Rank-based Geographical Regularization: Revisiting Contrastive Self-Supervised Learning for Multispectral Remote Sensing Imagery

• URL: http://arxiv.org/abs/2601.02289v1
• Date: Mon, 05 Jan 2026 17:24:50 GMT
• Title: Rank-based Geographical Regularization: Revisiting Contrastive Self-Supervised Learning for Multispectral Remote Sensing Imagery
• Authors: Tom Burgert, Leonard Hackel, Paolo Rota, Begüm Demir,
• Abstract summary: Self-supervised learning (SSL) has become a powerful paradigm for learning from large, unlabeled datasets.<n>Applying SSL to multispectral remote sensing (RS) images presents unique challenges and opportunities due to the geographical and temporal variability of the data.<n>We introduce GeoRank, a novel regularization method for contrastive SSL that improves upon prior techniques.
• Score: 10.20881592893918
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-supervised learning (SSL) has become a powerful paradigm for learning from large, unlabeled datasets, particularly in computer vision (CV). However, applying SSL to multispectral remote sensing (RS) images presents unique challenges and opportunities due to the geographical and temporal variability of the data. In this paper, we introduce GeoRank, a novel regularization method for contrastive SSL that improves upon prior techniques by directly optimizing spherical distances to embed geographical relationships into the learned feature space. GeoRank outperforms or matches prior methods that integrate geographical metadata and consistently improves diverse contrastive SSL algorithms (e.g., BYOL, DINO). Beyond this, we present a systematic investigation of key adaptations of contrastive SSL for multispectral RS images, including the effectiveness of data augmentations, the impact of dataset cardinality and image size on performance, and the task dependency of temporal views. Code is available at https://github.com/tomburgert/georank.

Related papers

• AddressCLIP: Empowering Vision-Language Models for City-wide Image Address Localization [57.34659640776723]
  We propose an end-to-end framework named AddressCLIP to solve the problem with more semantics. We have built three datasets from Pittsburgh and San Francisco on different scales specifically for the IAL problem.
  arXiv  Detail & Related papers  (2024-07-11T03:18:53Z)
• VG-SSL: Benchmarking Self-supervised Representation Learning Approaches for Visual Geo-localization [7.689824252319191]
  This study presents a novel VG-SSL framework, designed for versatile integration and benchmarking of diverse SSL methods for representation learning in VG. We adapt SSL techniques to improve VG on datasets from hand-held and car-mounted cameras used in robotics and autonomous vehicles. Results show that contrastive learning and information methods yield superior geo-specific representation quality, matching or surpassing the performance of state-of-the-art VG techniques.
  arXiv  Detail & Related papers  (2023-07-31T19:03:13Z)
• CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations [90.50864830038202]
  We present Contrastive Spatial Pre-Training (CSP), a self-supervised learning framework for geo-tagged images. We use a dual-encoder to separately encode the images and their corresponding geo-locations, and use contrastive objectives to learn effective location representations from images. CSP significantly boosts the model performance with 10-34% relative improvement with various labeled training data sampling ratios.
  arXiv  Detail & Related papers  (2023-05-01T23:11:18Z)
• De-coupling and De-positioning Dense Self-supervised Learning [65.56679416475943]
  Dense Self-Supervised Learning (SSL) methods address the limitations of using image-level feature representations when handling images with multiple objects. We show that they suffer from coupling and positional bias, which arise from the receptive field increasing with layer depth and zero-padding. We demonstrate the benefits of our method on COCO and on a new challenging benchmark, OpenImage-MINI, for object classification, semantic segmentation, and object detection.
  arXiv  Detail & Related papers  (2023-03-29T18:07:25Z)
• Self-Supervised Learning with Kernel Dependence Maximization [23.618292038419654]
  We propose Self-Supervised Learning with the Hilbert-Schmidt Independence Criterion (SSL-HSIC) SSL-HSIC maximizes dependence between representations of transformed versions of an image and the image identity. This self-supervised learning framework yields a new understanding of InfoNCE, a variational lower bound on the mutual information (MI) between different transformations.
  arXiv  Detail & Related papers  (2021-06-15T17:51:16Z)
• PGL: Prior-Guided Local Self-supervised Learning for 3D Medical Image Segmentation [87.50205728818601]
  We propose a PriorGuided Local (PGL) self-supervised model that learns the region-wise local consistency in the latent feature space. Our PGL model learns the distinctive representations of local regions, and hence is able to retain structural information.
  arXiv  Detail & Related papers  (2020-11-25T11:03:11Z)
• Geography-Aware Self-Supervised Learning [79.4009241781968]
  We show that due to their different characteristics, a non-trivial gap persists between contrastive and supervised learning on standard benchmarks. We propose novel training methods that exploit the spatially aligned structure of remote sensing data. Our experiments show that our proposed method closes the gap between contrastive and supervised learning on image classification, object detection and semantic segmentation for remote sensing.
  arXiv  Detail & Related papers  (2020-11-19T17:29:13Z)
• Remote Sensing Image Scene Classification with Self-Supervised Paradigm under Limited Labeled Samples [11.025191332244919]
  We introduce new self-supervised learning (SSL) mechanism to obtain the high-performance pre-training model for RSIs scene classification from large unlabeled data. Experiments on three commonly used RSIs scene classification datasets demonstrated that this new learning paradigm outperforms the traditional dominant ImageNet pre-trained model. The insights distilled from our studies can help to foster the development of SSL in the remote sensing community.
  arXiv  Detail & Related papers  (2020-10-02T09:27:19Z)
• Multi-Level Graph Convolutional Network with Automatic Graph Learning for Hyperspectral Image Classification [63.56018768401328]
  We propose a Multi-level Graph Convolutional Network (GCN) with Automatic Graph Learning method (MGCN-AGL) for HSI classification. By employing attention mechanism to characterize the importance among spatially neighboring regions, the most relevant information can be adaptively incorporated to make decisions. Our MGCN-AGL encodes the long range dependencies among image regions based on the expressive representations that have been produced at local level.
  arXiv  Detail & Related papers  (2020-09-19T09:26:20Z)

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.