In that, we show the optimality of our network in terms of loss functionals and highlight the generalisibility further. Moreover, we consider visual and quantitative results for images of ellipses. TVspecNET achieves high quantitative performance measures: SSIM is 0.9658, PSNR is 30.609 We show the visual results for an example ellipse image in Figure 3. Documentation can be found at https://odlgroup.github.io/odl/ All bands have the same scaling.

Hyperspectral imaging (HSI) classification is a critical tool with widespread applications across diverse fields such as agriculture, environmental monitoring, medicine, and materials science. Due to the limited availability of high-quality training samples and the high dimensionality of spectral data, HSI classification models are prone to overfitting and often face challenges in balancing accuracy and computational complexity. Furthermore, most of HSI classification models are monomodal, where it solely relies on spectral-spatial data to learn decision boundaries in the high dimensional embedding space. To address this, we propose a general-purpose Semantic Spectral-Spatial Fusion Network (S3FN) that uses contextual, class specific textual descriptions to complement the training of an HSI classification model. Specifically, S3FN leverages LLMs to generate comprehensive textual descriptions for each class label that captures their unique characteristics and spectral behaviors. These descriptions are then embedded into a vector space using a pre-trained text encoder such as BERT or RoBERTa to extract meaningful label semantics which in turn leads to a better feature-label alignment for improved classification performance. To demonstrate the effectiveness of our approach, we evaluate our model on three diverse HSI benchmark datasets - Hyperspectral Wood, HyperspectralBlueberries, and DeepHS-Fruit and report significant performance boost. Our results highlight the synergy between textual semantics and spectral-spatial data, paving the way for further advancements in semantically augmented HSI classification models. Codes are be available in: https://github.com/milab-nsu/S3FN

Earth Observation Foundation Models (EOFMs) have exploded in prevalence as tools for processing the massive volumes of remotely sensed and other earth observation data, and for delivering impact on the many essential earth monitoring tasks. An emerging trend posits using the outputs of pre-trained models as 'embeddings' which summarize high dimensional data to be used for generic tasks such as similarity search and content-specific queries. However, most EOFM models are trained only on single modalities of data and then applied or benchmarked by matching bands across different modalities. It is not clear from existing work what impact diverse sensor architectures have on the internal representations of the present suite of EOFMs. We show in this work that the representation space of EOFMs is highly sensitive to sensor architecture and that understanding this difference gives a vital perspective on the pitfalls of current EOFM design and signals for how to move forward as model developers, users, and a community guided by robust remote-sensing science.

Abstract--Cloud segmentation is a critical preprocessing step for many Earth observation tasks, yet most models are tightly coupled to specific sensor configurations and rely on ground-based processing. In this work, we propose Fast-SEnSeI, a lightweight, sensor-independent encoder module that enables flexible, on-board cloud segmentation across multispectral sensors with varying band configurations. Building upon SEnSeI-v2, Fast-SEnSeI integrates an improved spectral descriptor, lightweight architecture, and robust padding-band handling. It accepts arbitrary combinations of spectral bands and their wavelengths, producing fixed-size feature maps that feed into a compact, quantized segmentation model based on a modified U-Net. The module runs efficiently on embedded CPUs using Apache TVM, while the segmentation model is deployed on FPGA, forming a CPU-FPGA hybrid pipeline suitable for space-qualified hardware. Evaluations on Sentinel-2 and Landsat 8 datasets demonstrate accurate segmentation across diverse input configurations. As the volume of satellite imagery captured in orbit continues to grow, the traditional paradigm of ground-based data processing is reaching its limits. Downlink bottlenecks, limited bandwidth, and the need for timely data products have driven the development of on-board artificial intelligence (AI) capabilities [1], [2], [3], [4]. By moving parts of the processing pipeline directly onto the satellite, it becomes possible to filter, analyze, and prioritize data before transmission, enhancing mission efficiency and enabling new forms of real-time decision-making. A particularly promising application of on-board AI is cloud segmentation.

Hyperspectral imaging (HSI) provides rich spectral information for precise material classification and analysis; however, its high dimensionality introduces a computational burden and redundancy, making dimensionality reduction essential. We present an exploratory study into the application of post-hoc explainability methods in a model--driven framework for band selection, which reduces the spectral dimension while preserving predictive performance. A trained classifier is probed with explanations to quantify each band's contribution to its decisions. We then perform deletion--insertion evaluations, recording confidence changes as ranked bands are removed or reintroduced, and aggregate these signals into influence scores. Selecting the highest--influence bands yields compact spectral subsets that maintain accuracy and improve efficiency. Experiments on two public benchmarks (Pavia University and Salinas) demonstrate that classifiers trained on as few as 30 selected bands match or exceed full--spectrum baselines while reducing computational requirements. The resulting subsets align with physically meaningful, highly discriminative wavelength regions, indicating that model--aligned, explanation-guided band selection is a principled route to effective dimensionality reduction for HSI.

Hyperspectral imaging offers detailed spectral information for mineral mapping; however, weak mineral signatures are often masked by noisy and redundant bands, limiting detection performance. To address this, we propose a two-stage integrated framework for enhanced mineral detection in the Cuprite mining district. In the first stage, we compute the signal-to-noise ratio (SNR) for each spectral band and apply a phase-locked thresholding technique to discard low-SNR bands, effectively removing redundancy and suppressing background noise. Savitzky-Golay filtering is then employed for spectral smoothing, serving a dual role first to stabilize trends during band selection, and second to preserve fine-grained spectral features during preprocessing. In the second stage, the refined HSI data is reintroduced into the model, where KMeans clustering is used to extract 12 endmember spectra (W1 custom), followed by non negative least squares (NNLS) for abundance unmixing. The resulting endmembers are quantitatively compared with laboratory spectra (W1 raw) using cosine similarity and RMSE metrics. Experimental results confirm that our proposed pipeline improves unmixing accuracy and enhances the detection of weak mineral zones. This two-pass strategy demonstrates a practical and reproducible solution for spectral dimensionality reduction and unmixing in geological HSI applications.

In this paper, we propose a system for detecting adulteration in coconut milk, utilizing infrared spectroscopy. The machine learning-based proposed system comprises three phases: preprocessing, feature extraction, and classification. The first phase involves removing irrelevant data from coconut milk spectral signals. In the second phase, we employ the Linear Discriminant Analysis (LDA) algorithm for extracting the most discriminating features. In the third phase, we use the K-Nearest Neighbor (KNN) model to classify coconut milk samples into authentic or adulterated. We evaluate the performance of the proposed system using a public dataset comprising Fourier Transform Infrared (FTIR) spectral information of pure and contaminated coconut milk samples. Findings show that the proposed method successfully detects adulteration with a cross-validation accuracy of 93.33%.