Classification of Hyperspectral or Trichromatic Measurements of Ocean Color Data into Spectral Classes

Prasad, Dilip K.; Agarwal, Krishna

doi:10.3390/s16030413

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…HSI has been widely used in environmental monitoring [1], mineral exploration [2], agricultural remote sensing [3], vegetation ecology [4], ocean remote sensing [5], and other earth observation tasks. In these applications, because HSI exhibits mixed land cover categories, resulting in high intraclass variability and interclass similarity, it is a huge challenge for any classification model.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Depth-Separable Residual Networks for Hyperspectral Image Classification

Zhao

Wang

et al. 2020

Complexity

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At present, the classification of the hyperspectral image (HSI) based on the deep convolutional network has made great progress. Due to the high dimensionality of spectral features, limited samples of ground truth, and high nonlinearity of hyperspectral data, effective classification of HSI based on deep convolutional neural networks is still difficult. This paper proposes a novel deep convolutional network structure, namely, a hybrid depth-separable residual network, for HSI classification, called HDSRN. The HDSRN model organically combines 3D CNN, 2D CNN, multiresidual network ROR, and depth-separable convolutions to extract deeper abstract features. On the one hand, due to the addition of multiresidual structures and skip connections, this model can alleviate the problem of over fitting, help the backpropagation of gradients, and extract features more fully. On the other hand, the depth-separable convolutions are used to learn the spatial feature, which reduces the computational cost and alleviates the decline in accuracy. Extensive experiments on the popular HSI benchmark datasets show that the performance of the proposed network is better than that of the existing prevalent methods.

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Section: Introductionmentioning

confidence: 99%

Hybrid Depth-Separable Residual Networks for Hyperspectral Image Classification

Zhao

Wang

et al. 2020

Complexity

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“…However, a number of other classifications have been developed and used for various applications. Representative schemes have been based on • Cluster Analysis of R rs spectra: Eleveld et al (2017); Melin and Vantrepotte (2015); Prasad and Agarwal (2016); Wei et al (2016) • Fuzzy Logic Classification of R rs spectra: Moore et al (2001Moore et al ( , 2009Moore et al ( , 2014 • CHAPTER 5…”

Section: Other Classification Schemesmentioning

confidence: 99%

Recipes for the Derivation of Water Quality Parameters Using the High-Spatial-Resolution Data from Sensors on Board Sentinel-2A, Sentinel-2B, Landsat-5, Landsat-7, Landsat-8, and Landsat-9 Satellites

et al. 2023

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Satellites have provided high-resolution ( < 100 m) water color (i.e., remote sensing reflectance) and thermal emission imagery of aquatic environments since the early 1980s; however, global operational water quality products based on these data are not readily available (e.g., temperature, chlorophyll- a , turbidity, and suspended particle matter). Currently, because of the postprocessing required, only users with expressive experience can exploit these data, limiting their utility. Here, we provide paths (recipes) for the nonspecialist to access and derive water quality products, along with examples of applications, from sensors on board Landsat-5, Landsat-7, Landsat-8, Landsat-9, Sentinel-2A, and Sentinel-2B. We emphasize that the only assured metric for success in product derivation and the assigning of uncertainties to them is via validation with in situ data. We hope that this contribution will motivate nonspecialists to use publicly available high-resolution satellite data to study new processes and monitor a variety of novel environments that have received little attention to date.

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“…Remotely sensed hyperspectral data which is collected by hyperspectral sensors consist of hundreds of contiguous spectral bands with high resolutions [1,2]. Recent studies in remote sensing have shown that hyperspectral images (HSIs) have been successfully applied in precision agriculture, urban planning, environmental monitoring and various other fields [3][4][5][6]. One of the typical characteristics of an HSI is that it cannot only obtain the scene information in the two-dimensional space of the target image but can also acquire one-dimensional spectral information with a high resolution to characterize the physical property.…”

Section: Introductionmentioning

confidence: 99%

Representative Band Selection for Hyperspectral Image Classification

Xie

Lei

et al. 2018

IJGI

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The high dimensionality of hyperspectral images (HSIs) brings great difficulty for their later data processing. Band selection, as a commonly used dimension reduction technique, is the selection of optimal band combinations from the original bands, while attempting to remove the redundancy between bands and maintain a good classification ability. In this study, a novel hybrid filter-wrapper band selection method is proposed by a three-step strategy, i.e., band subset decomposition, band selection and band optimization. Based on the information gain (IG) and the spectral curve of the hyperspectral dataset, the band subset decomposition technique is improved, and a random selection strategy is suggested. The implementation of the first two steps addresses the problem of reducing inter-band redundancy. An optimization strategy based on a gray wolf optimizer (GWO) ensures that the selected band combination has a good classification ability. The classification performance of the selected band combination is verified on the Indian Pines, Pavia University and Salinas hyperspectral datasets with the aid of support vector machine (SVM) with a five-fold cross-validation. By comparing the proposed IG-GWO method with five state-of-the-art band selection approaches, the superiority of the proposed method for HSIs classification is experimentally demonstrated on three well-known hyperspectral datasets.

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Classification of Hyperspectral or Trichromatic Measurements of Ocean Color Data into Spectral Classes

Cited by 5 publications

References 20 publications

Hybrid Depth-Separable Residual Networks for Hyperspectral Image Classification

Hybrid Depth-Separable Residual Networks for Hyperspectral Image Classification

Recipes for the Derivation of Water Quality Parameters Using the High-Spatial-Resolution Data from Sensors on Board Sentinel-2A, Sentinel-2B, Landsat-5, Landsat-7, Landsat-8, and Landsat-9 Satellites

Representative Band Selection for Hyperspectral Image Classification

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