Conference Agenda

Bathymetry information around islands, reefs, and shallow-water regions is critical for both navigation safety and environmental management. However, these areas often feature diverse substrate types and strong spatial heterogeneity, which makes it challenging to accurately retrieve fine-scale bathymetry from traditional medium-resolution satellite imagery. High-spatial-resolution (HSR) sensors, such as PlanetScope SuperDove (~ 3.7 m spatial resolution), offer the potential to capture more detailed spatial features, yet their relatively low signal-to-noise ratio (SNR) can lead to noisy retrievals, particularly over low-reflectance waters. To mitigate this issue, incorporating the spatial context of neighboring pixels while jointly utilizing the spectral information offered by low- and high-resolution sensors can enhance the stability and accuracy of HSR-based bathymetry retrievals. In this study, a UNet++ neural network with the spatial and channel squeeze & excitation (scSE) attention mechanisms (BathyUNet++) was employed to retrieve bathymetry from SuperDove imagery. To satisfy the patch-based input requirement of UNet++, the model was fully trained using two sources of data: clear-sky SuperDove image patches paired with Landsat-8-derived bathymetry and a limited set of ALB data. Validation results demonstrated that the model accurately retrieved bathymetry in regions independent of the training set.The proposed model and framework can be readily adapted to other HSR sensors, offering a promising approach for global HSR shallow-water bathymetry retrieval using multi-source satellite observations.

Despite its potential in forestry, agriculture, environmental monitoring, safety surveillance, and defence, real-time hyperspectral imaging (HSI) remains challenging in practice because of the high dimensionality of the data and limited onboard computational resources.

This work introduces a distributed HSI classification framework that integrates federated learning, a Self-learning Minimal Learning Machine classifier (SL-MLM), adaptive Kalman filter-based model fusion, and lightweight MQTT-based communication on Raspberry Pi edge devices and a laptop serving as the base station. Acting as local nodes, Raspberry Pis process HSI data row by row, update their models recursively, and only exchange compact model parameters and classification results with the base station. HSI data in its raw form remains local.

The findings suggest that the proposed local learning workflow can be implemented on Raspberry Pi devices, and Kalman-based fusion improves stability and consistency in comparison to individual local models. The method is feasible in scenarios where the number of labelled data points is restricted, as the SL-MLM classifier can be initialized with a mere handful of class-specific reference points. The research demonstrates a feasible, low-cost approach to distributed

embedded HSI classification and sensing.

Monitoring inland Water Quality Parameters (WQPs) is essential for managing freshwater ecosystems and assessing anthropogenic impacts (Mishra et al., 2017). Satellite remote sensing provides a cost-effective and large-scale approach for monitoring inland WQPs. However, most existing satellite sensors have limited spectral resolution, restricting their ability to capture subtle optical variations expressed by inland WQPs, and/or insufficient spatial resolution to yield valid water-only pixels in narrow rivers or nearshore zones (Ansari et al., 2025).

Recent advances in hyperspectral satellite technology have created new opportunities for inland WQP monitoring. The Wyvern Dragonette-001, launched in April 2023, provides hyperspectral imagery with a spatial resolution of 5.3 m and 23 spectral bands within the visible to near-infrared range (500–800 nm) (Ansari et al., 2025; Wyvern Dragonette, 2023). Given its novelty, the potential of such imagery for assessing WQPs in inland water remains largely unexplored. A recent review (Ansari et al., 2025) evaluating the sensor’s spectral resolution and signal-to-noise ratio for retrieving inland WQPs indicated that Dragonette-001 is suitable for estimating non-algal particles (NAP) and shows potential for chlorophyll-a mapping, although it is likely unsuitable for retrieving Colored Dissolved Organic Matter (CDOM).

This study reports on a practical test that assessed the feasibility of using Wyvern Dragonette-001 imagery to retrieve turbidity, Suspended Sediments (SS), and Dissolved Organic Carbon (DOC) in a portion of the St. Lawrence River, Québec, Canada.

