Classification of visible and infrared hyperspectral images based on image segmentation and edge-preserving filtering

Cui, Binge; Ma, Xiudan; Xie, Xiaoyun; Ren, Guangbo; Ma, Yi

doi:10.1016/j.infrared.2016.12.010

Cited by 35 publications

(11 citation statements)

References 37 publications

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“…Each spectrum is thus originated from the different mechanisms, and by applying a temporal voltage pulse train to our device, separable visual information can be achieved. The separated VIS and NIR vision could thus be harnessed in various applications such as image fusion ( 5 ), red-green-blue depth imaging ( 23 ), and classification via image segmentation ( 24 ). The heterojunction photodetector exhibits the same-polarity operation for both near photovoltaic and photoconductive mode, which would alleviate the circuitry complexity compared to the recent multispectral photodetectors (table S1).…”

Section: Discussionmentioning

confidence: 99%

Visible and infrared dual-band imaging via Ge/MoS₂van der Waals heterostructure

Hwang

Park

et al. 2021

Sci. Adv.

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Section: Discussionmentioning

confidence: 99%

Visible and infrared dual-band imaging via Ge/MoS₂van der Waals heterostructure

Hwang

Park

et al. 2021

Sci. Adv.

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“…Therefore, in this paper, the motivation is that how to make more efficient use of spectral-spatial information in HSIs for hyperspectral anomaly detection. In the case of scene classification, numerous spectral-spatial feature extraction methods [1], [44] have been proposed to enhance the classification performance via capturing morphological property in images, such as attribute profiles (AP) [45], [46], extinction profiles (EP) [47], [48], morphological profiles (MP)based methods [49], [50], and its extended versions (EMP) [51], [53]. Among these methods, the EMP has drawn lots of attention.…”

Section: Employs Amentioning

confidence: 99%

Hyperspectral Anomaly Detection With Morphological Random Walker

et al. 2021

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This work proposes a new morphological random walker (MRW) method for hyperspectral anomaly detection. The proposed method introduces a morphology-based objective function into a random walker (RW) algorithm, sufficiently exploiting spatial morphological property and spatial similarity of HSIs for detection. The MRW method comprises two major stages. Firstly, we employ the extended morphological profiles (EMPs) and different operations to extract the spatial morphological property of HSIs. Second, according to the morphological property, we construct a morphology-based objective function. This function is incorporated into the RW-based optimization model, encoding the spatial similarity of HSIs in a weighted graph. Two factors determine the class of test pixels, including the spatial morphological information learned by EMPs, and the spatial correlation among adjoining pixels modeled by the weighted graph. Since the two factors are well considered in the MRW method, the proposed method illustrates outstanding detection performances for several widely used real HSIs.INDEX TERMS Hyperspectral images, anomaly detection, random walker, extended morphological profiles, morphological property.

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“…The effectiveness of AMSCF has been tested on three different hyperspectral image datasets. The first dataset is Indian Pines [52] collected in 1992 from an Airborne Visible Infrared Imaging Spectrometer (AVIRIS) sensor over the Indian Pines region in Northwestern Indiana. This dataset included 220 spectral bands with a spatial size of 145 × 145 pixels, with 200 bands remained and 20 spectral bands removed due to noise and water absorption.…”

Section: Experiments a Hyperspectral Data Descriptionmentioning

confidence: 99%

Adaptive Hyperspectral Image Classification Based on the Fusion of Manifolds Filter and Spatial Correlation Features

Liao

Wang

2020

IEEE Access

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In recent decades, the studies that obtain abundant spatial texture features, using a wide variety of filters for improving the performance of hyperspectral image (HSI) classification, have become a hotspot. However, the classification methods based on various filters are easy to fall into local feature extraction and neglect informative spatial correlation features. This paper presents an adaptive HSI classification method based on the fusion of adaptive manifold filter and spatial correlation feature (AMSCF). In which we use an adaptive manifold filter to extract spatial texture features, and use the domain transform normalized convolution filter and interpolated convolution filter to obtain the spatial correlation features. Besides, the spatial texture features and two correlation features are separately fused and classified by Large Margin Distribution Machine (LDM) to obtain the best classification. The experimental results demonstrate that the proposed AMSCF method is better than the other classification methods. INDEX TERMS Hyperspectral image, large margin distribution machine, adaptive manifold filter, domain transform convolution filter, classification.

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Classification of visible and infrared hyperspectral images based on image segmentation and edge-preserving filtering

Cited by 35 publications

References 37 publications

Visible and infrared dual-band imaging via Ge/MoS₂van der Waals heterostructure

Visible and infrared dual-band imaging via Ge/MoS₂van der Waals heterostructure

Hyperspectral Anomaly Detection With Morphological Random Walker

Adaptive Hyperspectral Image Classification Based on the Fusion of Manifolds Filter and Spatial Correlation Features

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Classification of visible and infrared hyperspectral images based on image segmentation and edge-preserving filtering

Cited by 35 publications

References 37 publications

Visible and infrared dual-band imaging via Ge/MoS2van der Waals heterostructure

Visible and infrared dual-band imaging via Ge/MoS2van der Waals heterostructure

Hyperspectral Anomaly Detection With Morphological Random Walker

Adaptive Hyperspectral Image Classification Based on the Fusion of Manifolds Filter and Spatial Correlation Features

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Visible and infrared dual-band imaging via Ge/MoS₂van der Waals heterostructure

Visible and infrared dual-band imaging via Ge/MoS₂van der Waals heterostructure