Search Results for author:
Found 64 papers, 32 papers with code
DynamicVL: Benchmarking Multimodal Large Language Models for Dynamic City Understanding
Multimodal large language models have demonstrated remarkable capabilities in visual understanding, but their application to long-term Earth observation analysis remains limited, primarily focusing on single-temporal or bi-temporal imagery.
Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models
Uncertainty quantification is essential for assessing the reliability and trustworthiness of modern AI systems.
Enhancing Monocular Height Estimation via Sparse LiDAR-Guided Correction
Recently, models trained on synthetic data and refined through domain adaptation have shown remarkable performance in MHE, yet it remains unclear how these models make predictions or how reliable they truly are.
Joint Super-Resolution and Segmentation for 1-m Impervious Surface Area Mapping in China's Yangtze River Economic Belt
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 available Sentinel-2 imagery.
SARLANG-1M: A Benchmark for Vision-Language Modeling in SAR Image Understanding
Synthetic Aperture Radar (SAR) is a crucial remote sensing technology, enabling all-weather, day-and-night observation with strong surface penetration for precise and continuous environmental monitoring and analysis.
A Vision Centric Remote Sensing Benchmark
Multimodal Large Language Models (MLLMs) have achieved remarkable success in vision-language tasks but their remote sensing (RS) counterpart are relatively under explored.
MP-HSIR: A Multi-Prompt Framework for Universal Hyperspectral Image Restoration
In this paper, we propose MP-HSIR, a novel multi-prompt framework that effectively integrates spectral, textual, and visual prompts to achieve universal HSI restoration across diverse degradation types and intensities.
OpenEarthMap-SAR: A Benchmark Synthetic Aperture Radar Dataset for Global High-Resolution Land Cover Mapping
To bridge this gap and facilitate advancements in SAR-based geospatial analysis, we introduce OpenEarthMap-SAR, a benchmark SAR dataset, for global high-resolution land cover mapping.
BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response
In this paper, we present a BDA dataset using veRy-hIGH-resoluTion optical and SAR imagery (BRIGHT) to support AI-based all-weather disaster response.
Ranked #1 on
Building Damage Assessment
on BRIGHT
Neural Hierarchical Decomposition for Single Image Plant Modeling
Obtaining high-quality, practically usable 3D models of biological plants remains a significant challenge in computer vision and graphics.
CrossEarth: Geospatial Vision Foundation Model for Domain Generalizable Remote Sensing Semantic Segmentation
The field of Remote Sensing Domain Generalization (RSDG) has emerged as a critical and valuable research frontier, focusing on developing models that generalize effectively across diverse scenarios.
Foundation Models for Remote Sensing and Earth Observation: A Survey
This survey systematically reviews the emerging field of RSFMs.
Generalized Few-Shot Semantic Segmentation in Remote Sensing: Challenge and Benchmark
While previous datasets and benchmarks discussed the few-shot segmentation setting in remote sensing, we are the first to propose a generalized few-shot segmentation benchmark for remote sensing.
GaussianOcc: Fully Self-supervised and Efficient 3D Occupancy Estimation with Gaussian Splatting
We introduce GaussianOcc, a systematic method that investigates the two usages of Gaussian splatting for fully self-supervised and efficient 3D occupancy estimation in surround views.
Segment Anything with Multiple Modalities
It addresses three main challenges: 1) adaptation toward diverse non-RGB sensors for single-modal processing, 2) synergistic processing of multi-modal data via sensor fusion, and 3) mask-free training for different downstream tasks.
SynRS3D: A Synthetic Dataset for Global 3D Semantic Understanding from Monocular Remote Sensing Imagery
To further enhance its utility, we develop a novel multi-task unsupervised domain adaptation (UDA) method, RS3DAda, coupled with our synthetic dataset, which facilitates the RS-specific transition from synthetic to real scenarios for land cover mapping and height estimation tasks, ultimately enabling global monocular 3D semantic understanding based on synthetic data.
Local-to-Global Cross-Modal Attention-Aware Fusion for HSI-X Semantic Segmentation
The FEM is used to enhance complementary information by combining the feature from the other modality across direction-aware, position-sensitive, and channel-wise dimensions.
HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model
Accurate hyperspectral image (HSI) interpretation is critical for providing valuable insights into various earth observation-related applications such as urban planning, precision agriculture, and environmental monitoring.
Unsupervised Domain Adaptation Architecture Search with Self-Training for Land Cover Mapping
Thus, we proposed a simple yet effective framework to search for lightweight neural networks automatically for land cover mapping tasks under domain shifts.
ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model
Convolutional neural networks (CNN) and Transformers have made impressive progress in the field of remote sensing change detection (CD).
Ranked #1 on
Change Detection
on SECOND
Change Detection Between Optical Remote Sensing Imagery and Map Data via Segment Anything Model (SAM)
In this study, we explore unsupervised multimodal change detection between two key remote sensing data sources: optical high-resolution imagery and OpenStreetMap (OSM) data.
Submeter-level Land Cover Mapping of Japan
Deep learning has shown promising performance in submeter-level mapping tasks; however, the annotation cost of submeter-level imagery remains a challenge, especially when applied on a large scale.
SpectralGPT: Spectral Remote Sensing Foundation Model
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.
Enhancing Monocular Height Estimation from Aerial Images with Street-view Images
Accurate height estimation from monocular aerial imagery presents a significant challenge due to its inherently ill-posed nature.
