1 code implementation • 23 Apr 2024 • Muhammad Ahmad, Salvatore Distifano, Adil Mehmood Khan, Manuel Mazzara, Chenyu Li, Jing Yao, Hao Li, Jagannath Aryal, Jun Zhou, Gemine Vivone, Danfeng Hong
Traditional approaches encounter the curse of dimensionality, struggle with feature selection and extraction, lack spatial information consideration, exhibit limited robustness to noise, face scalability issues, and may not adapt well to complex data distributions.
no code implementations • 19 Apr 2024 • JunMing Hou, ZiHan Cao, Naishan Zheng, Xuan Li, Xiaoyu Chen, Xinyang Liu, Xiaofeng Cong, Man Zhou, Danfeng Hong
In this way, our proposed method is capable of benefiting the cascaded modeling rule while achieving favorable performance in the efficient manner.
1 code implementation • 12 Apr 2024 • Jing Yao, Danfeng Hong, Chenyu Li, Jocelyn Chanussot
Recurrent neural networks and Transformers have recently dominated most applications in hyperspectral (HS) imaging, owing to their capability to capture long-range dependencies from spectrum sequences.
no code implementations • 23 Feb 2024 • Chenyu Li, Bing Zhang, Danfeng Hong, Jing Yao, Jocelyn Chanussot
These factors also limit the performance of the well-known low-rank representation (LRR) models in terms of robustness on the separation of background and target features and the reliance on manual parameter selection.
no code implementations • 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 • 26 Sep 2023 • Danfeng Hong, Bing Zhang, Hao Li, YuXuan Li, Jing Yao, Chenyu Li, Martin Werner, Jocelyn Chanussot, Alexander Zipf, Xiao Xiang Zhu
Artificial intelligence (AI) approaches nowadays have gained remarkable success in single-modality-dominated remote sensing (RS) applications, especially with an emphasis on individual urban environments (e. g., single cities or regions).
1 code implementation • 9 Aug 2023 • Ali Jamali, Swalpa Kumar Roy, Danfeng Hong, Peter M Atkinson, Pedram Ghamisi
Results illustrated the superiority of the developed SGU-MLP classification algorithm over several CNN and CNN-ViT-based models, including HybridSN, ResNet, iFormer, EfficientFormer and CoAtNet.
1 code implementation • 2 Dec 2022 • Xin Wu, Danfeng Hong, Jocelyn Chanussot
RM-DS integrates Residual U-blocks into a deep supervision network to generate deep multi-scale resolution-maintenance features while learning global context information.
no code implementations • 15 Oct 2022 • Keyu Yan, Man Zhou, Jie Huang, Feng Zhao, Chengjun Xie, Chongyi Li, Danfeng Hong
Panchromatic (PAN) and multi-spectral (MS) image fusion, named Pan-sharpening, refers to super-resolve the low-resolution (LR) multi-spectral (MS) images in the spatial domain to generate the expected high-resolution (HR) MS images, conditioning on the corresponding high-resolution PAN images.
1 code implementation • journal 2022 • Bobo Xi, Jiaojiao Li, Yunsong Li, Rui Song, Danfeng Hong, Jocelyn Chanussot.
Recently, embedding and metric-based few-shot learning (FSL) has been introduced into hyperspectral image classification (HSIC) and achieved impressive progress.
no code implementations • 13 May 2022 • Minghua Wang, Danfeng Hong, Zhu Han, Jiaxin Li, Jing Yao, Lianru Gao, Bing Zhang, Jocelyn Chanussot
Owing to the rapid development of sensor technology, hyperspectral (HS) remote sensing (RS) imaging has provided a significant amount of spatial and spectral information for the observation and analysis of the Earth's surface at a distance of data acquisition devices, such as aircraft, spacecraft, and satellite.
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
no code implementations • 3 May 2022 • Jiaxin Li, Danfeng Hong, Lianru Gao, Jing Yao, Ke Zheng, Bing Zhang, Jocelyn Chanussot
With the extremely rapid advances in remote sensing (RS) technology, a great quantity of Earth observation (EO) data featuring considerable and complicated heterogeneity is readily available nowadays, which renders researchers an opportunity to tackle current geoscience applications in a fresh way.
2 code implementations • 31 Mar 2022 • Swalpa Kumar Roy, Ankur Deria, Danfeng Hong, Behnood Rasti, Antonio Plaza, Jocelyn Chanussot
Vision transformers (ViTs) have been trending in image classification tasks due to their promising performance when compared to convolutional neural networks (CNNs).
no code implementations • 25 Oct 2021 • Xin Wu, Wei Li, Danfeng Hong, Ran Tao, Qian Du
Owing to effective and flexible data acquisition, unmanned aerial vehicle (UAV) has recently become a hotspot across the fields of computer vision (CV) and remote sensing (RS).
2 code implementations • 7 Jul 2021 • Danfeng Hong, Zhu Han, Jing Yao, Lianru Gao, Bing Zhang, Antonio Plaza, Jocelyn Chanussot
Hyperspectral (HS) images are characterized by approximately contiguous spectral information, enabling the fine identification of materials by capturing subtle spectral discrepancies.
2 code implementations • 23 May 2021 • Pan Chen, Danfeng Hong, Zhengchao Chen, Xuan Yang, Baipeng Li, Bing Zhang
Moreover, a self-supervised learning-based strategy is proposed to constrain feature learning.
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.
