Detection and Identification of Sub-Millimeter Films of Organic Compounds on Environmental Surfaces Using Short-Wave Infrared Hyperspectral Imaging: Algorithm Development Using a Synthetic Set of Targets

Kendler, Shai; Ron, Izhar; Cohen, Shay B.; Raich, Raviv; Mano, Ziv; Fishbain, Barak

doi:10.1109/jsen.2018.2886269

Cited by 15 publications

(11 citation statements)

References 24 publications

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“…In this paper, we consider the non-linear intimate mixing scenario of [9]. In this setting, the measurement of each pixel is modeled as a nonnegative linear combination of the spectral signature from a background material and the This paper extends our preliminary work presented in Hollis J, Raich R, Kim J, Fishbain B, Kendler Fig.…”

Section: Introductionmentioning

confidence: 91%

“…Other works have explored settings in which nonlinear effects, such as reflection and refraction, have a non-negligible effect on the spectral mixtures represented by each pixel. There are a wide range of potential nonlinear unmixing models [7], [8], and dedicated algorithms for cases where the measured signature is noisy [9]- [11].…”

Section: Introductionmentioning

confidence: 99%

“…We assume only one foreground material is present. Similar to [9] and [12], we assume that the background material is nearly invariant within small neighborhoods of pixels N k (henceforth referred to as patches) for k = 1, . .…”

Section: Introductionmentioning

confidence: 99%

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Foreground Signature Extraction for an Intimate Mixing Model in Hyperspectral Image Classification

Hollis

Raich

Kim

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The problem of foreground material signature extraction in an intimate (nonlinear) mixing setting is considered. It is possible for a foreground material signature to appear in combination with multiple background material signatures.We explore a framework for foreground material signature extraction based on a patch model that accounts for such background variation. We identify data conditions under which a foreground material signature can be extracted up to scaling and elementwise-inverse variations. We present algorithms based on volume minimization and endpoint member identification to recover foreground material signatures under these conditions. Numerical experiments on real and synthetic data illustrate the efficacy of the proposed algorithms.

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Section: Introductionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

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Foreground Signature Extraction for an Intimate Mixing Model in Hyperspectral Image Classification

Hollis

Raich

Kim

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…(Corresponding author: Ke Wu) Yanting Zhan, Ke Wu and Yanni Dong work with Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China. (email: z1606229857@163.com, kewu@cug.edu.cn, dongyanni@cug.edu.cn) urbanization analysis [7], precision agriculture [8], environmental monitoring [9], change detection [10]- [11] and target detection [12].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Spectral–Spatial Residual Attention Network for Hyperspectral Image Classification

Zhan

Dong

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Deep learning has achieved good performance in hyperspectral image classification (HSIC). Many methods based on deep learning use deep and complex network structures to extract rich spectral and spatial features of hyperspectral images (HSIs) with high accuracy. During the process, how to accurately extract the features and information from pixel blocks in HSIs is important. All of the spectral features are treated equally in classification, and the input of the network often contains much useless pixel information, leading to a low classification result. To solve this problem, an enhanced spectral-spatial residual attention network (ESSRAN) is proposed for HSIC in this paper. In the proposed network, the spectral-spatial attention network (SSAN), residual network (ResNet) and long-short term memory (LSTM) are combined to extract more discriminative spectral and spatial features. More specifically, SSAN is first applied to extract image features by using the spectral attention module to emphasize useful bands and suppress useless bands. The spatial attention module is used to emphasize pixels that have same category with the central pixel. Then, these obtained features are fed into an improved ResNet, which adopts LSTM to learn representative high-level semantic features of the spectral sequences, since the use of ResNet can prevent gradient disappearance and explosion. The proposed ESSRAN model is implemented on three commonly used HSI datasets and compared to some state-of-the-art methods. The results confirm that ESSRAN effectively improves accuracy.

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“…Since these images have hundreds of continuous observation bands across the entire electromagnetic spectrum, more spectral information can be obtained when they are used. Therefore, they are widely used in atmospheric detection [3], [4], environmental monitoring [5], [6], geological prospecting [7], [8], military reconnaissance [9], [10], and other fields. HSI classification [11]- [14] uses the rich information contained in HSI to assign unique category labels for each pixel, which is an important aspect of HSI applications.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Kernel Mode Using a Local Binary Pattern and Random Patch Convolution for Hyperspectral Image Classification

Yao

Yin

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Detection and Identification of Sub-Millimeter Films of Organic Compounds on Environmental Surfaces Using Short-Wave Infrared Hyperspectral Imaging: Algorithm Development Using a Synthetic Set of Targets

Cited by 15 publications

References 24 publications

Foreground Signature Extraction for an Intimate Mixing Model in Hyperspectral Image Classification

Foreground Signature Extraction for an Intimate Mixing Model in Hyperspectral Image Classification

Enhanced Spectral–Spatial Residual Attention Network for Hyperspectral Image Classification

A Multi-Kernel Mode Using a Local Binary Pattern and Random Patch Convolution for Hyperspectral Image Classification

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