no code implementations • 27 May 2025 • Weihao Xuan, Junjue Wang, Heli Qi, Zihang Chen, Zhuo Zheng, Yanfei Zhong, Junshi Xia, Naoto Yokoya
Multimodal large language models have demonstrated remarkable capabilities in visual understanding, but their application to long-term Earth observation analysis remains limited, primarily focusing on single-temporal or bi-temporal imagery.
no code implementations • 26 May 2025 • Weihao Xuan, Qingcheng Zeng, Heli Qi, Junjue Wang, Naoto Yokoya
Uncertainty quantification is essential for assessing the reliability and trustworthiness of modern AI systems.
no code implementations • 11 May 2025 • Jian Song, Hongruixuan Chen, Naoto Yokoya
Recently, models trained on synthetic data and refined through domain adaptation have shown remarkable performance in MHE, yet it remains unclear how these models make predictions or how reliable they truly are.
no code implementations • 8 May 2025 • Jie Deng, Danfeng Hong, Chenyu Li, Naoto Yokoya
We propose a novel joint framework by integrating super-resolution and segmentation, called JointSeg, which enables the generation of 1-meter ISA maps directly from freely available Sentinel-2 imagery.
1 code implementation • 4 Apr 2025 • Yimin Wei, Aoran Xiao, Yexian Ren, Yuting Zhu, Hongruixuan Chen, Junshi Xia, Naoto Yokoya
Synthetic Aperture Radar (SAR) is a crucial remote sensing technology, enabling all-weather, day-and-night observation with strong surface penetration for precise and continuous environmental monitoring and analysis.
no code implementations • 20 Mar 2025 • Abduljaleel Adejumo, Faegheh Yeganli, Clifford Broni-Bediako, Aoran Xiao, Naoto Yokoya, Mennatullah Siam
Multimodal Large Language Models (MLLMs) have achieved remarkable success in vision-language tasks but their remote sensing (RS) counterpart are relatively under explored.
1 code implementation • 12 Mar 2025 • Zhehui Wu, Yong Chen, Naoto Yokoya, wei he
In this paper, we propose MP-HSIR, a novel multi-prompt framework that effectively integrates spectral, textual, and visual prompts to achieve universal HSI restoration across diverse degradation types and intensities.
no code implementations • 18 Jan 2025 • Junshi Xia, Hongruixuan Chen, Clifford Broni-Bediako, Yimin Wei, Jian Song, Naoto Yokoya
To bridge this gap and facilitate advancements in SAR-based geospatial analysis, we introduce OpenEarthMap-SAR, a benchmark SAR dataset, for global high-resolution land cover mapping.
1 code implementation • 10 Jan 2025 • Hongruixuan Chen, Jian Song, Olivier Dietrich, Clifford Broni-Bediako, Weihao Xuan, Junjue Wang, Xinlei Shao, Yimin Wei, Junshi Xia, Cuiling Lan, Konrad Schindler, Naoto Yokoya
In this paper, we present a BDA dataset using veRy-hIGH-resoluTion optical and SAR imagery (BRIGHT) to support AI-based all-weather disaster response.
Ranked #1 on
Building Damage Assessment
on BRIGHT
no code implementations • CVPR 2025 • Zhihao Liu, Zhanglin Cheng, Naoto Yokoya
Obtaining high-quality, practically usable 3D models of biological plants remains a significant challenge in computer vision and graphics.
1 code implementation • 30 Oct 2024 • Ziyang Gong, Zhixiang Wei, Di Wang, Xianzheng Ma, Hongruixuan Chen, Yuru Jia, Yupeng Deng, Zhenming Ji, Xiangwei Zhu, Naoto Yokoya, Jing Zhang, Bo Du, Liangpei Zhang
The field of Remote Sensing Domain Generalization (RSDG) has emerged as a critical and valuable research frontier, focusing on developing models that generalize effectively across diverse scenarios.
1 code implementation • 22 Oct 2024 • Aoran Xiao, Weihao Xuan, Junjue Wang, Jiaxing Huang, DaCheng Tao, Shijian Lu, Naoto Yokoya
This survey systematically reviews the emerging field of RSFMs.