1 code implementation • 21 May 2021 • Danfeng Hong, Jingliang Hu, Jing Yao, Jocelyn Chanussot, Xiao Xiang Zhu
Moreover, to better assess multimodal baselines and the newly-proposed S2FL model, three multimodal RS benchmark datasets, i. e., Houston2013 -- hyperspectral and multispectral data, Berlin -- hyperspectral and synthetic aperture radar (SAR) data, Augsburg -- hyperspectral, SAR, and digital surface model (DSM) data, are released and used for land cover classification.
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).
2 code implementations • 15 Jan 2021 • Muhammad Ahmad, Sidrah Shabbir, Swalpa Kumar Roy, Danfeng Hong, Xin Wu, Jing Yao, Adil Mehmood Khan, Manuel Mazzara, Salvatore Distefano, Jocelyn Chanussot
Therefore, this survey discusses some strategies to improve the generalization performance of DL strategies which can provide some future guidelines.
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 • 13 Sep 2020 • Haixia Bi, Jing Yao, Zhiqiang Wei, Danfeng Hong, Jocelyn Chanussot
Polarimetric synthetic aperture radar (PolSAR) image classification has been investigated vigorously in various remote sensing applications.
1 code implementation • IEEE Geoscience and Remote Sensing Letters 2020 • Danfeng Hong, Lianru Gao, Renlong Hang, Bing Zhang, Jocelyn Chanussot
To overcome this limitation, we present a simple but effective multimodal DL baseline by following a deep encoder–decoder network architecture, EndNet for short, for the classification of hyperspectral and light detection and ranging (LiDAR) data.
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 • 6 Aug 2020 • Danfeng Hong, Lianru Gao, Jing Yao, Bing Zhang, Antonio Plaza, Jocelyn Chanussot
Convolutional neural networks (CNNs) have been attracting increasing attention in hyperspectral (HS) image classification, owing to their ability to capture spatial-spectral feature representations.
1 code implementation • 28 Jul 2020 • Lianru Gao, Danfeng Hong, Jing Yao, Bing Zhang, Paolo Gamba, Jocelyn Chanussot
However, the ability in the fusion of HS and MS images remains to be improved, particularly in large-scale scenes, due to the limited acquisition of HS images.
1 code implementation • 28 Jul 2020 • Ke Zheng, Lianru Gao, Wenzhi Liao, Danfeng Hong, Bing Zhang, Ximin Cui, Jocelyn Chanussot
In this work, an unsupervised deep learning-based fusion method - HyCoNet - that can solve the problems in HSI-MSI fusion without the prior PSF and SRF information is proposed.
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 • 17 Jul 2020 • Danfeng Hong, Jing Yao, Xin Wu, Jocelyn Chanussot, Xiao Xiang Zhu
In recent years, hyperspectral imaging, also known as imaging spectroscopy, has been paid an increasing interest in geoscience and remote sensing community.
no code implementations • 16 Jul 2020 • Xin Wu, Wei Li, Danfeng Hong, Jiaojiao Tian, Ran Tao, Qian Du
In addition, the generalization ability of Ms-AFt in dense remote sensing scenes is further verified on stereo aerial imagery of a large camping site.
1 code implementation • ECCV 2020 • Jing Yao, Danfeng Hong, Jocelyn Chanussot, Deyu Meng, Xiaoxiang Zhu, Zongben Xu
The recent advancement of deep learning techniques has made great progress on hyperspectral image super-resolution (HSI-SR).
no code implementations • 29 Jun 2020 • Nan Ge, Richard Bamler, Danfeng Hong, Xiao Xiang Zhu
This paper addresses the general problem of single-look multi-master SAR tomography.
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.
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.
1 code implementation • 5 Mar 2020 • Behnood Rasti, Danfeng Hong, Renlong Hang, Pedram Ghamisi, Xudong Kang, Jocelyn Chanussot, Jon Atli Benediktsson
The advances in feature extraction have been inspired by two fields of research, including the popularization of image and signal processing as well as machine (deep) learning, leading to two types of feature extraction approaches named shallow and deep techniques.
no code implementations • 4 Feb 2020 • Renlong Hang, Zhu Li, Pedram Ghamisi, Danfeng Hong, Guiyu Xia, Qingshan Liu
For the feature-level fusion, three different fusion strategies are evaluated, including the concatenation strategy, the maximization strategy, and the summation strategy.
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).
no code implementations • 13 Jun 2019 • Jingliang Hu, Danfeng Hong, Xiao Xiang Zhu
Multi-modal data fusion has recently been shown promise in classification tasks in remote sensing.
no code implementations • 27 May 2019 • Xin Wu, Danfeng Hong, Jocelyn Chanussot, Yang Xu, Ran Tao, Yue Wang
To this end, we propose a novel and efficient framework for geospatial object detection in this letter, called Fourier-based rotation-invariant feature boosting (FRIFB).
no code implementations • 28 Feb 2019 • Renlong Hang, Qingshan Liu, Danfeng Hong, Pedram Ghamisi
The first RNN layer is used to eliminate redundant information between adjacent spectral bands, while the second RNN layer aims to learn the complementary information from non-adjacent spectral bands.
no code implementations • 23 Jan 2019 • Xin Wu, Danfeng Hong, Jiaojiao Tian, Jocelyn Chanussot, Wei Li, Ran Tao
To this end, we propose a novel object detection framework, called optical remote sensing imagery detector (ORSIm detector), integrating diverse channel features extraction, feature learning, fast image pyramid matching, and boosting strategy.
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 • 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 • 7 2014 • Danfeng Hong, Jian Su, Qinggen Hong, Zhenkuan Pan, Guodong Wang
The experimental results are used to demonstrate the theoretica conclusion that the structure laver is stable foidifferent bluring scales The WRHOG method also proves to be an advanced and robust method of distinauishing blurredpalmprints.