Apr 4, 2023 · With the goal of improving the adaptability of SR models to the inputs of different spectral sensors and enhancing the utilization of remote sensing data, we propose a novel approach, the masked autoencoder-based spectral image reconstruction model (SpectralMAE).

Nov 25, 2024 · In this article, we explore spectral and spatial remote sensing image features as improved MAE-reconstruction targets. We first conduct a study on reconstructing various image features, all performing comparably well or better than raw pixels.

Masked Angle-Aware Autoencoder for Remote Sensing Images (ECCV 2024) - benesakitam/MA3E

We introduce a novel pretraining framework called Saliency Supervised Masked Autoencoder (SSMAE), which improves the traditional masked autoencoder by incorporating saliency supervision. This innovation enables more targeted feature …

Therefore, we propose the Masked Angle-Aware Autoencoder (MA3E), which perceives and learns angle information by restoring the preset angle variations during original pixel recon-struction.

Oct 28, 2022 · To address the problem, this article presents a novel masked auto-encoding spectral–spatial transformer (MAEST), which gathers two different collaborative branches: 1) a reconstruction path, which dynamically uncovers the most robust encoding features based on a masking auto-encoding strategy, and 2) a classification path, which embeds these ...

Aug 4, 2024 · This paper proposes the Masked Angle-Aware Autoencoder (MA3E) to perceive and learn angles during pre-training. We design a \textit{scaling center crop} operation to create the rotated crop with random orientation on each original image, introducing the …

spectral and spatial remote sensing image features as improved MAE-reconstruction targets. We first conduct a study on recon-structing various image features, all performing comparably well or better than raw pixels. Based on such observations, we propose Feature Guided Masked Autoencoder (FG-MAE): reconstructing

3 days ago · Due to the excellent feature extraction capabilities, deep learning has become the mainstream method for hyperspectral image (HSI) classification. Transformer, with its powerful long-range relationship modeling ability, has become a popular model; however, it usually requires a large number of labeled data for parameter training, which may be costly and impractical for HSI classification. As ...

Apr 4, 2023 · With the goal of improving the adaptability of SR models to the inputs of different spectral sensors and enhancing the utilization of remote sensing data, we propose a novel approach, the masked autoencoder-based spectral image reconstruction model (SpectralMAE).