1 code implementation • 17 Sep 2024 • Clifford Broni-Bediako, Junshi Xia, Jian Song, Hongruixuan Chen, Mennatullah Siam, Naoto Yokoya
While previous datasets and benchmarks discussed the few-shot segmentation setting in remote sensing, we are the first to propose a generalized few-shot segmentation benchmark for remote sensing.
1 code implementation • 21 Aug 2024 • Wanshui Gan, Fang Liu, Hongbin Xu, Ningkai Mo, Naoto Yokoya
We introduce GaussianOcc, a systematic method that investigates the two usages of Gaussian splatting for fully self-supervised and efficient 3D occupancy estimation in surround views.
1 code implementation • 17 Aug 2024 • Aoran Xiao, Weihao Xuan, Heli Qi, Yun Xing, Naoto Yokoya, Shijian Lu
It addresses three main challenges: 1) adaptation toward diverse non-RGB sensors for single-modal processing, 2) synergistic processing of multi-modal data via sensor fusion, and 3) mask-free training for different downstream tasks.
1 code implementation • 26 Jun 2024 • Jian Song, Hongruixuan Chen, Weihao Xuan, Junshi Xia, Naoto Yokoya
To further enhance its utility, we develop a novel multi-task unsupervised domain adaptation (UDA) method, RS3DAda, coupled with our synthetic dataset, which facilitates the RS-specific transition from synthetic to real scenarios for land cover mapping and height estimation tasks, ultimately enabling global monocular 3D semantic understanding based on synthetic data.
no code implementations • 25 Jun 2024 • Xuming Zhang, Naoto Yokoya, Xingfa Gu, Qingjiu Tian, Lorenzo Bruzzone
The FEM is used to enhance complementary information by combining the feature from the other modality across direction-aware, position-sensitive, and channel-wise dimensions.
1 code implementation • 17 Jun 2024 • Di Wang, Meiqi Hu, Yao Jin, Yuchun Miao, Jiaqi Yang, Yichu Xu, Xiaolei Qin, Jiaqi Ma, Lingyu Sun, Chenxing Li, Chuan Fu, Hongruixuan Chen, Chengxi Han, Naoto Yokoya, Jing Zhang, Minqiang Xu, Lin Liu, Lefei Zhang, Chen Wu, Bo Du, DaCheng Tao, Liangpei Zhang
Accurate hyperspectral image (HSI) interpretation is critical for providing valuable insights into various earth observation-related applications such as urban planning, precision agriculture, and environmental monitoring.
1 code implementation • 23 Apr 2024 • Clifford Broni-Bediako, Junshi Xia, Naoto Yokoya
Thus, we proposed a simple yet effective framework to search for lightweight neural networks automatically for land cover mapping tasks under domain shifts.
1 code implementation • 4 Apr 2024 • Hongruixuan Chen, Jian Song, Chengxi Han, Junshi Xia, Naoto Yokoya
Convolutional neural networks (CNN) and Transformers have made impressive progress in the field of remote sensing change detection (CD).
Ranked #1 on
Change Detection
on SECOND
no code implementations • 17 Jan 2024 • Hongruixuan Chen, Jian Song, Naoto Yokoya
In this study, we explore unsupervised multimodal change detection between two key remote sensing data sources: optical high-resolution imagery and OpenStreetMap (OSM) data.
no code implementations • 19 Nov 2023 • Naoto Yokoya, Junshi Xia, Clifford Broni-Bediako
Deep learning has shown promising performance in submeter-level mapping tasks; however, the annotation cost of submeter-level imagery remains a challenge, especially when applied on a large scale.
1 code implementation • 13 Nov 2023 • Danfeng Hong, Bing Zhang, Xuyang Li, YuXuan Li, Chenyu Li, Jing Yao, Naoto Yokoya, Hao Li, Pedram Ghamisi, Xiuping Jia, Antonio Plaza, Paolo Gamba, Jon Atli Benediktsson, Jocelyn Chanussot
The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner.
no code implementations • 3 Nov 2023 • Xiaomou Hou, Wanshui Gan, Naoto Yokoya
Accurate height estimation from monocular aerial imagery presents a significant challenge due to its inherently ill-posed nature.
no code implementations • 1 Nov 2023 • Iu Yahiro, Takashi Ishida, Naoto Yokoya
One of the main approaches to address this problem is to modify the loss function, often using regularization terms in addition to changing the type of adversarial losses.
