SpectralGPT: Spectral Remote Sensing Foundation Model

• URL: http://arxiv.org/abs/2311.07113v3
• Date: Mon, 12 Feb 2024 14:06:41 GMT
• Title: SpectralGPT: Spectral Remote Sensing Foundation Model
• Authors: Danfeng Hong, Bing Zhang, Xuyang Li, Yuxuan Li, Chenyu Li, Jing Yao, Naoto Yokoya, Hao Li, Pedram Ghamisi, Xiuping Jia, Antonio Plaza, Paolo Gamba, Jon Atli Benediktsson, Jocelyn Chanussot
• Abstract summary: 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.
• Score: 60.023956954916414
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The foundation model has recently garnered significant attention due to its potential to revolutionize the field of visual representation learning in a self-supervised manner. While most foundation models are tailored to effectively process RGB images for various visual tasks, there is a noticeable gap in research focused on spectral data, which offers valuable information for scene understanding, especially in remote sensing (RS) applications. To fill this gap, we created for the first time a universal RS foundation model, named SpectralGPT, which is purpose-built to handle spectral RS images using a novel 3D generative pretrained transformer (GPT). Compared to existing foundation models, SpectralGPT 1) accommodates input images with varying sizes, resolutions, time series, and regions in a progressive training fashion, enabling full utilization of extensive RS big data; 2) leverages 3D token generation for spatial-spectral coupling; 3) captures spectrally sequential patterns via multi-target reconstruction; 4) trains on one million spectral RS images, yielding models with over 600 million parameters. 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 across four downstream tasks: single/multi-label scene classification, semantic segmentation, and change detection.

Related papers

• SpectralEarth: Training Hyperspectral Foundation Models at Scale [47.93167977587301]
  We introduce SpectralEarth, a large-scale multi-temporal dataset designed to pretrain hyperspectral foundation models. We pretrain a series of foundation models on SpectralEarth using state-of-the-art self-supervised learning (SSL) algorithms. We construct four downstream datasets for land-cover and crop-type mapping, providing benchmarks for model evaluation.
  arXiv  Detail & Related papers  (2024-08-15T22:55:59Z)
• SpectralGaussians: Semantic, spectral 3D Gaussian splatting for multi-spectral scene representation, visualization and analysis [3.8834382997684087]
  We propose a novel cross-spectral rendering framework based on 3D Gaussian Splatting (3DGS) This framework generates realistic and semantically meaningful splats from registered multi-view spectrum and segmentation maps. Our contributions address challenges in multi-spectral scene representation, rendering, and editing, offering new possibilities for diverse applications.
  arXiv  Detail & Related papers  (2024-08-13T15:32:54Z)
• Hyperspectral Dataset and Deep Learning methods for Waste from Electric and Electronic Equipment Identification (WEEE) [0.0]
  We evaluate the performance of diverse deep learning architectures for hyperspectral image segmentation. Results show that incorporating spatial information alongside spectral data leads to improved segmentation results. We contribute to the field by cleaning and publicly releasing the Tecnalia WEEE Hyperspectral dataset.
  arXiv  Detail & Related papers  (2024-07-05T13:45:11Z)
• TOP-ReID: Multi-spectral Object Re-Identification with Token Permutation [64.65950381870742]
  We propose a cyclic token permutation framework for multi-spectral object ReID, dubbled TOP-ReID. We also propose a Token Permutation Module (TPM) for cyclic multi-spectral feature aggregation. Our proposed framework can generate more discriminative multi-spectral features for robust object ReID.
  arXiv  Detail & Related papers  (2023-12-15T08:54:15Z)
• 3DiffTection: 3D Object Detection with Geometry-Aware Diffusion Features [70.50665869806188]
  3DiffTection is a state-of-the-art method for 3D object detection from single images. We fine-tune a diffusion model to perform novel view synthesis conditioned on a single image. We further train the model on target data with detection supervision.
  arXiv  Detail & Related papers  (2023-11-07T23:46:41Z)
• MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction [148.26195175240923]
  We propose a novel Transformer-based method, Multi-stage Spectral-wise Transformer (MST++) for efficient spectral reconstruction. In the NTIRE 2022 Spectral Reconstruction Challenge, our approach won the First place.
  arXiv  Detail & Related papers  (2022-04-17T02:39:32Z)
• spectrai: A deep learning framework for spectral data [0.0]
  We present spectrai, an open-source framework designed to facilitate the training of neural networks on spectral data. Specti provides numerous built-in spectral data pre-processing and augmentation methods, neural networks for spectral data including spectral (image) denoising, spectral (image) classification, spectral image segmentation, and spectral image super-resolution.
  arXiv  Detail & Related papers  (2021-08-17T12:54:34Z)
• There and Back Again: Self-supervised Multispectral Correspondence Estimation [13.56924750612194]
  We introduce a novel cycle-consistency metric that allows us to self-supervise. This, combined with our spectra-agnostic loss functions, allows us to train the same network across multiple spectra. We demonstrate our approach on the challenging task of dense RGB-FIR correspondence estimation.
  arXiv  Detail & Related papers  (2021-03-19T12:33:56Z)
• Spatial-Spectral Residual Network for Hyperspectral Image Super-Resolution [82.1739023587565]
  We propose a novel spectral-spatial residual network for hyperspectral image super-resolution (SSRNet) Our method can effectively explore spatial-spectral information by using 3D convolution instead of 2D convolution, which enables the network to better extract potential information. In each unit, we employ spatial and temporal separable 3D convolution to extract spatial and spectral information, which not only reduces unaffordable memory usage and high computational cost, but also makes the network easier to train.
  arXiv  Detail & Related papers  (2020-01-14T03:34:55Z)

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.