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Joint Style and Layout Synthesizing: Toward Generalizable Remote Sensing Semantic Segmentation
Qi Zang;Shuang Wang;Dong Zhao;Zhun Zhong;Biao Hou;Licheng Jiao
IEEE Transactions on Circuits and Systems for Video Technology
Year: 2024 | Early Access Article |
This paper studies the domain generalized remote sensing semantic segmentation (RSSS), aiming to generalize a model trained only on the source domain to unseen domains. Existing methods in computer vision treat style information as domain characteristics to achieve domain-agnostic learning. Nevertheless, their generalizability to RSSS remains constrained, due to the incomplete consideration of dom...Show More
Transcending Fusion: A Multiscale Alignment Method for Remote Sensing Image–Text Retrieval
Rui Yang;Shuang Wang;Yingping Han;Yuanheng Li;Dong Zhao;Dou Quan;Yanhe Guo;Licheng Jiao;Zhi Yang
Remote sensing image-text retrieval (RSITR) is pivotal for knowledge services and data mining in the remote sensing (RS) domain. Considering the multiscale representations in image content and text vocabulary can enable the models to learn richer representations and enhance retrieval. Current multiscale RSITR approaches typically align multiscale fused image features with text features but overloo...Show More
Stable Neighbor Denoising for Source-free Domain Adaptive Segmentation
Dong Zhao;Shuang Wang;Qi Zang;Licheng Jiao;Nicu Sebe;Zhun Zhong
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
We study source-free unsupervised domain adaptation (SFUDA) for semantic segmentation, which aims to adapt a source-trained model to the target domain without accessing the source data. Many works have been proposed to address this challenging problem, among which uncertainty-based self-training is a predominant approach. However, without comprehensive denoising mechanisms, they still largely fall...Show More
Object-Level Change Detection via Siamese Detection Network
IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
Year: 2024 | Conference Paper |
Traditional change detection methods often lack instance-specific analysis, resulting in inefficient resource allocation and response strategies. This paper introduces a novel network for instance-level change detection. Our approach utilizes a dual-stream encoder with shared-weight backbone to extract robust features, followed by a differential process to highlight changes while suppressing uncha...Show More
Mitigating Style Differences in Bitemporal Remote Sensing Images for Change Detection
IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
Year: 2024 | Conference Paper |
Change detection has seen significant advancements with the development of deep learning. However, due to variations in sensors or atmospheric conditions, bitemporal images often exhibit visually significant style differences, posing challenges for the detection of changed regions. This paper presents a change detection network designed to effectively address the challenges posed by style differen...Show More
ConDA: Continual Adaptation in Remote Sensing Via Visual Style Playback
Shi Liang;Ketao Zhong;Dong Zhao;Hu Yang;Shuang Wang;Yanhe Guo
IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
Year: 2024 | Conference Paper |
This study focuses on continual adaptation in remote sensing semantic segmentation, addressing challenges posed by frequent data updates and model forgetting. Remote sensing images exhibit variations in visual styles due to factors like location, time, and weather conditions, creating distinct domains. To counter performance degradation in new domains, we introduce a new challenge task in remote s...Show More
Active Domain Adaptive Semantic Segmentation with Regional Relative Entropy for Remote Sensing Images
Fei Liang;Ruizhi Yang;Zhengyao Wang;Yang Hu;Dong Zhao;Shuang Wang
IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium
Year: 2024 | Conference Paper |
This paper presents a novel approach using active learning to tackle domain adaptation challenges in remote sensing semantic segmentation. Unsupervised Domain Adaptation for Semantic Segmentation (UDASS) aims to transfer a model trained on labeled source domain data to an unlabeled target domain. Existing UDASS methods struggle with the complexity of domain shift factors in remote sensing scenes, ...Show More
Select, Purify, and Exchange: A Multisource Unsupervised Domain Adaptation Method for Building Extraction
Shuang Wang;Qi Zang;Dong Zhao;Chaowei Fang;Dou Quan;Yutong Wan;Yanhe Guo;Licheng Jiao
