Hyperspectral target detection based on transform domain adaptive constrained energy minimization
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TLDR
Wang et al. as discussed by the authors proposed a fractional domain-based revised constrained energy minimization detector, where sliding double window strategy is used to make the best of the local spatial statistical characteristics of testing pixel.About:
This article is published in International Journal of Applied Earth Observation and Geoinformation.The article was published on 2021-12-01 and is currently open access. It has received 3 citations till now. The article focuses on the topics: Fractional Fourier transform & Hyperspectral imaging.read more
Citations
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Hyperspectral Anomaly Detection Based on Improved RPCA with Non-Convex Regularization
TL;DR: This work proposes a non-convex regularized approximation model based on low-rank and sparse matrix decomposition (LRSNCR), which is closer to the original problem than RPCA and has better detection performance.
Journal ArticleDOI
Collaborative-guided spectral abundance learning with bilinear mixing model for hyperspectral subpixel target detection
TL;DR: Wang et al. as discussed by the authors proposed a novel collaborative-guided spectral abundance learning model (denoted as CGSAL) for subpixel target detection based on the bilinear mixing model in hyperspectral images.
Journal ArticleDOI
Fusarium head blight monitoring in wheat ears using machine learning and multimodal data from asymptomatic to symptomatic periods
Ghulam Mustafa,Hengbiao Zheng,Wei Li,Yuming Yin,Yongqing Wang,Meng Zhou,Peng Liu,Muhammad Bilal,Haiyan Jia,Guoqiang Li,Tao Cheng,Yongchao Tian,Weixing Cao,Yan Zhu,Xia Yao +14 more
TL;DR: In this article , a methodology is developed using features extracted from hyperspectral reflectance (HR), chlorophyll fluorescence imaging (CFI), and high-throughput phenotyping (HTP) for asymptomatic to symptomatic disease detection from two consecutive years of experiments.
References
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Journal ArticleDOI
Object detection using transformed signatures in multitemporal hyperspectral imagery
TL;DR: This work has demonstrated the transformation of target spectral signatures to search for candidate targets using multitemporal hyperspectral images without requiring accurate geo-registration.
Journal ArticleDOI
Multitarget Multiple-Instance Learning for Hyperspectral Target Detection
Susan K. Meerdink,James Bocinsky,Alina Zare,Nicholas M. Kroeger,Connor H. McCurley,Daniel Shats,Paul D. Gader +6 more
TL;DR: In this paper, the authors proposed an approach, with two variations, that estimates multiple-target signatures from training samples with imprecise labels: multitarget multiple-instance adaptive cosine estimator (MTMI-ACE) and multitarget multi-instance spectral match filter (MMI-SMF).
Proceedings ArticleDOI
A target-constrained interference-minimized filter for subpixel target detection in hyperspectral imagery
Hsuan Ren,Chein-I Chang +1 more
TL;DR: This paper presents a target-onstrained interference-minimized filter (TCIMF) which does not require to identify interferers, but can minimize the effects caused by interference.
Journal ArticleDOI
Spectral-Spatial Discriminant Feature Learning for Hyperspectral Image Classification
TL;DR: A novel discriminant feature learning (DFL) method, which combines spectral and spatial information into a hypergraph Laplacian, which increases classification accuracy and outperforms the state-of-the-art HSI classification methods.
Journal ArticleDOI
Target detection using difference measured function based matched filter for hyperspectral imagery
TL;DR: The proposed difference measured function based matched filter (DFMF), which could include the famous algorithm MF as a special case, uses a new measured function to build an objective function, and utilizes the gradient descent method to find an optimal projection vector.