Search Results for author: Danfeng Hong

Found 51 papers, 23 papers with code

Superpixelwise Low-rank Approximation based Partial Label Learning for Hyperspectral Image Classification

1 code implementation27 May 2024 Shujun Yang, Yu Zhang, Yao Ding, Danfeng Hong

In this paper, we propose a novel superpixelwise low-rank approximation (LRA)-based partial label learning method, namely SLAP, which is the first to take into account partial label learning in HSI classification.

Hyperspectral Image Classification Partial Label Learning

A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers

1 code implementation23 Apr 2024 Muhammad Ahmad, Salvatore Distifano, Adil Mehmood Khan, Manuel Mazzara, Chenyu Li, Jing Yao, Hao Li, Jagannath Aryal, 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.

Classification feature selection +2

Linearly-evolved Transformer for Pan-sharpening

no code implementations19 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.

SpectralMamba: Efficient Mamba for Hyperspectral Image Classification

1 code implementation12 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.

Classification Hyperspectral Image Classification +1

Low-Rank Representations Meets Deep Unfolding: A Generalized and Interpretable Network for Hyperspectral Anomaly Detection

no code implementations23 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.

Anomaly Detection

S2MAE: A Spatial-Spectral Pretraining Foundation Model for Spectral Remote Sensing Data

no code implementations CVPR 2024 Xuyang Li, Danfeng Hong, Jocelyn Chanussot

The model efficiently captures local spectral consistency and spatial invariance using compact cube tokens demonstrating versatility to diverse input characteristics.

SpectralGPT: Spectral Remote Sensing Foundation Model

no code implementations13 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.

Change Detection Representation Learning +3

Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation using High-Resolution Domain Adaptation Networks

no code implementations26 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).

Domain Adaptation Segmentation +1

Spatial Gated Multi-Layer Perceptron for Land Use and Land Cover Mapping

1 code implementation9 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.

Image Classification

UIU-Net: U-Net in U-Net for Infrared Small Object Detection

1 code implementation2 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.

Object object-detection +2

Panchromatic and Multispectral Image Fusion via Alternating Reverse Filtering Network

no code implementations15 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.

Few-shot Learning with Class-Covariance Metric for Hyperspectral Image Classification

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.

Few-Shot Learning Hyperspectral Image Classification

Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Review

no code implementations13 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.

Anomaly Detection Super-Resolution +1

Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution with Subpixel Fusion

1 code implementation7 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

Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review

no code implementations3 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.

Earth Observation

Multimodal Fusion Transformer for Remote Sensing Image Classification

2 code implementations31 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).

Classification Image Classification +2

Deep Learning for UAV-based Object Detection and Tracking: A Survey

no code implementations25 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).

Management Object +3

SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers

2 code implementations7 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.

Classification Hyperspectral Image Classification

Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification with A Shared and Specific Feature Learning Model

1 code implementation21 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.

Land Cover Classification

Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral Unmixing

1 code implementation21 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.

Hyperspectral Unmixing

Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing

no code implementations2 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).

Joint and Progressive Subspace Analysis (JPSA) with Spatial-Spectral Manifold Alignment for Semi-Supervised Hyperspectral Dimensionality Reduction

1 code implementation21 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).

Dimensionality Reduction

PolSAR Image Classification Based on Robust Low-Rank Feature Extraction and Markov Random Field

no code implementations13 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.

Classification Data Augmentation +2

Deep Encoder-Decoder Networks for Classification of Hyperspectral and LiDAR Data

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.

Classification Decoder

More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classification

1 code implementation12 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.

Classification General Classification +2

Graph Convolutional Networks for Hyperspectral Image Classification

1 code implementation6 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.

Classification General Classification +1

Spectral Superresolution of Multispectral Imagery with Joint Sparse and Low-Rank Learning

1 code implementation28 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.

Coupled Convolutional Neural Network with Adaptive Response Function Learning for Unsupervised Hyperspectral Super-Resolution

1 code implementation28 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.


Guided Deep Decoder: Unsupervised Image Pair Fusion

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.

Decoder Pansharpening

Spatial-Spectral Manifold Embedding of Hyperspectral Data

no code implementations17 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.

Vehicle Detection of Multi-source Remote Sensing Data Using Active Fine-tuning Network

no code implementations16 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.

Transfer Learning

Single-Look Multi-Master SAR Tomography: An Introduction

no code implementations29 Jun 2020 Nan Ge, Richard Bamler, Danfeng Hong, Xiao Xiang Zhu

This paper addresses the general problem of single-look multi-master SAR tomography.

Learning Convolutional Sparse Coding on Complex Domain for Interferometric Phase Restoration

no code implementations6 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.

Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep (Overview and Toolbox)

1 code implementation5 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.

General Classification Hyperspectral Image Classification

Classification of Hyperspectral and LiDAR Data Using Coupled CNNs

no code implementations4 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.

Classification General Classification

Learning Shared Cross-modality Representation Using Multispectral-LiDAR and Hyperspectral Data

no code implementations18 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.

Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification

no code implementations18 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).

Attribute General Classification +1

Fourier-based Rotation-invariant Feature Boosting: An Efficient Framework for Geospatial Object Detection

no code implementations27 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).

Object object-detection +1

Cascaded Recurrent Neural Networks for Hyperspectral Image Classification

no code implementations28 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.

Classification General Classification +1

ORSIm Detector: A Novel Object Detection Framework in Optical Remote Sensing Imagery Using Spatial-Frequency Channel Features

no code implementations23 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.

Novel Object Detection object-detection +1

Learnable Manifold Alignment (LeMA) : A Semi-supervised Cross-modality Learning Framework for Land Cover and Land Use Classification

no code implementations9 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?

General Classification

CoSpace: Common Subspace Learning from Hyperspectral-Multispectral Correspondences

no code implementations30 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.

Classification General Classification +1

An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing

no code implementations29 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.

Dictionary Learning Hyperspectral Unmixing

Joint & Progressive Learning from High-Dimensional Data for Multi-Label Classification

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).

General Classification Multi-Label Classification +1

Blurred Palmprint Recognition Based on Stable-FeatureExtraction Using a Vese-Osher Decomposition Model

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.

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