Papers Land Cover Classification
“Land Cover Classification” 태그가 달린 논문 151편 · 필터 해제
Baltimore Atlas: FreqWeaver Adapter for Semi-supervised Ultra-high Spatial Resolution Land Cover Classification
Ultra-high Spatial Resolution Land Cover Classification is essential for fine-grained land cover analysis, yet it remains challenging due to the high cost of pixel-level annotations, significant scale variation, and the …
Land Cover ClassificationSegmentationBeyond Pretty Pictures: Combined Single- and Multi-Image Super-resolution for Sentinel-2 Images
Super-resolution aims to increase the resolution of satellite images by reconstructing high-frequency details, which go beyond na\"ive upsampling. This has particular relevance for Earth observation missions like Sentine…
Earth ObservationImage Super-ResolutionLand Cover ClassificationSuper-ResolutionHyperPointFormer: Multimodal Fusion in 3D Space with Dual-Branch Cross-Attention Transformers
Multimodal remote sensing data, including spectral and lidar or photogrammetry, is crucial for achieving satisfactory land-use / land-cover classification results in urban scenes. So far, most studies have been conducted…
Land Cover ClassificationSupervised and self-supervised land-cover segmentation & classification of the Biesbosch wetlands
Accurate wetland land-cover classification is essential for environmental monitoring, biodiversity assessment, and sustainable ecosystem management. However, the scarcity of annotated data, especially for high-resolution…
Land Cover ClassificationSelf-Supervised LearningGeneralizable Multispectral Land Cover Classification via Frequency-Aware Mixture of Low-Rank Token Experts
We introduce Land-MoE, a novel approach for multispectral land cover classification (MLCC). Spectral shift, which emerges from disparities in sensors and geospatial conditions, poses a significant challenge in this domai…
Domain AdaptationDomain GeneralizationLand Cover ClassificationSemantic SegmentationHyperspectralMAE: The Hyperspectral Imagery Classification Model using Fourier-Encoded Dual-Branch Masked Autoencoder
Hyperspectral imagery provides rich spectral detail but poses unique challenges because of its high dimensionality in both spatial and spectral domains. We propose \textit{HyperspectralMAE}, a Transformer-based foundatio…
Hyperspectral Image ClassificationImage ReconstructionLand Cover ClassificationTransfer LearningPrototype-Based Information Compensation Network for Multi-Source Remote Sensing Data Classification
Multi-source remote sensing data joint classification aims to provide accuracy and reliability of land cover classification by leveraging the complementary information from multiple data sources. Existing methods confron…
Land Cover ClassificationCore-Set Selection for Data-efficient Land Cover Segmentation
The increasing accessibility of remotely sensed data and the potential of such data to inform large-scale decision-making has driven the development of deep learning models for many Earth Observation tasks. Traditionally…
Earth ObservationImage SegmentationLand Cover ClassificationSemantic SegmentationPAD: Phase-Amplitude Decoupling Fusion for Multi-Modal Land Cover Classification
The fusion of Synthetic Aperture Radar (SAR) and RGB imagery for land cover classification remains challenging due to modality heterogeneity and the underutilization of spectral complementarity. Existing methods often fa…
Land Cover ClassificationGeographical Context Matters: Bridging Fine and Coarse Spatial Information to Enhance Continental Land Cover Mapping
Land use and land cover mapping from Earth Observation (EO) data is a critical tool for sustainable land and resource management. While advanced machine learning and deep learning algorithms excel at analyzing EO imagery…
Computational EfficiencyEarth ObservationLand Cover ClassificationSAR-to-RGB Translation with Latent Diffusion for Earth Observation
Earth observation satellites like Sentinel-1 (S1) and Sentinel-2 (S2) provide complementary remote sensing (RS) data, but S2 images are often unavailable due to cloud cover or data gaps. To address this, we propose a dif…
Cloud RemovalEarth ObservationLand Cover ClassificationTranslationFlexiMo: A Flexible Remote Sensing Foundation Model
The rapid expansion of multi-source satellite imagery drives innovation in Earth observation, opening unprecedented opportunities for Remote Sensing Foundation Models to harness diverse data. However, many existing model…
Cloud DetectionEarth ObservationLand Cover Classificationmodel+1A Survey on Remote Sensing Foundation Models: From Vision to Multimodality
The rapid advancement of remote sensing foundation models, particularly vision and multimodal models, has significantly enhanced the capabilities of intelligent geospatial data interpretation. These models combine variou…
Change DetectionLand Cover ClassificationTransfer LearningUncertainty-aware Bayesian machine learning modelling of land cover classification
Land cover classification involves the production of land cover maps, which determine the type of land through remote sensing imagery. Over recent years, such classification is being performed by machine learning classif…
ClassificationLand Cover ClassificationCerraData-4MM: A multimodal benchmark dataset on Cerrado for land use and land cover classification
The Cerrado faces increasing environmental pressures, necessitating accurate land use and land cover (LULC) mapping despite challenges such as class imbalance and visually similar categories. To address this, we present …
Land Cover ClassificationSemantic SegmentationASANet: Asymmetric Semantic Aligning Network for RGB and SAR image land cover classification
Synthetic Aperture Radar (SAR) images have proven to be a valuable cue for multimodal Land Cover Classification (LCC) when combined with RGB images. Most existing studies on cross-modal fusion assume that consistent feat…
Land Cover ClassificationPattern Integration and Enhancement Vision Transformer for Self-Supervised Learning in Remote Sensing
Recent self-supervised learning (SSL) methods have demonstrated impressive results in learning visual representations from unlabeled remote sensing images. However, most remote sensing images predominantly consist of sce…
Change DetectionLand Cover Classificationobject-detectionObject Detection+2Beyond Grid Data: Exploring Graph Neural Networks for Earth Observation
Earth Observation (EO) data analysis has been significantly revolutionized by deep learning (DL), with applications typically limited to grid-like data structures. Graph Neural Networks (GNNs) emerge as an important inno…
Earth ObservationLand Cover ClassificationSFA-Net: Semantic Feature Adjustment Network for Remote Sensing Image Segmentation
Advances in deep learning and computer vision techniques have made impacts in the field of remote sensing, enabling efficient data analysis for applications such as land cover classification and change detection. Convolu…
Change DetectionDecoderImage SegmentationLand Cover Classification+1DDU-Net: A Domain Decomposition-Based CNN for High-Resolution Image Segmentation on Multiple GPUs
The segmentation of ultra-high resolution images poses challenges such as loss of spatial information or computational inefficiency. In this work, a novel approach that combines encoder-decoder architectures with domain …
DecoderImage SegmentationLand Cover ClassificationSemantic Segmentation