1 code implementation • 4 Oct 2023 • Hongruixuan Chen, Cuiling Lan, Jian Song, Clifford Broni-Bediako, Junshi Xia, Naoto Yokoya
Optical high-resolution imagery and OSM data are two important data sources of change detection (CD).
1 code implementation • 1 Oct 2023 • Hongruixuan Chen, Jian Song, Chen Wu, Bo Du, Naoto Yokoya
Change detection (CD) is a critical task in studying the dynamics of ecosystems and human activities using multi-temporal remote sensing images.
no code implementations • 12 Sep 2023 • Clifford Broni-Bediako, Junshi Xia, Naoto Yokoya
With the success of efficient deep learning methods (i. e., efficient deep neural networks) for real-time semantic segmentation in computer vision, researchers have adopted these efficient deep neural networks in remote sensing image analysis.
1 code implementation • 5 Sep 2023 • Jian Song, Hongruixuan Chen, Naoto Yokoya
However, when it comes to remote sensing image processing, the creation of synthetic datasets becomes challenging due to the demand for larger-scale and more diverse 3D models.
1 code implementation • 23 Aug 2023 • Xiaoyu Dong, Naoto Yokoya
Experiments show that our approach can effectively enhance dark scene understanding based on multi-modal images with limited semantics by shaping semantic-discriminative feature spaces.
Ranked #1 on
Semantic Segmentation
on LLRGBD-synthetic
1 code implementation • 17 Mar 2023 • Wanshui Gan, Ningkai Mo, Hongbin Xu, Naoto Yokoya
In this work, we present a simple framework for 3D occupancy estimation, which is a CNN-based framework designed to reveal several key factors for 3D occupancy estimation, such as network design, optimization, and evaluation.
no code implementations • 19 Oct 2022 • Junshi Xia, Naoto Yokoya, Bruno Adriano, Clifford Broni-Bediako
We introduce OpenEarthMap, a benchmark dataset, for global high-resolution land cover mapping.
1 code implementation • 3 Oct 2022 • Hongruixuan Chen, Naoto Yokoya, Chen Wu, Bo Du
Subsequently, the similarity levels of two structural relationships are calculated from learned graph representations and two difference images are generated based on the similarity levels.
no code implementations • 26 Sep 2022 • Michael Schmitt, Pedram Ghamisi, Naoto Yokoya, Ronny Hänsch
In the era of deep learning, annotated datasets have become a crucial asset to the remote sensing community.
1 code implementation • 19 Jul 2022 • Xiaoyu Dong, Naoto Yokoya, Longguang Wang, Tatsumi Uezato
Self-supervised cross-modal super-resolution (SR) can overcome the difficulty of acquiring paired training data, but is challenging because only low-resolution (LR) source and high-resolution (HR) guide images from different modalities are available.
1 code implementation • 28 May 2022 • Wanshui Gan, Hongbin Xu, Yi Huang, Shifeng Chen, Naoto Yokoya
The proposed LUTs-based refinement module achieves the performance gain with little computational cost and could serve as the plug-and-play module in the novel view synthesis task.
1 code implementation • 7 May 2022 • Danfeng Hong, Jing Yao, Deyu Meng, Naoto Yokoya, Jocelyn Chanussot
Enormous efforts have been recently made to super-resolve hyperspectral (HS) images with the aid of high spatial resolution multispectral (MS) images.
Hyperspectral Image Super-Resolution
Image Super-Resolution
+1
1 code implementation • CVPR 2022 • Ningkai Mo, Wanshui Gan, Naoto Yokoya, Shifeng Chen
In this paper, a computation efficient regression framework is presented for estimating the 6D pose of rigid objects from a single RGB-D image, which is applicable to handling symmetric objects.
Ranked #4 on
6D Pose Estimation
on DTTD-Mobile
no code implementations • 4 Nov 2021 • Junshi Xia, Naoto Yokoya, Bruno Adriano
Humanitarian organizations must have fast and reliable data to respond to disasters.
