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MambaX: Image Super-Resolution with State Predictive Control
Chenyu Li, Danfeng Hong, Bing Zhang +3
Image super-resolution (SR) is a critical technology for overcoming the inherent hardware limitations of sensors. However, existing approaches mainly focus on directly enhancing th…
Hyperspectral Imaging
Danfeng Hong, Chenyu Li, Naoto Yokoya +6
Hyperspectral imaging (HSI) is an advanced sensing modality that simultaneously captures spatial and spectral information, enabling non-invasive, label-free analysis of material, c…
Joint Super-Resolution and Segmentation for 1-m Impervious Surface Area Mapping in China's Yangtze River Economic Belt
Jie Deng, Danfeng Hong, Chenyu Li +1
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 availabl…
MP-HSIR: A Multi-Prompt Framework for Universal Hyperspectral Image Restoration
Zhehui Wu, Yong Chen, Naoto Yokoya +1
Hyperspectral images (HSIs) often suffer from diverse and unknown degradations during imaging, leading to severe spectral and spatial distortions. Existing HSI restoration methods…
SpectralGPT: Spectral Remote Sensing Foundation Model
Danfeng Hong, Bing Zhang, Xuyang Li +11
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. Whil…
Real-Time Semantic Segmentation: A Brief Survey & Comparative Study in Remote Sensing
Clifford Broni-Bediako, Junshi Xia, Naoto Yokoya
Real-time semantic segmentation of remote sensing imagery is a challenging task that requires a trade-off between effectiveness and efficiency. It has many applications including t…
Understanding Dark Scenes by Contrasting Multi-Modal Observations
Xiaoyu Dong, Naoto Yokoya
Understanding dark scenes based on multi-modal image data is challenging, as both the visible and auxiliary modalities provide limited semantic information for the task. Previous m…
A Simple Framework for 3D Occupancy Estimation in Autonomous Driving
Wanshui Gan, Ningkai Mo, Hongbin Xu +1
The task of estimating 3D occupancy from surrounding-view images is an exciting development in the field of autonomous driving, following the success of Bird's Eye View (BEV) perce…
OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping
Junshi Xia, Naoto Yokoya, Bruno Adriano +1
We introduce OpenEarthMap, a benchmark dataset, for global high-resolution land cover mapping. OpenEarthMap consists of 2.2 million segments of 5000 aerial and satellite images cov…
Unsupervised Multimodal Change Detection Based on Structural Relationship Graph Representation Learning
Hongruixuan Chen, Naoto Yokoya, Chen Wu +1
Unsupervised multimodal change detection is a practical and challenging topic that can play an important role in time-sensitive emergency applications. To address the challenge tha…
EOD: The IEEE GRSS Earth Observation Database
Michael Schmitt, Pedram Ghamisi, Naoto Yokoya +1
In the era of deep learning, annotated datasets have become a crucial asset to the remote sensing community. In the last decade, a plethora of different datasets was published, eac…
Learning Mutual Modulation for Self-Supervised Cross-Modal Super-Resolution
Xiaoyu Dong, Naoto Yokoya, Longguang Wang +1
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…
V4d: voxel for 4d novel view synthesis
Wanshui Gan, Hongbin Xu, Yi Huang +2
Neural radiance fields have made a remarkable breakthrough in the novel view synthesis task at the 3D static scene. However, for the 4D circumstance (e.g., dynamic scene), the perf…
ES6D: A Computation Efficient and Symmetry-Aware 6D Pose Regression Framework
Ningkai Mo, Wanshui Gan, Naoto Yokoya +1
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 symm…
Building Damage Mapping with Self-PositiveUnlabeled Learning
Junshi Xia, Naoto Yokoya, Bruno Adriano
Humanitarian organizations must have fast and reliable data to respond to disasters. Deep learning approaches are difficult to implement in real-world disasters because it might be…
Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing
Danfeng Hong, Wei He, Naoto Yokoya +5
Hyperspectral imaging, also known as image spectrometry, is a landmark technique in geoscience and remote sensing (RS). In the past decade, enormous efforts have been made to proce…
Synthesizing Optical and SAR Imagery From Land Cover Maps and Auxiliary Raster Data
Gerald Baier, Antonin Deschemps, Michael Schmitt +1
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).…
Joint and Progressive Subspace Analysis (JPSA) with Spatial-Spectral Manifold Alignment for Semi-Supervised Hyperspectral Dimensionality Reduction
Danfeng Hong, Naoto Yokoya, Jocelyn Chanussot +2
Conventional nonlinear subspace learning techniques (e.g., manifold learning) usually introduce some drawbacks in explainability (explicit mapping) and cost-effectiveness (lineariz…
Learning from Multimodal and Multitemporal Earth Observation Data for Building Damage Mapping
Bruno Adriano, Naoto Yokoya, Junshi Xia +4
Earth observation technologies, such as optical imaging and synthetic aperture radar (SAR), provide excellent means to monitor ever-growing urban environments continuously. Notably…
More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classification
Danfeng Hong, Lianru Gao, Naoto Yokoya +4
Classification and identification of the materials lying over or beneath the Earth's surface have long been a fundamental but challenging research topic in geoscience and remote se…
Illumination invariant hyperspectral image unmixing based on a digital surface model
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 s…
X-ModalNet: A Semi-Supervised Deep Cross-Modal Network for Classification of Remote Sensing Data
Danfeng Hong, Naoto Yokoya, Gui-Song Xia +2
This paper addresses the problem of semi-supervised transfer learning with limited cross-modality data in remote sensing. A large amount of multi-modal earth observation images, su…
Breaking the Limits of Remote Sensing by Simulation and Deep Learning for Flood and Debris Flow Mapping
Naoto Yokoya, Kazuki Yamanoi, Wei He +4
We propose a framework that estimates inundation depth (maximum water level) and debris-flow-induced topographic deformation from remote sensing imagery by integrating deep learnin…
Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification
Danfeng Hong, Xin Wu, Pedram Ghamisi +3
Up to the present, an enormous number of advanced techniques have been developed to enhance and extract the spatially semantic information in hyperspectral image processing and ana…