The paper critiques the common practice of random pixel splits in hyperspectral image classification, noting that such splits allow test pixels to be adjacent to training pixels, inflating accuracy. It proposes a leakage‑free evaluation protocol that enforces spatial separation based on the model’s receptive field and applies it to ten diverse architectures, finding a significant drop in Macro‑F1 (average 0.147) and substantial changes in model rankings. The study also shows that all ten models misclassify the same pixels, indicating a spectral ambiguity in the data that current methods cannot resolve.
By Ehsan Faghih, Fatemeh Ashrafi, Marguerite Moore, Zahra Saki
Hyperspectral remote sensing has advanced across diverse deep learning paradigms, including spectral spatial CNNs, Vision Transformers, Mamba, graph neural networks, Kolmogorov Arnold networks, and se...
The technical report introduces Hyperspectral Image Models, a modular framework that unifies 55 deep‑learning models across six paradigms for hyperspectral remote sensing. It standardizes tensor conventions, evaluation protocols, and dataset handling, integrating 24 benchmark scenes from various sensors and providing tools to avoid train‑test overlap. Experiments across 1,320 model‑scene combinations show that scene difficulty outweighs architecture, with no single paradigm dominating and small models achieving performance comparable to much larger ones.
By Tanishq Rachamalla, Aryan Das, Srishti Kaushik, Swalpa Kumar Roy
The paper introduces a PyTorch library that unifies 55 hyperspectral image models across six deep‑learning paradigms, providing a common registry, automatic tensor adaptation, and standardized constructors. It includes 24 benchmark scenes from various sensors, with tools for data preprocessing, patch extraction, and rigorous train‑test partitioning to avoid overlap. Experiments on 1,320 model‑scene combinations show that scene difficulty outweighs architecture choice, with no single paradigm dominating and small models performing competitively with much larger ones.
By Tanishq Rachamalla, Aryan Das, Srishti Kaushik, Swalpa Kumar Roy
Hyperspectral remote sensing has advanced across diverse deep learning paradigms, including spectral spatial CNNs, Vision Transformers, Mamba, graph neural networks, Kolmogorov Arnold networks, and se...
arXiv:2609.06232v1 Announce Type: cross
Abstract: Ground-truth defect masks in industrial inspection datasets are typically reserved for evaluation. This paper repurposes them as spatial supervision...
By Sajjad Rezvani Boroujeni, Muskan Saraf, Gnana Tulasi Makineni, Tom Bush, Hossein Abedi
arXiv:2609.28283v1 Announce Type: new
Abstract: Several foundation models dedicated to hyperspectral images have recently been made available. These models are trained on large unlabeled datasets and...
By Edgard Dabier, Christophe Kervazo, Pietro Gori, Florence Tupin
arXiv:2607. 23024v1 Announce Type: cross Abstract: High-resolution satellite imagery is the backbone of good land-cover classification, and without that, environmental monitoring, urban planning, and sustainable resource management all fall short.
By Atiq Ur Rehman, Joseph Michael Donovan
The paper investigates how stable band‑selection methods are and how that stability relates to semantic segmentation performance on the Hyperspectral City V2 dataset. Six band‑selection techniques were tested on ten different class‑balanced ROI sets, producing 60 top‑25 band subsets. Results show that Sim‑LP has the highest intra‑method stability, and together with JMIM+CSNR it also delivers the best segmentation results, achieving up to 2.01 mIoU improvement and 18–22× faster CPU inference for a 9‑band subset, though stability does not consistently predict segmentation quality.
By Jiarong Li, Imad Ali Shah, Enda Ward, Martin Glavin, Edward Jones, Brian Deegan
The study evaluates the use of frozen geospatial foundation embeddings (AlphaEarth) for mapping cultivated versus non‑cultivated land in Maine. Using 192 spatially separated patches and USDA Cropland Data Layer labels, a lightweight classifier achieved 93.7% overall accuracy without fine‑tuning, and a nearest‑class‑centroid rule reached 90.2%. A balanced sample of 60,000 labeled pixels was nearly as effective as the full 8.6 million‑pixel pool, and classifiers trained in one year remained accurate across 2018‑2023. In a blind human validation of 385 points, the AlphaEarth‑plus‑random‑forest map matched 95.3% of the consensus, outperforming the CDL reference (91.7%).
By Mohammad Ammar Mughees, Giovanni Montefoschi, Zhongxin Chen, Maria Antonia Brovelli
arXiv:2607. 22725v1 Announce Type: cross Abstract: Data augmentation is routinely used to improve generalization in image classification, but the assumptions underlying standard policies are poorly matched to coherent imaging.
By Mohamed Abdallah Salem, Nourhan Zein Diab
arXiv:2608. 09101v1 Announce Type: cross Abstract: Semantic segmentation models are trained and evaluated against human-drawn masks, yet remote-sensing annotations are often coarse, incomplete, or misaligned; high overlap scores may then reflect agreement with imperfect labels rather than faithfulness to the image, creating an evaluation paradox.
By Shuaishuai Cao, Shuwei Peng, Meng Tang, Min Huang, Youjin Wang, Jie Chen, Jing Ouyang, Zhiwei Zhai