arXiv Computer Vision
Sep 22

Dimensionality reduction for AI based hyperspectral image classification based on XAI

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.

By Vladimir Zeljkovi\'c, Branka Stojanovi\'c, Harald Ganster, Aleksandar Ne\v{s}kovi\'c
arXiv Machine Learning
Aug 18

Convolution-Free Holistic Multivariance Decomposition Layer for Efficient Hyperspectral Image Classification Tensor Networks

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.

By S\"uha Tuna, \"Ulker Ba\c{s}ar
arXiv Computer Vision
4d ago

HyperSAM: A Promptable Foundation Model for Hyperspectral Remote Sensing

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 AI
Aug 13

Remote Sensing and Machine Learning-Based Analysis of Land Use and Vegetation Change in Dhaka District, Bangladesh

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.

By Muhammad Masud Tarek, Md. Alamgir Hossain, Md. Samiul Islam, Muntasir Hasan Kanchan