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Multimodal Image Classification Based on Convolutional Network and Attention-Based Hidden Markov Random Field
Elham Kordi Ghasrodashti;Peyman Adibi;Hossein Karshenas;Hamidreza Baradaran Kashani;Jocelyn Chanussot
In this article, a multimodal deep architecture for classification of light detection and ranging (LiDAR) and hyperspectral image (HSI) is proposed, acquiring the knowledge of both modalities by leveraging modality-specific information and their complementary information. The proposed model consists of two main steps. First, to improve the performance of a 2-D convolutional neural network (2DCNN),...Show More
PDCNet: A Polarimetric Data-Enhanced Contrastive Learning Network for PolSAR Land Cover Classification
Bo Ren;Chaoyue Hua;Biao Hou;Jian Lv;Chen Yang;Licheng Jiao;Jocelyn Chanussot
Polarimetric synthetic aperture radar (PolSAR) has rich polarization information, offering an efficient and reliable means of collecting information. However, how to effectively leverage these complex data to extract polarization features remains a key challenge. Recently, contrastive learning has been successful in computer vision, with fewer labeled and a large amount of unlabeled data. Inspired...Show More
HyLiOSR: Staged Progressive Learning for Joint Open-Set Recognition of Hyperspectral and LiDAR Data
Bobo Xi;Mingshuo Cai;Jiaojiao Li;Zhengjue Wang;Shou Feng;Yunsong Li;Jocelyn Chanussot
The joint classification of hyperspectral images (HSIs) and light detection and ranging (LiDAR) data have seen significant advancements in recent research. However, it would be more practical if we could simultaneously detect the unknown classes in a more realistic open-set scenario. In this article, we introduce a novel open-set recognition (OSR) method for HSI and LiDAR data, termed HyLiOSR, whi...Show More
MAGS: Max-Gap Loss-Guided Siamese-Reconstruction Network for Hyperspectral Image Partial Label Learning
Xiaoyu Tian;Fulin Luo;Chuan Fu;Tan Guo;Bo Du;Jocelyn Chanussot
Due to the powerful feature extraction capabilities of deep learning, a series of deep learning-based methods for hyperspectral image (HSI) classification have been proposed and achieved satisfactory performance. However, most of these methods require a large number of labeled data, and the collection of completely accurate pixel-level labeled HSI data is difficult, resulting from the intricate la...Show More
Nonlocal and Local Feature-Coupled Self-Supervised Network for Hyperspectral Anomaly Detection
Hyperspectral anomaly detection (HAD) aims to locate targets deviating from the background distribution in hyperspectral images (HSIs) without requiring prior knowledge. Most current deep learning-based HAD methods struggle to effectively distinguish anomalies due to limited utilization of supervision information and intrinsic nonlocal self-similarity in HSIs. To this end, this article proposes a ...Show More
A Survey of Sample-Efficient Deep Learning for Change Detection in Remote Sensing: Tasks, strategies, and challenges
Lei Ding;Danfeng Hong;Maofan Zhao;Hongruixuan Chen;Chenyu Li;Jie Deng;Naoto Yokoya;Lorenzo Bruzzone;Jocelyn Chanussot
IEEE Geoscience and Remote Sensing Magazine
Year: 2025 | Early Access Article |
In the last decade, the rapid development of deep learning (DL) has made it possible to perform automatic, accurate, and robust change detection (CD) on large volumes of remote sensing images (RSIs). However, despite advances in CD methods, their practical application in real-world contexts remains limited because of diverse input data and the applicational context. For example, the collected RSIs...Show More
Kolmogorov–Arnold Network for Hyperspectral Change Detection
Hyperspectral change detection (HCD) techniques to monitor Earth’s surface processes advanced markedly in recent years. Seasonal variations and associated spectral signatures as well as nonlinear noise patterns emanating from sensors and atmospheric sources pose fundamental challenges in HCD. Advanced deep learning models, such as those that leverage convolutional neural networks (3D-Siamese) or t...Show More
Fully-Connected Transformer for Multi-Source Image Fusion
Xiao Wu;Zi-Han Cao;Ting-Zhu Huang;Liang-Jian Deng;Jocelyn Chanussot;Gemine Vivone
Multi-source image fusion combines the information coming from multiple images into one data, thus improving imaging quality. This topic has aroused great interest in the community. How to integrate information from different sources is still a big challenge, although the existing self-attention based transformer methods can capture spatial and channel similarities. In this paper, we first discuss...Show More
Iteratively Regularizing Hyperspectral and Multispectral Image Fusion With Framelets
Xiangfei Shen;Lihui Chen;Haijun Liu;Xichuan Zhou;Wenxing Bao;Ling Tian;Gemine Vivione;Jocelyn Chanussot
