The study evaluates eleven feature detector–matcher combinations for lunar visual SLAM using synthetic, lunar-testbed, and terrestrial-analogue datasets. SuperPoint pairings outperform SIFT with ratio-tested FLANN in a rotation-only metric on lunar testbeds, but SIFT excels when rotation and translation are jointly scored on synthetic data. In stereo VO, SuperPoint maintains tracking across all datasets, while classical detectors are more sensitive to lighting conditions, and the fastest learned combination is SuperPoint with LightGlue.
By Guillem Casadesus Vila, Grace Gao
arXiv:2610.35642v1 Announce Type: cross
Abstract: Visual Place Recognition (VPR) is an important means for loop closure detection in visual Simultaneous Localization and Mapping (SLAM). While state-o...
By Richard Fechner, Peer Neubert
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.
By Li Pang, Xinqiao Wu, Jing Yao, Pedram Ghamisi, Jun Zhou, Zhengchao Chen, Deyu Meng, Xiangyong Cao
arXiv:2608.30537v1 Announce Type: cross
Abstract: Rapid mineral characterization is essential for applications ranging from mineral exploration to industrial ore processing. To this end, Hyperspectra...
By Eleftheria Tetoula-Tsonga (Institute of Communication and Computer Systems, Athens, Greece), George Arvanitakis (Geonova, Athens, Greece), Theodoros Giannakas (Institute of Communication and Computer Systems, Athens, Greece)
MANTLE is a multi‑task adaptive network designed for planetary perception, featuring a shared DINOv2 backbone with separate heads for landform classification and boulder segmentation. Trained on HiRISE and MSL imagery, it achieved 92.56% accuracy on seven Martian terrain classes and a 0.753 IoU for boulder segmentation, with strong cross‑sol generalization. The framework follows the Modular Uplink Principle, allowing lightweight task‑specific heads to be trained on Earth and uplinked to the rover without retraining the core model.
By Pranav Durai, Gary Doran
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...
Rapid mineral characterization is essential for applications ranging from mineral exploration to industrial ore processing. To this end, Hyperspectral Imaging (HSI) has emerged as a promising sensing...
arXiv:2604.08884v2 Announce Type: replace-cross
Abstract: Multimodal Large Language Models (MLLMs) have achieved strong performance on RGB image understanding, yet their ability to use spectral evide...
By Xinyu Zhang, Zurong Mai, Qingmei Li, Xiaoya Fan, Zjin Liao, Haoyuan Liang, Yibin Wen, Yuhang Chen, Chan Tsz Ho, Bi Tianyuan, Ruifeng Su, Zihao Qiang, Juepeng Zheng, Jianxi Huang, Yutong Lu, Haohuan Fu
arXiv:2609.01060v1 Announce Type: cross
Abstract: Compact snapshot hyperspectral cameras provide rich instantaneous spectral measurements for ground-level machine vision, but at lower spatial resolut...
By Mohamad Jouni, Aur\'elien Godet, Mauro Dalla Mura
The paper presents M2Heat, a physics-inspired framework for fusing hyperspectral and LiDAR data in land‑cover classification. It introduces a visual heat conduction module (vHeat) and Frequency Value Embeddings (FVEs) to model anisotropic information flow, capturing global dependencies with sub‑quadratic complexity. Combined with a Cross‑Frequency Fusion (CFF) module, M2Heat delivers discriminative, robust features and achieves competitive performance on Trento, Houston2013, and Augsburg benchmarks while offering an interpretable heat‑conduction perspective.
By Kan Wei, Jiahui Cui, Jing Yao, Xinyu Zhao, Lei Wang, Pedram Ghamisi
arXiv:2607. 16366v1 Announce Type: cross Abstract: Robotic navigation in unstructured environments requires robust situational awareness to safely traverse hazards such as steep slopes and rocky terrain.
By Raul Castilla-Arquillo, Carlos Perez-del-Pulgar, Levin Gerdes, Alfonso Garcia-Cerezo, Miguel A. Olivares-Mendez
The fusion of hyperspectral (HS) and Light Detection and Ranging (LiDAR) data plays a crucial role in enhancing land-cover classification by jointly exploiting spectral, spatial, and structural cues....