no code implementations • 17 Mar 2023 • Wanshui Gan, Ningkai Mo, Hongbin Xu, Naoto Yokoya
For evaluation, we propose a simple sampling strategy to define the metric for occupancy evaluation, which is flexible for current public datasets.
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.
no code implementations • 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.
no code implementations • 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.
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, 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.
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).
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.