Improving hyperspectral sub-pixel target detection in multiple target signatures using a revised replacement signal model

Khoshboresh-Masouleh, Mehdi; Hasanlou, Mahdi

doi:10.1080/22797254.2020.1850179

Cited by 8 publications

(9 citation statements)

References 47 publications

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“…In the next subsection, we describe two different procedures to solve problem (19). Denoting by α the generic solution returned by these procedures, we use it in (17) and the final expression of the detection architecture is…”

Section: Glrt-based Detector Designsmentioning

confidence: 99%

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Multiple Sub-Pixel Target Detection for Hyperspectral Imaging Systems

Addabbo

Fiscante

Giunta

et al. 2023

IEEE Trans. Signal Process.

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Hyperspectral target detection is a task of primary importance in remote sensing since it allows for location and discrimination of target features. To this end, the reflectance maps, which contain the spectral signatures and related abundances of the materials in the observed scene, are often used. However, due to the low spatial resolution of most hyperspectral sensors, targets occupy a fraction of the pixel and, hence, the spectra of different sub-pixel targets (including the background spectrum) are mixed together within the same pixel. To solve this issue, in this paper, we adopt a generalized replacement model accounting for multiple sub-pixel target spectra and formulate the detection problem at hand as a binary hypothesis test where under the alternative hypothesis the target is modeled in terms of a linear combination of endmembers whose coefficients also account for the presence of the background. Then, we devise detection architectures based upon the generalized likelihood ratio test where the unknown parameters are suitably estimated through procedures inspired by the maximum likelihood approach. The performances of the proposed decision schemes are evaluated by means of both synthetic as well as real data and compared with an analogous counterpart by showing the effectiveness of the proposed procedure.

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Section: Glrt-based Detector Designsmentioning

confidence: 99%

“…A. Solution to Equation (19) The approach devised here relies on an iterative solution of (19). In particular, we firstly highlight the dependence of the objective function from a single entry of α, say α j , and then, at each iteration, we minimize g(α) with respect to α j as the index j varies.…”

Section: Glrt-based Detector Designsmentioning

confidence: 99%

Multiple Sub-Pixel Target Detection for Hyperspectral Imaging Systems

Addabbo

Fiscante

Giunta

et al. 2023

IEEE Trans. Signal Process.

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show abstract

“…To this end, we resort to an hyperspectral dataset, namely the Rochester Institute of Technology (RIT) experiment 3 [18]. The RIT open data experiment has been specially designed for target detection and has been widely used in the open literature [12], [19]. Indeed, a corrected and geo-registered reflectance map is available so that the detection performance will be independent from any particular experimental setup.…”

Section: Performance Analysismentioning

confidence: 99%

Multiple Sub-Pixel Target Detection for Hyperspectral Imaging Systems

Addabbo¹,

Fiscante²,

Guerra³

et al. 2023

Preprint

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Hyperspectral target detection is a task of primary importance in remote sensing since it allows identification, location, and discrimination of target features. To this end, the reflectance maps, which contain the spectral signatures and related abundances of the materials in the observed scene, are often used. However, due to the low spatial resolution of most hyperspectral sensors, targets occupy a fraction of the pixel and, hence, the spectra of different sub-pixel targets (including the background spectrum) are mixed together within the same pixel. To solve this issue, in this paper, we adopt a generalized replacement model accounting for multiple sub-pixel target spectra and formulate the detection problem at hand as a binary hypothesis test where under the alternative hypothesis the target is modeled in terms of a linear combination of endmembers whose coefficients also account for the presence of the background. Then, we devise detection architectures based upon the generalized likelihood ratio test where the unknown parameters are suitably estimated through procedures inspired by the maximum likelihood approach. The performances of the proposed decision schemes are evaluated by means of both synthetic as well as real data and compared with an analogous counterpart by showing the effectiveness of the proposed procedure.

show abstract

“…In recent years, the role of traditional methods such as terrestrial mapping and traditional aerial photogrammetry techniques has been dimmed due to the high cost and also the need for a long time to generate a multi-task dataset for scene understanding (Crawshaw, 2020;Masouleh and Sadeghian, 2019;Ruder, 2017;Zhang and Yang, 2018). An affordable and accurate way to generate multitask data is to use the combination of an Unmanned Aerial Vehicle (UAV) with a high-resolution digital camera (e.g., RGB, Multi-spectral, Thermal, or Hyperspectral) and machine learning methods (Bayanlou and Khoshboresh-Masouleh, 2020;Khoshboresh-Masouleh and Hasanlou, 2020). Although UAV with a high-resolution digital camera is an efficient tool for data generation, there is still a lack of multi-task datasets for scene understanding (Khoshboresh Masouleh and Shah-Hosseini, 2019).…”

Section: Motivationmentioning

confidence: 99%

Multi-Task Learning From Fixed-Wing Uav Images for 2d/3d City Modelling

Bayanlou

Khoshboresh-Masouleh

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Single-task learning in artificial neural networks will be able to learn the model very well, and the benefits brought by transferring knowledge thus become limited. In this regard, when the number of tasks increases (e.g., semantic segmentation, panoptic segmentation, monocular depth estimation, and 3D point cloud), duplicate information may exist across tasks, and the improvement becomes less significant. Multi-task learning has emerged as a solution to knowledge-transfer issues and is an approach to scene understanding which involves multiple related tasks each with potentially limited training data. Multi-task learning improves generalization by leveraging the domain-specific information contained in the training data of related tasks. In urban management applications such as infrastructure development, traffic monitoring, smart 3D cities, and change detection, automated multi-task data analysis for scene understanding based on the semantic, instance, and panoptic annotation, as well as monocular depth estimation, is required to generate precise urban models. In this study, a common framework for the performance assessment of multi-task learning methods from fixed-wing UAV images for 2D/3D city modelling is presented.

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Improving hyperspectral sub-pixel target detection in multiple target signatures using a revised replacement signal model

Cited by 8 publications

References 47 publications

Multiple Sub-Pixel Target Detection for Hyperspectral Imaging Systems

Multiple Sub-Pixel Target Detection for Hyperspectral Imaging Systems

Multiple Sub-Pixel Target Detection for Hyperspectral Imaging Systems

Multi-Task Learning From Fixed-Wing Uav Images for 2d/3d City Modelling

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