Pixel-Wise Multimodal Contrastive Learning for Remote Sensing Images

• URL: http://arxiv.org/abs/2601.04127v1
• Date: Wed, 07 Jan 2026 17:41:11 GMT
• Title: Pixel-Wise Multimodal Contrastive Learning for Remote Sensing Images
• Authors: Leandro Stival, Ricardo da Silva Torres, Helio Pedrini,
• Abstract summary: We propose a novel approach that leverages pixel-wise two-dimensional (2D) representations to encode visual property variations from satellite images.<n>We also introduce PIxel-wise Multimodal Contrastive (PIMC), a new multimodal self-supervision approach.<n>Our experimental results show that the use of 2D representations significantly enhances feature extraction from SITS.
• Score: 3.346542738111468
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Satellites continuously generate massive volumes of data, particularly for Earth observation, including satellite image time series (SITS). However, most deep learning models are designed to process either entire images or complete time series sequences to extract meaningful features for downstream tasks. In this study, we propose a novel multimodal approach that leverages pixel-wise two-dimensional (2D) representations to encode visual property variations from SITS more effectively. Specifically, we generate recurrence plots from pixel-based vegetation index time series (NDVI, EVI, and SAVI) as an alternative to using raw pixel values, creating more informative representations. Additionally, we introduce PIxel-wise Multimodal Contrastive (PIMC), a new multimodal self-supervision approach that produces effective encoders based on two-dimensional pixel time series representations and remote sensing imagery (RSI). To validate our approach, we assess its performance on three downstream tasks: pixel-level forecasting and classification using the PASTIS dataset, and land cover classification on the EuroSAT dataset. Moreover, we compare our results to state-of-the-art (SOTA) methods on all downstream tasks. Our experimental results show that the use of 2D representations significantly enhances feature extraction from SITS, while contrastive learning improves the quality of representations for both pixel time series and RSI. These findings suggest that our multimodal method outperforms existing models in various Earth observation tasks, establishing it as a robust self-supervision framework for processing both SITS and RSI. Code avaliable on

Related papers

• A Recipe for Improving Remote Sensing VLM Zero Shot Generalization [0.4427533728730559]
  We present two novel image-caption datasets for training of remote sensing foundation models.<n>The first dataset pairs aerial and satellite imagery with captions generated by Gemini using landmarks extracted from Google Maps.<n>The second dataset utilizes public web images and their corresponding alt-text, filtered for the remote sensing domain.
  arXiv  Detail & Related papers  (2025-03-10T21:09:02Z)
• Galileo: Learning Global & Local Features of Many Remote Sensing Modalities [34.71460539414284]
  We present a novel self-supervised learning algorithm that extracts multi-scale features across a flexible set of input modalities through masked modeling.<n>Our Galileo is a single generalist model that outperforms SoTA specialist models for satellite images and pixel time series across eleven benchmarks and multiple tasks.
  arXiv  Detail & Related papers  (2025-02-13T14:21:03Z)
• LargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous Driving [88.85002707211777]
  LargeAD is a versatile and scalable framework designed for large-scale 3D pretraining across diverse real-world driving datasets.<n>Our framework leverages VFMs to extract semantically rich superpixels from 2D images, which are aligned with LiDAR point clouds.<n>This alignment facilitates cross-modal representation learning, enhancing the semantic consistency between 2D and 3D data.
  arXiv  Detail & Related papers  (2025-01-07T18:59:59Z)
• Deep Multimodal Fusion for Semantic Segmentation of Remote Sensing Earth Observation Data [0.08192907805418582]
  This paper proposes a late fusion deep learning model (LF-DLM) for semantic segmentation. One branch integrates detailed textures from aerial imagery captured by UNetFormer with a Multi-Axis Vision Transformer (ViT) backbone. The other branch captures complex-temporal dynamics from the Sentinel-2 satellite imageMax time series using a U-ViNet with Temporal Attention (U-TAE)
  arXiv  Detail & Related papers  (2024-10-01T07:50:37Z)
• Rethinking Transformers Pre-training for Multi-Spectral Satellite Imagery [78.43828998065071]
  Recent advances in unsupervised learning have demonstrated the ability of large vision models to achieve promising results on downstream tasks. Such pre-training techniques have also been explored recently in the remote sensing domain due to the availability of large amount of unlabelled data. In this paper, we re-visit transformers pre-training and leverage multi-scale information that is effectively utilized with multiple modalities.
  arXiv  Detail & Related papers  (2024-03-08T16:18:04Z)
• A Model-data-driven Network Embedding Multidimensional Features for Tomographic SAR Imaging [5.489791364472879]
  We propose a new model-data-driven network to achieve tomoSAR imaging based on multi-dimensional features. We add two 2D processing modules, both convolutional encoder-decoder structures, to enhance multi-dimensional features of the imaging scene effectively. Compared with the conventional CS-based FISTA method and DL-based gamma-Net method, the result of our proposed method has better performance on completeness while having decent imaging accuracy.
  arXiv  Detail & Related papers  (2022-11-28T02:01:43Z)
• Learning Enriched Features for Fast Image Restoration and Enhancement [166.17296369600774]
  This paper presents a holistic goal of maintaining spatially-precise high-resolution representations through the entire network. We learn an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details. Our approach achieves state-of-the-art results for a variety of image processing tasks, including defocus deblurring, image denoising, super-resolution, and image enhancement.
  arXiv  Detail & Related papers  (2022-04-19T17:59:45Z)
• Inertial Sensor Data To Image Encoding For Human Action Recognition [0.0]
  Convolutional Neural Networks (CNNs) are successful deep learning models in the field of computer vision. In this paper, we use 4 types of spatial domain methods for transforming inertial sensor data to activity images. For creating a multimodal fusion framework, we made each type of activity images multimodal by convolving with two spatial domain filters.
  arXiv  Detail & Related papers  (2021-05-28T01:22:52Z)
• DeepMultiCap: Performance Capture of Multiple Characters Using Sparse Multiview Cameras [63.186486240525554]
  DeepMultiCap is a novel method for multi-person performance capture using sparse multi-view cameras. Our method can capture time varying surface details without the need of using pre-scanned template models.
  arXiv  Detail & Related papers  (2021-05-01T14:32:13Z)
• Adaptive Context-Aware Multi-Modal Network for Depth Completion [107.15344488719322]
  We propose to adopt the graph propagation to capture the observed spatial contexts. We then apply the attention mechanism on the propagation, which encourages the network to model the contextual information adaptively. Finally, we introduce the symmetric gated fusion strategy to exploit the extracted multi-modal features effectively. Our model, named Adaptive Context-Aware Multi-Modal Network (ACMNet), achieves the state-of-the-art performance on two benchmarks.
  arXiv  Detail & Related papers  (2020-08-25T06:00:06Z)
• Learning Enriched Features for Real Image Restoration and Enhancement [166.17296369600774]
  convolutional neural networks (CNNs) have achieved dramatic improvements over conventional approaches for image restoration task. We present a novel architecture with the collective goals of maintaining spatially-precise high-resolution representations through the entire network. Our approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details.
  arXiv  Detail & Related papers  (2020-03-15T11:04:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.