1 code implementation • 21 May 2021 • Danfeng Hong, Lianru Gao, Jing Yao, Naoto Yokoya, Jocelyn Chanussot, Uta Heiden, Bing Zhang
Over the past decades, enormous efforts have been made to improve the performance of linear or nonlinear mixing models for hyperspectral unmixing, yet their ability to simultaneously generalize various spectral variabilities and extract physically meaningful endmembers still remains limited due to the poor ability in data fitting and reconstruction and the sensitivity to various spectral variabilities.
no code implementations • 2 Mar 2021 • Danfeng Hong, wei he, Naoto Yokoya, Jing Yao, Lianru Gao, Liangpei Zhang, Jocelyn Chanussot, Xiao Xiang Zhu
Hyperspectral imaging, also known as image spectrometry, is a landmark technique in geoscience and remote sensing (RS).
no code implementations • 30 Dec 2020 • wei he, Naoto Yokoya, Xin Yuan
Specifically, the RGB measurement is utilized to estimate the coefficients, meanwhile the CASSI measurement is adopted to provide the orthogonal spectral basis.
1 code implementation • 23 Nov 2020 • Gerald Baier, Antonin Deschemps, Michael Schmitt, Naoto Yokoya
We synthesize both optical RGB and synthetic aperture radar (SAR) remote sensing images from land cover maps and auxiliary raster data using generative adversarial networks (GANs).
1 code implementation • 24 Oct 2020 • wei he, Quanming Yao, Chao Li, Naoto Yokoya, Qibin Zhao, Hongyan zhang, Liangpei Zhang
Non-local low-rank tensor approximation has been developed as a state-of-the-art method for hyperspectral image (HSI) restoration, which includes the tasks of denoising, compressed HSI reconstruction and inpainting.
1 code implementation • 21 Sep 2020 • Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot, Jian Xu, Xiao Xiang Zhu
Conventional nonlinear subspace learning techniques (e. g., manifold learning) usually introduce some drawbacks in explainability (explicit mapping) and cost-effectiveness (linearization), generalization capability (out-of-sample), and representability (spatial-spectral discrimination).
no code implementations • 14 Sep 2020 • Bruno Adriano, Naoto Yokoya, Junshi Xia, Hiroyuki Miura, Wen Liu, Masashi Matsuoka, Shunichi Koshimura
In this study, we have developed a global multisensor and multitemporal dataset for building damage mapping.
1 code implementation • 12 Aug 2020 • Danfeng Hong, Lianru Gao, Naoto Yokoya, Jing Yao, Jocelyn Chanussot, Qian Du, Bing Zhang
In particular, we also investigate a special case of multi-modality learning (MML) -- cross-modality learning (CML) that exists widely in RS image classification applications.
1 code implementation • ECCV 2020 • Tatsumi Uezato, Danfeng Hong, Naoto Yokoya, wei he
The proposed network is composed of an encoder-decoder network that exploits multi-scale features of a guidance image and a deep decoder network that generates an output image.
no code implementations • 23 Jul 2020 • Tatsumi Uezato, Naoto Yokoya, wei he
Although many spectral unmixing models have been developed to address spectral variability caused by variable incident illuminations, the mechanism of the spectral variability is still unclear.
no code implementations • 24 Jun 2020 • Danfeng Hong, Naoto Yokoya, Gui-Song Xia, Jocelyn Chanussot, Xiao Xiang Zhu
This paper addresses the problem of semi-supervised transfer learning with limited cross-modality data in remote sensing.
