English
norskBlar i forfatter "Liu, Qinghui"
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The 1st Agriculture-Vision Challenge: Methods and Results
(Chapter; Bokkapittel, 2020-07-28)The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation task associated with our challenge dataset. Around 57 participating teams from various countries compete to achieve state-of-the-art in aerial agriculture semantic segmentation. The Agriculture-Vision ... -
Advancing Land Cover Mapping in Remote Sensing with Deep Learning
(Doctoral thesis; Doktorgradsavhandling, 2021-12-08)Automatic mapping of land cover in remote sensing data plays an increasingly significant role in several earth observation (EO) applications, such as sustainable development, autonomous agriculture, and urban planning. Due to the complexity of the real ground surface and environment, accurate classification of land cover types is facing many challenges. This thesis provides novel deep learning-based ... -
Dense dilated convolutions merging network for land cover classification
(Journal article; Tidsskriftartikkel; Peer reviewed, 2020-03-06)Land cover classification of remote sensing images is a challenging task due to limited amounts of annotated data, highly imbalanced classes, frequent incorrect pixel-level annotations, and an inherent complexity in the semantic segmentation task. In this article, we propose a novel architecture called the dense dilated convolutions' merging network (DDCM-Net) to address this task. The proposed ... -
Multi-View Self-Constructing Graph Convolutional Networks With Adaptive Class Weighting Loss for Semantic Segmentation
(Conference object; Konferansebidrag, 2020-07-28)We propose a novel architecture called the Multi-view Self-Constructing Graph Convolutional Networks (MSCG-Net) for semantic segmentation. Building on the recently proposed Self-Constructing Graph (SCG) module, which makes use of learnable latent variables to self-construct the underlying graphs directly from the input features without relying on manually built prior knowledge graphs, we leverage ... -
Self-Constructing Graph Convolutional Networks for Semantic Labeling
(Journal article; Tidsskriftartikkel; Peer reviewed, 2021-02-17)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 ... -
Self-constructing graph neural networks to model long-range pixel dependencies for semantic segmentation of remote sensing images
(Journal article; Tidsskriftartikkel; Peer reviewed, 2021-06-16)Capturing global contextual representations in remote sensing images by exploiting long-range pixel-pixel dependencies has been shown to improve segmentation performance. However, how to do this efficiently is an open question as current approaches of utilising attention schemes, or very deep models to increase the field of view, increases complexity and memory consumption. Inspired by recent work ...
