STARS: Sensor-agnostic Transformer Architecture for Remote Sensing

• URL: http://arxiv.org/abs/2411.05714v1
• Date: Fri, 08 Nov 2024 17:16:02 GMT
• Title: STARS: Sensor-agnostic Transformer Architecture for Remote Sensing
• Authors: Ethan King, Jaime Rodriguez, Diego Llanes, Timothy Doster, Tegan Emerson, James Koch,
• Abstract summary: We present a sensor-agnostic spectral transformer as the basis for spectral foundation models. We introduce a Universal Spectral Representation (USR) that encodes spectra from any spectral instrument into a common representation. We develop a methodology for pre-training such models in a self-supervised manner.
• Score: 2.6938549839852524
• License:
• Abstract: We present a sensor-agnostic spectral transformer as the basis for spectral foundation models. To that end, we introduce a Universal Spectral Representation (USR) that leverages sensor meta-data, such as sensing kernel specifications and sensing wavelengths, to encode spectra obtained from any spectral instrument into a common representation, such that a single model can ingest data from any sensor. Furthermore, we develop a methodology for pre-training such models in a self-supervised manner using a novel random sensor-augmentation and reconstruction pipeline to learn spectral features independent of the sensing paradigm. We demonstrate that our architecture can learn sensor independent spectral features that generalize effectively to sensors not seen during training. This work sets the stage for training foundation models that can both leverage and be effective for the growing diversity of spectral data.

Related papers

• MSSIDD: A Benchmark for Multi-Sensor Denoising [55.41612200877861]
  We introduce a new benchmark, the Multi-Sensor SIDD dataset, which is the first raw-domain dataset designed to evaluate the sensor transferability of denoising models. We propose a sensor consistency training framework that enables denoising models to learn the sensor-invariant features.
  arXiv  Detail & Related papers  (2024-11-18T13:32:59Z)
• Point-Calibrated Spectral Neural Operators [54.13671100638092]
  We introduce Point-Calibrated Spectral Transform, which learns operator mappings by approximating functions with the point-level adaptive spectral basis. Point-Calibrated Spectral Neural Operators learn operator mappings by approximating functions with the point-level adaptive spectral basis.
  arXiv  Detail & Related papers  (2024-10-15T08:19:39Z)
• SenPa-MAE: Sensor Parameter Aware Masked Autoencoder for Multi-Satellite Self-Supervised Pretraining [1.4528189330418977]
  SenPa-MAE encodes the sensor parameters of an observed multispectral signal into the image embeddings. SenPa-MAE can be pre-trained on imagery of different satellites with non-matching spectral or geometrical sensor characteristics.
  arXiv  Detail & Related papers  (2024-08-20T16:53:30Z)
• Bridging Remote Sensors with Multisensor Geospatial Foundation Models [15.289711240431107]
  msGFM is a multisensor geospatial foundation model that unifies data from four key sensor modalities. For data originating from identical geolocations, our model employs an innovative cross-sensor pretraining approach. msGFM has demonstrated enhanced proficiency in a range of both single-sensor and multisensor downstream tasks.
  arXiv  Detail & Related papers  (2024-04-01T17:30:56Z)
• Neural Plasticity-Inspired Multimodal Foundation Model for Earth Observation [48.66623377464203]
  Our novel approach introduces the Dynamic One-For-All (DOFA) model, leveraging the concept of neural plasticity in brain science. This dynamic hypernetwork, adjusting to different wavelengths, enables a single versatile Transformer jointly trained on data from five sensors to excel across 12 distinct Earth observation tasks.
  arXiv  Detail & Related papers  (2024-03-22T17:11:47Z)
• SpectralGPT: Spectral Remote Sensing Foundation Model [60.023956954916414]
  A universal RS foundation model, named SpectralGPT, is purpose-built to handle spectral RS images using a novel 3D generative pretrained transformer (GPT) Compared to existing foundation models, SpectralGPT accommodates input images with varying sizes, resolutions, time series, and regions in a progressive training fashion, enabling full utilization of extensive RS big data. Our evaluation highlights significant performance improvements with pretrained SpectralGPT models, signifying substantial potential in advancing spectral RS big data applications within the field of geoscience.
  arXiv  Detail & Related papers  (2023-11-13T07:09:30Z)
• Data-Induced Interactions of Sparse Sensors [3.050919759387984]
  We take a thermodynamic view to compute the full landscape of sensor interactions induced by the training data. Mapping out these data-induced sensor interactions allows combining them with external selection criteria and anticipating sensor replacement impacts.
  arXiv  Detail & Related papers  (2023-07-21T18:13:37Z)
• The secret role of undesired physical effects in accurate shape sensing with eccentric FBGs [1.0805335573008565]
  Eccentric fiber Bragg gratings (FBG) are cheap and easy-to-fabricate shape sensors that are often interrogated with simple setups. Here, we present a novel technique to overcome these limitations and provide accurate and precise shape estimation.
  arXiv  Detail & Related papers  (2022-10-28T09:07:08Z)
• Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra [68.8204255655161]
  We focus on unsupervised techniques for analyzing spectral data from transiting exoplanets. We show that there is a high degree of correlation in the spectral data, which calls for appropriate low-dimensional representations. We uncover interesting structures in the principal component basis, namely, well-defined branches corresponding to different chemical regimes.
  arXiv  Detail & Related papers  (2022-01-07T22:26:33Z)
• Deep Soft Procrustes for Markerless Volumetric Sensor Alignment [81.13055566952221]
  In this work, we improve markerless data-driven correspondence estimation to achieve more robust multi-sensor spatial alignment. We incorporate geometric constraints in an end-to-end manner into a typical segmentation based model and bridge the intermediate dense classification task with the targeted pose estimation one. Our model is experimentally shown to achieve similar results with marker-based methods and outperform the markerless ones, while also being robust to the pose variations of the calibration structure.
  arXiv  Detail & Related papers  (2020-03-23T10:51:32Z)

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.