Hyperspectral (HS) and multispectral (MS) image fusion mainly focuses on transferring spatial details from high spatial resolution (HR) MS images (MSIs) to low spatial resolution (LR) HS images (HSIs). Recent investigations introduce prior regularizations, such as sparsity, low-rankness, or total variation, to enhance fusion quality by denoising latent factor images. This article proposes a new HS...Show More
Subpixel Spectral Variability Network for Hyperspectral Image Classification
Zhu Han;Jin Yang;Lianru Gao;Zhiqiang Zeng;Bing Zhang;Jocelyn Chanussot
Deep learning-based frameworks have shown great potential in the field of hyperspectral image (HSI) classification owing to their superior modeling capabilities. However, the existence of mixed pixels and spectral heterogeneity limits the discriminant performance of the classifier, which makes it impossible to distinguish the mixed spectra effectively in actual scenarios. To address this gap, we p...Show More
Transductive Few-Shot Learning With Enhanced Spectral-Spatial Embedding for Hyperspectral Image Classification
Bobo Xi;Yun Zhang;Jiaojiao Li;Yan Huang;Yunsong Li;Zan Li;Jocelyn Chanussot
Few-shot learning (FSL) has been rapidly developed in the hyperspectral image (HSI) classification, potentially eliminating time-consuming and costly labeled data acquisition requirements. Effective feature embedding is empirically significant in FSL methods, which is still challenging for the HSI with rich spectral-spatial information. In addition, compared with inductive FSL, transductive models...Show More
IcGAN4ColSAR: A Novel Multispectral Conditional Generative Adversarial Network Approach for SAR Image Colorization
Kangqing Shen;Gemine Vivone;Simone Lolli;Michael Schmitt;Xiaoyuan Yang;Jocelyn Chanussot
SAR colorization aims to enrich gray-scale SAR images with color while ensuring the preservation of original radiometric and spatial details. However, researchers often limit themselves to using only the red, green, and blue bands of a multispectral image as the source of color information, coupled with a single-polarization channel from the SAR image. This approach neglects the intrinsic characte...Show More
Enhanced Deep Image Prior for Unsupervised Hyperspectral Image Super-Resolution
Jiaxin Li;Ke Zheng;Lianru Gao;Zhu Han;Zhi Li;Jocelyn Chanussot
Depending on a large-scale paired dataset of low-resolution hyperspectral image (LrHSI), high-resolution multispectral image (HrMSI), and corresponding high-resolution hyperspectral image (HrHSI), the supervised paradigm has achieved impressive performance in the hyperspectral image super-resolution (HISR). However, the intrinsic data-intensive manner hinders its further application in real scenar...Show More
MCTGCL: Mixed CNN–Transformer for Mars Hyperspectral Image Classification With Graph Contrastive Learning
Bobo Xi;Yun Zhang;Jiaojiao Li;Tie Zheng;Xunfeng Zhao;Haitao Xu;Changbin Xue;Yunsong Li;Jocelyn Chanussot
Hyperspectral image (HSI) classification has been extensively studied in the context of Earth observation. However, its application in Mars exploration remains limited. Although convolutional neural networks (CNNs) have proven effective in HSI processing, their local receptive fields hinder their ability to capture long-range features. Transformers excel in global modeling and perform well in HSI ...Show More
Conditional Gaussian Enhanced Dense Correlation Matching for Cross-Category Land Cover Classification
As the requirements for the downstream tasks of land cover classification (LCC) continue to increase, the category system used for LCC is constantly being refined. This causes previous land cover products and manually annotated training samples to become quickly outdated. Meanwhile, manually annotating samples with more refined categories is extremely time-consuming. To address this impasse, a cro...Show More
Hyperspectral Image Classification With Mamba
Zhaojie Pan;Chenyu Li;Antonio Plaza;Jocelyn Chanussot;Danfeng Hong
Local and global spectral and spatial information is crucial for hyperspectral image (HSI) classification. However, modeling the global context has been challenging due to the limitations of receptive fields and quadratic complexity. Mamba’s ability to leverage long-range dependencies with linear computational complexity offers an effective approach to alleviate this issue; however, it does lead t...Show More
Hyperspectral Pansharpening: Critical review, tools, and future perspectives
Matteo Ciotola;Giuseppe Guarino;Gemine Vivone;Giovanni Poggi;Jocelyn Chanussot;Antonio Plaza;Giuseppe Scarpa
Hyperspectral (HS) pansharpening consists of fusing a high-resolution panchromatic (PAN) band and a low-resolution HS image to obtain a new image with high resolution in both the spatial and spectral domains. These remote sensing products are valuable for a wide range of applications, driving ever-growing research efforts. Nonetheless, results still do not meet application demands. In part, this c...Show More
