Phase-Locked SNR Band Selection for Weak Mineral Signal Detection in Hyperspectral Imagery
- URL: http://arxiv.org/abs/2508.00539v2
- Date: Tue, 05 Aug 2025 07:14:05 GMT
- Title: Phase-Locked SNR Band Selection for Weak Mineral Signal Detection in Hyperspectral Imagery
- Authors: Judy X Yang,
- Abstract summary: We propose a two-stage integrated framework for enhanced mineral detection in the Cuprite mining district.<n>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.<n>In the second stage, the refined HSI data is reintroduced into the model, where KMeans clustering is used to extract 12 endmember spectra.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- Spectrum-driven Mixed-frequency Network for Hyperspectral Salient Object
Detection [14.621504062838731]
We propose a novel approach that fully leverages the spectral characteristics by extracting two distinct frequency components from the spectrum.
The Spectral Saliency approximates the region of salient objects, while the Spectral Edge captures edge information of salient objects.
To effectively utilize this dual-frequency information, we introduce a novel lightweight Spectrum-driven Mixed-frequency Network (SMN)
arXiv Detail & Related papers (2023-12-02T08:05:45Z) - Boosting the Generalization Ability for Hyperspectral Image Classification using Spectral-spatial Axial Aggregation Transformer [14.594398447576188]
In the hyperspectral image classification (HSIC) task, the most commonly used model validation paradigm is partitioning the training-test dataset through pixel-wise random sampling.
In our experiments, we found that the high accuracy was reached because the training and test datasets share a lot of information.
We propose a spectral-spatial axial aggregation transformer model, namely SaaFormer, that preserves generalization across dataset partitions.
arXiv Detail & Related papers (2023-06-29T07:55:43Z) - DDS2M: Self-Supervised Denoising Diffusion Spatio-Spectral Model for
Hyperspectral Image Restoration [103.79030498369319]
Self-supervised diffusion model for hyperspectral image restoration is proposed.
textttDDS2M enjoys stronger ability to generalization compared to existing diffusion-based methods.
Experiments on HSI denoising, noisy HSI completion and super-resolution on a variety of HSIs demonstrate textttDDS2M's superiority over the existing task-specific state-of-the-arts.
arXiv Detail & Related papers (2023-03-12T14:57:04Z) - Evaluation of the potential of Near Infrared Hyperspectral Imaging for
monitoring the invasive brown marmorated stink bug [53.682955739083056]
The brown marmorated stink bug (BMSB), Halyomorpha halys, is an invasive insect pest of global importance that damages several crops.
The present study consists in a preliminary evaluation at the laboratory level of Near Infrared Hyperspectral Imaging (NIR-HSI) as a possible technology to detect BMSB specimens.
arXiv Detail & Related papers (2023-01-19T11:37:20Z) - Spectral Unmixing of Hyperspectral Images Based on Block Sparse
Structure [1.491109220586182]
This paper presents an innovative spectral unmixing approach for hyperspectral images (HSIs) based on block-sparse structure and sparse Bayesian learning strategy.
arXiv Detail & Related papers (2022-04-10T09:37:41Z) - Hyperspectral Image Denoising Using Non-convex Local Low-rank and Sparse
Separation with Spatial-Spectral Total Variation Regularization [49.55649406434796]
We propose a novel non particular approach to robust principal component analysis for HSI denoising.
We develop accurate approximations to both rank and sparse components.
Experiments on both simulated and real HSIs demonstrate the effectiveness of the proposed method.
arXiv Detail & Related papers (2022-01-08T11:48:46Z) - FastHyMix: Fast and Parameter-free Hyperspectral Image Mixed Noise
Removal [20.043152870504738]
This paper introduces a fast and parameter-free hyperspectral image mixed noise removal method (termed FastHyMix)
It exploits two main characteristics of hyperspectral data, namely low-rankness in the spectral domain and high correlation in the spatial domain.
The proposed method takes advantage of the low-rankness using subspace representation and the correlation of HSIs by adding a powerful deep image prior.
arXiv Detail & Related papers (2021-09-18T08:35:45Z) - Spectral Splitting and Aggregation Network for Hyperspectral Face
Super-Resolution [82.59267937569213]
High-resolution (HR) hyperspectral face image plays an important role in face related computer vision tasks under uncontrolled conditions.
In this paper, we investigate how to adapt the deep learning techniques to hyperspectral face image super-resolution.
We present a spectral splitting and aggregation network (SSANet) for HFSR with limited training samples.
arXiv Detail & Related papers (2021-08-31T02:13:00Z) - Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in
Frequency Domain [88.7339322596758]
We present a novel Spatial-Phase Shallow Learning (SPSL) method, which combines spatial image and phase spectrum to capture the up-sampling artifacts of face forgery.
SPSL can achieve the state-of-the-art performance on cross-datasets evaluation as well as multi-class classification and obtain comparable results on single dataset evaluation.
arXiv Detail & Related papers (2021-03-02T16:45:08Z) - A Comparative study of Artificial Neural Networks Using Reinforcement
learning and Multidimensional Bayesian Classification Using Parzen Density
Estimation for Identification of GC-EIMS Spectra of Partially Methylated
Alditol Acetates [0.304585143845864]
This study reports the development of a pattern recognition search engine for a World Wide Web-based database of gas chromatography-electron impact mass spectra (GC-EIMS) of partially methylated Alditol acetates (PMAAs)
The developed system is implemented on the world wide web, and is intended to identify PMAAs using submitted spectra of these molecules recorded on any GC-EIMS instrument.
arXiv Detail & Related papers (2020-07-31T17:54:51Z) - Hyperspectral Image Super-resolution via Deep Progressive Zero-centric
Residual Learning [62.52242684874278]
Cross-modality distribution of spatial and spectral information makes the problem challenging.
We propose a novel textitlightweight deep neural network-based framework, namely PZRes-Net.
Our framework learns a high resolution and textitzero-centric residual image, which contains high-frequency spatial details of the scene.
arXiv Detail & Related papers (2020-06-18T06:32:11Z) - Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral
Imagery [79.69449412334188]
In this paper, we investigate how to adapt state-of-the-art residual learning based single gray/RGB image super-resolution approaches.
We introduce a spatial-spectral prior network (SSPN) to fully exploit the spatial information and the correlation between the spectra of the hyperspectral data.
Experimental results on some hyperspectral images demonstrate that the proposed SSPSR method enhances the details of the recovered high-resolution hyperspectral images.
arXiv Detail & Related papers (2020-05-18T14:25:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.