Conference Agenda

Hyperspectral images (HSIs) are often severely degraded by mixed noise, such as Gaussian, stripe, and impulse noise during acquisition and transmission, which seriously impedes their subsequent applications. Therefore, HSI denoising is both crucial and challenging. In this work, we present a gradient-domain outlier-guided texture-preserved total variation (OG-TPTV) regularizer designed to remove mixed noise in HSIs. First, we utilize the mode-3 low-rank property of HSI gradient maps along the spectral dimension and apply a low-rank decomposition model to extract their spatial representation coefficients (SRCs). To improve the sparsity characterization of SRCs in the gradient subspace, an outlier-guided strategy is introduced. Specifically, we perform outlier detection on gradient maps to distinguish noise from texture structures and remove outliers to generate precise texture weighting maps. The resulting texture weight maps offer adaptive guidance for adjusting the strength of the sparsity constraints. Finally, a denoising method for HSIs is developed based on OG-TPTV. Extensive experiments on both synthetic and real HSIs demonstrate the superior denoising performance of our method.

Hyperspectral satellite missions generate massive data volumes that are difficult to transmit and store under tight onboard resource constraints, making effective lossy compression a key enabling technology. We propose SpectralNet-X, a transformer-based autoencoder for spectral-only compression of spaceborne hyperspectral imagery at a fixed compression ratio of 16. The encoder maps each spectrum to a low-dimensional latent code using a 1D convolutional projection followed by stacked self-attention layers with rotary position embeddings, and aggregates information via cross-attention pooling. The decoder reconstructs full-band spectra through an upsampling stack and per-band affine calibration. To improve reconstruction fidelity and generalization, SpectralNet-X is first pretrained in a masked-signal reconstruction task inspired by SimMIM and then fine-tuned with a mixed objective that combines mean-squared error and spectral angle mapper (SAM) terms using a scheduled weighting scheme. We evaluate SpectralNet-X on the large-scale HySpecNet–11k benchmark and in a mission-realistic cross-sensor setting, where models trained on HySpecNet–11k are tested on PRISMA hyperspectral scenes. Across PSNR, SSIM, and SAM, and when compared to three different compression autoencoders, SpectralNet-X achieves the lowest angular reconstruction errors while maintaining competitive distortion metrics and substantially reducing the fraction of spectra with large SAM outliers. These results indicate that transformer-based spectral compression is a promising candidate for robust, mission-realistic onboard hyperspectral data reduction.

Validating the performance of deep learning models in satellite imagery is essential for ensuring model generalizability, decision reliability, and spatial transferability—particularly in the context of hyperspectral images, which contain high-dimensional, spatially complex data. While it is well recognized that multiple spatial characteristics influence deep learning model performance, few studies have systematically examined how the interactions among these characteristics affect model validation sensitivity in hyperspectral contexts. This study aims to investigate how the interaction between spatial scale (e.g., surrounding 3, 5, 7 grids) and training sample size (e.g., 10%, 30%, 50% of all data) influences the validation accuracy and sensitivity of deep learning models. An innovative validation sensitivity index is developed to quantify the change in accuracy per unit of spatial scale and sample size, enabling a more refined assessment of model robustness. The index is applied to three representative hyperspectral datasets, covering diverse environmental and spectral conditions. Results show that spatial scale accounts for 0~21.0% accuracy variation, training sample size contributes 5.6~36.5% variation, but their interaction leads to 5.4~70.3% variation, indicating a nonlinear amplification enhanced effect. These findings may be explained by the compounded influence of data contextuality, spatial redundancy, and model overfitting dynamics. This study demonstrates the critical need to consider spatial interactions in validation design, offering new insights for enhancing the reliability of geospatial artificial intelligence (GeoAI) applications in remote sensing and spatial data science.

Surface reflectance is a foundational variable in optical remote sensing, as inaccuracies introduced during atmospheric correction can propagate and amplify across subsequent satellite-derived products. Nonetheless, the extent to which the choice of Radiative Transfer Model (RTM) affects reflectance retrieval has not been sufficiently examined. This study investigates how two widely used RTMs—6SV and VLIDORT—produce different surface reflectance outcomes when applied under consistent atmospheric and geometric conditions for the GEO-KOMPSAT-2B/GEMS instrument. To ensure comparability, both models were driven by identical GEMS aerosol properties and an equivalent LUT configuration.

The comparison shows that while the two RTMs reproduce broadly similar spatial patterns, systematic quantitative differences remain in the retrieved reflectance. These differences vary depending on atmospheric and viewing conditions, particularly under higher aerosol loading. A sensitivity analysis further indicates that aerosol amount and scattering characteristics, alongside viewing geometry, are key factors influencing the magnitude of RTM divergence.

Overall, this study provides a structured assessment of RTM-dependent variability in atmospheric correction and highlights the importance of model choice when interpreting or harmonizing surface reflectance products. The findings offer a basis for improving consistency in future GEMS-based retrievals and for advancing reliable surface reflectance generation in geostationary remote sensing.

Remote sensing acquisition systems rely on a range of platforms, from drones to satellite missions, to record multimodal Earth surface data. This fact encourages the preparation of datasets with complementary properties, thereby increasing their discriminative potential. A common complementary combination is between Hyperspectral and LiDAR-generated digital surface model data. While engaging, this fusion poses challenges for specific applications. Multiple works fuse these modalities at the feature level using vector concatenation, maximization, or averaging. Although functional, these methods omit target interactions between the modalities. Another challenge in remote sensing is the quantity and quality of labels required by deep learning methods, which are expensive, error-prone, and difficult to scale. We address the challenges above by proposing a self-supervised processing framework based on cross-modal attention that effectively fuses features at multiple levels, thereby exploiting complementary information across data streams. Specifically, our method is founded on a pseudo-Siamese network that reweights each modality’s features with information from the other via a mirrored cross-modal attention. The network’s objective is to maximize the similarity between the feature representations of both streams. A fusion network builds a latent representation using the learned encoders and attention modules. Then, a k-Nearest Neighbor classifier categorizes each sample within the representation using ten labels per class. Our experiments show that our spatial- and channel-spatial cross-modal attention approaches outperform well-established fusion methods for label-efficient land cover classification across datasets. Our findings lay the groundwork for fusion methods that effectively exploit inter-stream data relationships to encourage complementarity.

Despite the rapid development of satellite sensors, acquiring high-resolution RGB images remains a challenge. In this paper, a GAN-based multiscale feature-based pan-to-rgb model is proposed to establish a novel framework for high-resolution, high-fidelity RGB images generation from remote sensing panchromatic images. The spatial structure, texture, and color of the results are consistent with the real images, and the colors are naturally realistic and vibrant. Multiscale features and symmetric luminance color decoders are utilized to overcome color desaturation, inaccuracy, and distortion in conventional algorithms. By combining CNNs for local feature modeling and transformers for global feature modeling, this approach learns pan-to-rgb mappings to produce high-resolution, high-fidelity RGB images in CIELAB space. Besides, the luminance distance loss and the color distance loss are utilized to prevent the coupling of luminance and color. We also conducted experimental validation on Gaofen-7 satellite data, and the results demonstrated that the FID, CF, and △CF indicators of the proposed algorithm improved by 2.90%, 11.77%, and 64.51%, respectively, compared to the comparison algorithms.