Dimensionality Reduction for Hyperspectral Image Classification
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The paper proposes using AI-based dimensionality reduction to improve wood recycling by applying convolutional neural networks to multi‑channel hyperspectral imaging with over 200 spectral channels. It focuses on streamlining the feature space for training and inference while incorporating explainable AI methods. The authors present a solution framework aimed at enhancing the sustainability and efficiency of wood recycling processes.
arXiv:2608. 16241v1 Announce Type: cross Abstract: Feature extraction for hyperspectral image classification is conventionally addressed using rigid tensor decompositions that fail to capture complex spatio-spectral interdependencies, or heavily parameterized convolutional neural networks that are computationally expensive.
HyperSAM is a promptable foundation model for hyperspectral remote sensing that integrates a data‑centric synthesis pipeline with a spectral adaptation architecture based on Segment Anything Model 3 (SAM3). The model generates full‑spectrum hyperspectral cubes from high‑resolution multispectral imagery using a physics‑informed abundance‑transfer generator, and employs SAM3‑derived pseudo‑masks for object‑centric supervision. With a frozen SAM3 RGB branch, a trainable hyperspectral encoder, ControlNet‑style feature injection, and a mixture‑of‑experts mask refiner, HyperSAM demonstrates strong generalization across diverse hyperspectral tasks such as classification, anomaly detection, change detection, target detection, and airborne oil‑spill mapping.
Hyperspectral remote sensing provides dense spectral measurements that are indispensable for material-level Earth observation, yet the construction of a general-purpose hyperspectral foundation model...
arXiv:2606. 29053v1 Announce Type: new Abstract: In general, an ensemble classifier is more accurate than a single classifier.
arXiv:2608. 12001v1 Announce Type: cross Abstract: Rapid urbanization in Dhaka District, Bangladesh has triggered substantial alterations in land use and environmental conditions, necessitating systematic monitoring for informed urban planning and ecological sustainability.