1 code implementation • 9 Jun 2020 • Naoto Yokoya, Kazuki Yamanoi, wei he, Gerald Baier, Bruno Adriano, Hiroyuki Miura, Satoru Oishi
We propose a framework that estimates inundation depth (maximum water level) and debris-flow-induced topographic deformation from remote sensing imagery by integrating deep learning and numerical simulation.
no code implementations • 6 Mar 2020 • Jian Kang, Danfeng Hong, Jialin Liu, Gerald Baier, Naoto Yokoya, Begüm Demir
Interferometric phase restoration has been investigated for decades and most of the state-of-the-art methods have achieved promising performances for InSAR phase restoration.
no code implementations • 6 Jan 2020 • Wei He, Yong Chen, Naoto Yokoya, Chao Li, Qibin Zhao
In this paper, we propose a new model, named coupled tensor ring factorization (CTRF), for HSR.
no code implementations • 18 Dec 2019 • Danfeng Hong, Xin Wu, Pedram Ghamisi, Jocelyn Chanussot, Naoto Yokoya, Xiao Xiang Zhu
In this paper, we propose a solution to address this issue by locally extracting invariant features from hyperspectral imagery (HSI) in both spatial and frequency domains, using a method called invariant attribute profiles (IAPs).
no code implementations • 18 Dec 2019 • Danfeng Hong, Jocelyn Chanussot, Naoto Yokoya, Jian Kang, Xiao Xiang Zhu
Due to the ever-growing diversity of the data source, multi-modality feature learning has attracted more and more attention.
1 code implementation • 27 Apr 2019 • Ying Qu, Hairong Qi, Chiman Kwan, Naoto Yokoya, Jocelyn Chanussot
With this design, the network allows to extract correlated spectral and spatial information from unregistered images that better preserves the spectral information.
no code implementations • 9 Jan 2019 • Danfeng Hong, Naoto Yokoya, Nan Ge, Jocelyn Chanussot, Xiao Xiang Zhu
In this paper, we aim at tackling a general but interesting cross-modality feature learning question in remote sensing community --- can a limited amount of highly-discrimin-ative (e. g., hyperspectral) training data improve the performance of a classification task using a large amount of poorly-discriminative (e. g., multispectral) data?
no code implementations • 30 Dec 2018 • Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot, Xiao Xiang Zhu
To achieve accurate land cover classification over a large coverage, we propose a cross-modality feature learning framework, called common subspace learning (CoSpace), by jointly considering subspace learning and supervised classification.
no code implementations • 19 Dec 2018 • Pedram Ghamisi, Behnood Rasti, Naoto Yokoya, Qunming Wang, Bernhard Hofle, Lorenzo Bruzzone, Francesca Bovolo, Mingmin Chi, Katharina Anders, Richard Gloaguen, Peter M. Atkinson, Jon Atli Benediktsson
The sharp and recent increase in the availability of data captured by different sensors combined with their considerably heterogeneous natures poses a serious challenge for the effective and efficient processing of remotely sensed data.
2 code implementations • CVPR 2019 • Wei He, Quanming Yao, Chao Li, Naoto Yokoya, Qibin Zhao
This is done by first learning a low-dimensional projection and the related reduced image from the noisy HSI.
Ranked #10 on
Hyperspectral Image Denoising
on ICVL-HSI-Gaussian50
no code implementations • 29 Oct 2018 • Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot, Xiao Xiang Zhu
To this end, we propose a novel spectral mixture model, called the augmented linear mixing model (ALMM), to address spectral variability by applying a data-driven learning strategy in inverse problems of hyperspectral unmixing.
no code implementations • ECCV 2018 • Danfeng Hong, Naoto Yokoya, Jian Xu, Xiaoxiang Zhu
Despite the fact that nonlinear subspace learning techniques (e. g. manifold learning) have successfully applied to data representation, there is still room for improvement in explainability (explicit mapping), generalization (out-of-samples), and cost-effectiveness (linearization).
no code implementations • 26 Jul 2018 • Wei He, Naoto Yokoya
In this paper, we present the optical image simulation from a synthetic aperture radar (SAR) data using deep learning based methods.
no code implementations • 17 Apr 2015 • Laetitia Loncan, Luis B. Almeida, José M. Bioucas-Dias, Xavier Briottet, Jocelyn Chanussot, Nicolas Dobigeon, Sophie Fabre, Wenzhi Liao, Giorgio A. Licciardi, Miguel Simões, Jean-Yves Tourneret, Miguel A. Veganzones, Gemine Vivone, Qi Wei, Naoto Yokoya
In this work, we compare new pansharpening techniques designed for hyperspectral data with some of the state of the art methods for multispectral pansharpening, which have been adapted for hyperspectral data.