-enhancement network (SCE-Net) is proposed to jointly perform height estimation and semantic segmentation on single aerial images. A feature separation–fusion module is constructed to effectively separate and fuse height features and semantic features based on an attention mechanism for feature representation enhancement across tasks. In addition, a height-guided feature distance loss and a semantic-guided feature distance loss are designed based on deep metric learning to achieve task-aware feature representation enhancement. Extensive experiments are conducted on the Vaihingen dataset and the Potsdam dataset to verify the effectiveness of the proposed method. The experimental results demonstrate that the proposed SCE-Net could outperform the state-of-the-art methods and achieve better performance in both height estimation and semantic segmentation.

Height estimation from single images, strictly speaking, is an ill-posed problem. However, recently, it is shown that it is both possible and feasible to learn a mapping from image statistics to height information. In spite of recent efforts in this field, how to learn fine-shape preserving features, such as object boundaries and contours, is still an open issue. In this work, we propose a progressive learning network to estimate height information from single aerial images in a coarse-to-fine manner. In particular, a gated feature aggregation module is introduced to effectively combine low-level and high-level features. The proposed method is validated on three public datasets, including the Vaihingen dataset, the Potsdam dataset, and the DFC2019 dataset. Both quantitative and qualitative experimental results demonstrate that the proposed method can achieve more accurate height estimation from single aerial images, especially with better object boundary and contour preserving capability, than four related height estimation methods.

Open-set recognition aims to identify unknown classes while maintaining classification performance on known classes and has attracted increasing attention in the pattern recognition field. However, how to learn effective feature representations whose distributions are usually complex for classifying both known-class and unknown-class samples when only the known-class samples are available for training is an ongoing issue in open-set recognition. In contrast to methods implementing a single Gaussian, a mixture of Gaussians (MoG), or multiple MoGs, we propose a novel autoencoder that learns feature representations by modeling them as mixtures of exponential power distributions (MoEPs) in latent spaces called MoEP-AE. The proposed autoencoder considers that many real-world distributions are sub-Gaussian or super-Gaussian and can thus be represented by MoEPs rather than a single Gaussian or an MoG or multiple MoGs. We design a differentiable sampler that can sample from an MoEP to guarantee that the proposed autoencoder is trained effectively. Furthermore, we propose an MoEP-AE-based method for open-set recognition by introducing a discrimination strategy, where the MoEP-AE is used to model the distributions of the features extracted from the input known-class samples by minimizing a designed loss function at the training stage, called MoEP-AE-OSR. Extensive experimental results in both standard-dataset and cross-dataset settings demonstrate that the MoEP-AE-OSR method outperforms 14 existing open-set recognition methods in most cases in both open-set recognition and closed-set recognition tasks.