Flooding Regularization for Stable Training of Generative Adversarial Networks
One of the main approaches to address this problem is to modify the loss function, often using regularization terms in addition to changing the type of adversarial losses.
ObjFormer: Learning Land-Cover Changes From Paired OSM Data and Optical High-Resolution Imagery via Object-Guided Transformer
Optical high-resolution imagery and OSM data are two important data sources of change detection (CD).
Exchange means change: an unsupervised single-temporal change detection framework based on intra- and inter-image patch exchange
Change detection (CD) is a critical task in studying the dynamics of ecosystems and human activities using multi-temporal remote sensing images.
Real-Time Semantic Segmentation: A Brief Survey & Comparative Study in Remote Sensing
With the success of efficient deep learning methods (i. e., efficient deep neural networks) for real-time semantic segmentation in computer vision, researchers have adopted these efficient deep neural networks in remote sensing image analysis.
SyntheWorld: A Large-Scale Synthetic Dataset for Land Cover Mapping and Building Change Detection
However, when it comes to remote sensing image processing, the creation of synthetic datasets becomes challenging due to the demand for larger-scale and more diverse 3D models.
Understanding Dark Scenes by Contrasting Multi-Modal Observations
Experiments show that our approach can effectively enhance dark scene understanding based on multi-modal images with limited semantics by shaping semantic-discriminative feature spaces.
Ranked #1 on
Semantic Segmentation
on LLRGBD-synthetic
A Simple Framework for 3D Occupancy Estimation in Autonomous Driving
In this work, we present a simple framework for 3D occupancy estimation, which is a CNN-based framework designed to reveal several key factors for 3D occupancy estimation, such as network design, optimization, and evaluation.
OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover Mapping
We introduce OpenEarthMap, a benchmark dataset, for global high-resolution land cover mapping.
Unsupervised Multimodal Change Detection Based on Structural Relationship Graph Representation Learning
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.
EOD: The IEEE GRSS Earth Observation Database
In the era of deep learning, annotated datasets have become a crucial asset to the remote sensing community.
Learning Mutual Modulation for Self-Supervised Cross-Modal Super-Resolution
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.
V4d: voxel for 4d novel view synthesis
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.
Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution with Subpixel Fusion
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
ES6D: A Computation Efficient and Symmetry-Aware 6D Pose Regression Framework
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.
Ranked #4 on
6D Pose Estimation
on DTTD-Mobile
Building Damage Mapping with Self-PositiveUnlabeled Learning
Humanitarian organizations must have fast and reliable data to respond to disasters.
Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral Unmixing
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.
Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing
Hyperspectral imaging, also known as image spectrometry, is a landmark technique in geoscience and remote sensing (RS).
Fast Hyperspectral Image Recovery via Non-iterative Fusion of Dual-Camera Compressive Hyperspectral Imaging
Specifically, the RGB measurement is utilized to estimate the coefficients, meanwhile the CASSI measurement is adopted to provide the orthogonal spectral basis.
Synthesizing Optical and SAR Imagery From Land Cover Maps and Auxiliary Raster Data
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).
Non-local Meets Global: An Iterative Paradigm for Hyperspectral Image Restoration
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.
Joint and Progressive Subspace Analysis (JPSA) with Spatial-Spectral Manifold Alignment for Semi-Supervised Hyperspectral Dimensionality Reduction
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).
Learning from Multimodal and Multitemporal Earth Observation Data for Building Damage Mapping
In this study, we have developed a global multisensor and multitemporal dataset for building damage mapping.
More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classification
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.
Guided Deep Decoder: Unsupervised Image Pair Fusion
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.
Illumination invariant hyperspectral image unmixing based on a digital surface model
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.
X-ModalNet: A Semi-Supervised Deep Cross-Modal Network for Classification of Remote Sensing Data
This paper addresses the problem of semi-supervised transfer learning with limited cross-modality data in remote sensing.
Breaking the Limits of Remote Sensing by Simulation and Deep Learning for Flood and Debris Flow Mapping
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.
Learning Convolutional Sparse Coding on Complex Domain for Interferometric Phase Restoration
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.
Hyperspectral Super-Resolution via Coupled Tensor Ring Factorization
In this paper, we propose a new model, named coupled tensor ring factorization (CTRF), for HSR.
Invariant Attribute Profiles: A Spatial-Frequency Joint Feature Extractor for Hyperspectral Image Classification
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).
Learning Shared Cross-modality Representation Using Multispectral-LiDAR and Hyperspectral Data
Due to the ever-growing diversity of the data source, multi-modality feature learning has attracted more and more attention.
Unsupervised and Unregistered Hyperspectral Image Super-Resolution with Mutual Dirichlet-Net
With this design, the network allows to extract correlated spectral and spatial information from unregistered images that better preserves the spectral information.
Learnable Manifold Alignment (LeMA) : A Semi-supervised Cross-modality Learning Framework for Land Cover and Land Use Classification
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?
CoSpace: Common Subspace Learning from Hyperspectral-Multispectral Correspondences
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.
Multisource and Multitemporal Data Fusion in Remote Sensing
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.
Non-local Meets Global: An Integrated Paradigm for Hyperspectral Denoising
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
An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing
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.
Joint & Progressive Learning from High-Dimensional Data for Multi-Label Classification
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).
Multi-temporal Sentinel-1 and -2 Data Fusion for Optical Image Simulation
In this paper, we present the optical image simulation from a synthetic aperture radar (SAR) data using deep learning based methods.
Hyperspectral pansharpening: a review
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.
