Self-Supervision in Time for Satellite Images(S3-TSS): A novel method of SSL technique in Satellite images
With the limited availability of labeled data with various atmospheric conditions in remote sensing images, it seems useful to work with self-supervised algorithms. Few pretext-based algorithms, including from rotation, spatial context and jigsaw puzzles are not appropriate for satellite images. Often, satellite images have a higher temporal frequency. So, the temporal dimension of remote sensing data provides natural augmentation without requiring us to create artificial augmentation of images. Here, we propose S3-TSS, a novel method of self-supervised learning technique that leverages natural augmentation occurring in temporal dimension. We compare our results with current state-of-the-art methods and also perform various experiments. We observed that our method was able to perform better than baseline SeCo in four downstream datasets. Code for our work can be found here: https://github.com/hewanshrestha/Why-Self-Supervision-in-Time
Code (1)
Tasks
Self-Supervised LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Weakly-supervised Camera Localization by Ground-to-satellite Image Registration
The ground-to-satellite image matching/retrieval was initially proposed for city-scale ground camera localization. This work addresses the problem of improving camera pose accuracy by ground-to-satellite image matching a…
Camera LocalizationContrastive LearningImage RegistrationRetrieval+1DeepTriNet: A Tri-Level Attention Based DeepLabv3+ Architecture for Semantic Segmentation of Satellite Images
The segmentation of satellite images is crucial in remote sensing applications. Existing methods face challenges in recognizing small-scale objects in satellite images for semantic segmentation primarily due to ignoring …
Change DetectionImage SegmentationManagementSegmentation+1Test-Time Certifiable Self-Supervision to Bridge the Sim2Real Gap in Event-Based Satellite Pose Estimation
Deep learning plays a critical role in vision-based satellite pose estimation. However, the scarcity of real data from the space environment means that deep models need to be trained using synthetic data, which raises th…
Pose EstimationTest-time AdaptationAn Efficient Method for the Classification of Croplands in Scarce-Label Regions
Two of the main challenges for cropland classification by satellite time-series images are insufficient ground-truth data and inaccessibility of high-quality hyperspectral images for under-developed areas. Unlabeled medi…
ClassificationDomain AdaptationGeneral ClassificationOpen-Ended Question Answering+2SkySplat: Generalizable 3D Gaussian Splatting from Multi-Temporal Sparse Satellite Images
Three-dimensional scene reconstruction from sparse-view satellite images is a long-standing and challenging task. While 3D Gaussian Splatting (3DGS) and its variants have recently attracted attention for its high efficie…