Hyperspectral imaging produces vast data volumes that often exceed storage and transmission capacities on airborne and satellite platforms. This study systematically investigates the effects of lossy hyperspectral data compression on the scientific usability of the resulting data products. Using UAV-based HySpex acquisitions from the HyperThun’22 campaign, several state-of-the-art learning-based compression models were evaluated, including spectral, spatial, and spatio-spectral architectures. The analysis quantifies how compression-induced reconstruction errors propagate through the full pre-processing workflow, from raw digital numbers through radiometric calibration, geometric correction, and atmospheric correction to the final surface reflectance domain.

Results show that spectral models such as the Adaptive 1D Convolutional Autoencoder (A1D-CAE) achieve the highest fidelity, maintaining sub-degree spectral deviations and near-perfect structural similarity. In contrast, purely spatial or 3D convolutional models exhibit severe distortions that persist across all pre-processing levels. The findings demonstrate that lossy compression can be applied at the raw stage without compromising the integrity of reflectance products, provided that spectral correlations are explicitly modeled.

This work highlights the importance of selecting compression architectures consistent with sensor characteristics and pre-processing workflows and provides a quantitative foundation for future operational implementations of onboard hyperspectral compression in Earth observation missions.

Efficient nitrogen (N) management remains a major challenge for sustainable potato production, particularly on coarse-textured soils prone to nutrient leaching. This study investigates the use of Visible–Near Infrared to Short-Wave Infrared (VNIR–SWIR, 350–2500 nm) hyperspectral spectroscopy for non-destructive, in-season estimation of petiole nitrate nitrogen (PNN) under both field and laboratory conditions. Spectral data were collected using an ASD FieldSpec Pro spectroradiometer and processed through Savitzky–Golay smoothing, Standard Normal Variate normalization, and first-derivative transformation. Variable Importance in Projection (VIP) analysis was employed to identify N-sensitive wavelengths, and three predictive approaches—One-Dimensional Convolutional Neural Network (1D-CNN), Support Vector Regression (SVR), and Partial Least Squares Regression (PLSR)—were compared for their predictive accuracy. Calibration transfer using Piecewise Direct Standardization (PDS) was applied to harmonize field spectra with laboratory measurements. Results showed that the 1D-CNN achieved the highest predictive performance (R² = 0.90, RMSE = 0.22%), outperforming SVR and PLSR. PDS improved field-based predictions by reducing spectral discrepancies caused by illumination and canopy variability. The findings highlight the potential of hyperspectral spectroscopy combined with deep learning and calibration transfer techniques to provide accurate and scalable diagnostics of plant nitrogen status. This research supports the integration of proximal sensing and data-driven models for precision nutrient management in potato systems and broader agricultural applications.

This contribution presents a novel method for infrared image stripe remover that addresses the limitations of current approaches in difficulty of extracting structural information of stripes. The proposed framework integrates strip convlution layers with multi-size kernels in a dense connection to enhance stripe structural information expression in challenging conditions and provides more reliable results for practical applications. Experimental evaluations on multiple datasets demonstrate significant improvements compared to state-of-the-art methods. The method is designed to be computationally efficient and suitable for real-world deployment in fields.

This paper aims to evaluate the performance of ISODATA clustering for tree species classification using ultra-high-resolution multispectral data collected with Unmanned Aerial Vehicle. The study focuses on two sites in Żednia forest district near the city of Bialystok, northeastern Poland. The input data consist of 10-band multispectral orthomosaics with a resolution of 10 cm, acquired from an UAV platform equipped with a MicaSense RedEdge-MX dual camera and image-based Canopy Height Model. The classifications were conducted at two levels of forest detail: forest types, including two classes (broadleaf and conifer), and tree species, comprising four classes in Study Area 1 and ten species in Study Area 2. Multiple classifications were generated, testing different input parameters such as the number of clusters and various combinations of input data. For the first level of classification (forest type), overall accuracies range from 84,09% to 97,57% in Study Area 1 and from 82,31% to 92,74% in Study Area 2. At the second level of classification (tree species), overall accuracies vary from 70.73% to 91.77% in Study Area 1 and from 36,51% to 72,33% in Study Area 2. Overall, ISODATA demonstrates robust performance in classifying forest types in both study areas. However, performance in classifying tree species varies across different classes, with relatively high accuracies observed for certain species such as spruce, pine, oak, larch, and birch. The results underscore the potential of multispectral UAV data and unsupervised classification methods for accurately classifying tree species.