Graph U-Net With Topology-Feature Awareness Pooling for Hyperspectral Image Classification
Rong Chen;Gemine Vivone;Guanghui Li;Chenglong Dai;Danfeng Hong;Jocelyn Chanussot
Nowadays, various graph convolutional networks (GCNs) to process graph-structured data have been proposed for hyperspectral image (HSI) classification. Nevertheless, most GCN-based HSI classification methods emphasize graph node feature aggregation instead of graph pooling, resulting in them being shallow networks and unable to extract deep discriminative features. Besides, to obtain the new graph...Show More
Deep Learning in Remote Sensing Image Fusion: Methods, protocols, data, and future perspectives
Gemine Vivone;Liang-Jian Deng;Shangqi Deng;Danfeng Hong;Menghui Jiang;Chenyu Li;Wei Li;Huanfeng Shen;Xiao Wu;Jin-Liang Xiao;Jing Yao;Mengmeng Zhang;Jocelyn Chanussot;Salvador García;Antonio Plaza
Image fusion can be conducted at different levels, with pixel-level image fusion involving the direct combination of original information from source images. The objective of methods falling under this category is to generate a fused image that enhances both visual perception and subsequent processing tasks. This survey article draws upon research findings in pixel-level image fusion for remote se...Show More
Probing Synergistic High-Order Interaction for Multi-Modal Image Fusion
Man Zhou;Naishan Zheng;Xuanhua He;Danfeng Hong;Jocelyn Chanussot
Multi-modal image fusion aims to generate a fused image by integrating and distinguishing the cross-modality complementary information from multiple source images. While the cross-attention mechanism with global spatial interactions appears promising, it only captures second-order spatial interactions, neglecting higher-order interactions in both spatial and channel dimensions. This limitation ham...Show More
VOGTNet: Variational Optimization-Guided Two-Stage Network for Multispectral and Panchromatic Image Fusion
Peng Wang;Zhongchen He;Bo Huang;Mauro Dalla Mura;Henry Leung;Jocelyn Chanussot
Multispectral image (MS) and panchromatic image (PAN) fusion, which is also named as multispectral pansharpening, aims to obtain MS with high spatial resolution and high spectral resolution. However, due to the usual neglect of noise and blur generated in the imaging and transmission phases of data during training, many deep learning (DL) pansharpening methods fail to perform on the dataset contai...Show More
Learning Disentangled Priors for Hyperspectral Anomaly Detection: A Coupling Model-Driven and Data-Driven Paradigm
Chenyu Li;Bing Zhang;Danfeng Hong;Xiuping Jia;Antonio Plaza;Jocelyn Chanussot
Accurately distinguishing between background and anomalous objects within hyperspectral images poses a significant challenge. The primary obstacle lies in the inadequate modeling of prior knowledge, leading to a performance bottleneck in hyperspectral anomaly detection (HAD). In response to this challenge, we put forth a groundbreaking coupling paradigm that combines model-driven low-rank represen...Show More
Prospects for Mitigating Spectral Variability in Tropical Species Classification Using Self-Supervised Learning
Colin Prieur;Nassim Ait Ali Braham;Paul Tresson;Grégoire Vincent;Jocelyn Chanussot
2024 14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)
Year: 2024 | Conference Paper |
Airborne hyperspectral imaging is a promising method for identifying tropical species, but spectral variability between acquisitions hinders consistent results. This paper proposes using Self-Supervised Learning (SSL) to encode spectral features that are robust to abiotic variability and relevant for species identification. By employing the state-of-the-art Barlow-Twins approach on repeated spectr...Show More
Deformable Kolmogorov-Arnold Networks For Hyperspectral Image Classification
2024 14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)
Year: 2024 | Conference Paper |
Hyperspectral image classification (HSIC) has witnessed significant advancements with the advent of deep learning, particularly through the exploitation of Convolutional Neural Networks (CNNs). However, CNNs face notable challenges, including difficulty in capturing long-range dependencies, constrained receptive fields, and high computational over-head. To overcome these limitations, we propose a ...Show More
LM-Net: A Lightweight Matching Network for Remote Sensing Image Matching and Registration
Dou Quan;Zhe Wang;Chonghua Lv;Shuang Wang;Yi Li;Bo Ren;Jocelyn Chanussot;Licheng Jiao
Deep feature learning methods have shown significant advantages over handcrafted feature-based methods in remote sensing image matching and registration. Existing deep learning methods usually introduce complex modules into the deep convolutional network for more robust feature learning. However, they usually require high computation and memory resources for the computing device and have expensive...Show More
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