Accurately extracting buildings from aerial images has essential research significance for timely understanding human intervention on the land. The distribution discrepancies between diversified unlabeled remote sensing images (changes in imaging sensor, location, and environment) and labeled historical images significantly degrade the generalization performance of deep learning algorithms. Unsupe...Show More
Learning Pseudo-Relations for Cross-domain Semantic Segmentation
Dong Zhao;Shuang Wang;Qi Zang;Dou Quan;Xiutiao Ye;Rui Yang;Licheng Jiao
2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (9)
Domain adaptive semantic segmentation aims to adapt a model trained on labeled source domain to unlabeled target domain. Self-training shows competitive potential in this field. Existing methods along this stream mainly focus on selecting reliable predictions on target data as pseudo-labels for category learning, while ignoring the useful relations between pixels for relation learning. In this pap...Show More
Towards Better Stability and Adaptability: Improve Online Self-Training for Model Adaptation in Semantic Segmentation
Dong Zhao;Shuang Wang;Qi Zang;Dou Quan;Xiutiao Ye;Licheng Jiao
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (14)
Unsupervised domain adaptation (UDA) in semantic segmentation transfers the knowledge of the source domain to the target one to improve the adaptability of the segmentation model in the target domain. The need to access labeled source data makes UDA unable to handle adaptation scenarios involving privacy, property rights protection, and confidentiality. In this paper, we focus on unsupervised mode...Show More
Cluster Alignment With Target Knowledge Mining for Unsupervised Domain Adaptation Semantic Segmentation
Shuang Wang;Dong Zhao;Chi Zhang;Yuwei Guo;Qi Zang;Yu Gu;Yi Li;Licheng Jiao
Unsupervised domain adaptation (UDA) carries out knowledge transfer from the labeled source domain to the unlabeled target domain. Existing feature alignment methods in UDA semantic segmentation achieve this goal by aligning the feature distribution between domains. However, these feature alignment methods ignore the domain-specific knowledge of the target domain. In consequence, 1) the correlatio...Show More
The Outcome of the 2022 Landslide4Sense Competition: Advanced Landslide Detection From Multisource Satellite Imagery
Omid Ghorbanzadeh;Yonghao Xu;Hengwei Zhao;Junjue Wang;Yanfei Zhong;Dong Zhao;Qi Zang;Shuang Wang;Fahong Zhang;Yilei Shi;Xiao Xiang Zhu;Lin Bai;Weile Li;Weihang Peng;Pedram Ghamisi
The scientific outcomes of the 2022 Landslide4Sense (L4S) competition organized by the Institute of Advanced Research in Artificial Intelligence are presented here. The objective of the competition is to automatically detect landslides based on large-scale multiple sources of satellite imagery collected globally. The 2022 L4S aims to foster interdisciplinary research on recent developments in deep...Show More
MANet: Multi-Scale Aware-Relation Network for Semantic Segmentation in Aerial Scenes
Pei He;Licheng Jiao;Ronghua Shang;Shuang Wang;Xu Liu;Dou Quan;Kun Yang;Dong Zhao
Semantic segmentation is an important yet unsolved problem in aerial scenes understanding. One of the major challenges is the intense variations of scenes and object scales. In this article, we propose a novel multi-scale aware-relation network (MANet) to tackle this problem in remote sensing. Inspired by the process of human perception of multi-scale (MS) information, we explore discriminative an...Show More
An Adaptation of Cnn for Small Target Detection in the Infrared
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
Year: 2018 | Conference Paper |
Cited by: Papers (13)
Due to the low signal to noise ratio and limited spatial resolution, small target detection in an infrared image is a challenging task. Existing methods often have high false alarm rates and low probabilities of detection when infrared small targets submerge in the background clutter. In this paper, the Convolutional Neural Network (CNN) is adapted to extract the hidden features of small targets f...Show More
Globally stable adaptive tracking control of a wheeled mobile robot using RBF neural network as feedforward compensator
2013 25th Chinese Control and Decision Conference (CCDC)
Year: 2013 | Conference Paper |
Cited by: Papers (1)
In this paper, a new adaptive position tracking control strategy is proposed for a class of wheeled mobile robot systems where radial basis function (RBF) neural network (NN) is used to model the uncertainty. The so-called feedforward compensation scheme is developed where only the information of the reference position is employed as the NN input. The main advantage is that the global stability of...Show More
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