A novel hyperspectral remote sensing images classification using Gaussian Processes with conditional random fields

Yao, Futian; Qian, Yuntao; Hu, Zhenfang; Li, Jiming

doi:10.1109/iske.2010.5680882

Cited by 10 publications

(7 citation statements)

References 16 publications

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“…An increasing number of classifier models have been successfully used in hyperspectral image classification, such as statistical Gaussian (Yao et al , 2010) and Bayesian models (Mohamed and Farag, 2005), neural network models (Hou, 2011), support vector machines (Melgani and Bruzzone, 2004) (SVM), kernel-based methods and random forests (Ham et al , 2005). The operation of random forest is to build a large number of decision trees during training and then output the class output mode of a single tree.…”

Section: Underwater Applications Of Hyperspectral Imagesmentioning

confidence: 99%

Hyperspectral imaging for underwater object detection

Wang

Liu

et al. 2021

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Purpose This paper aims to demonstrate the principle and practical applications of hyperspectral object detection, carry out the problem we now face and the possible solution. Also some challenges in this field are discussed. Design/methodology/approach First, the paper summarized the current research status of the hyperspectral techniques. Then, the paper demonstrated the development of underwater hyperspectral techniques from three major aspects, which are UHI preprocess, unmixing and applications. Finally, the paper presents a conclusion of applications of hyperspectral imaging and future research directions. Findings Various methods and scenarios for underwater object detection with hyperspectral imaging are compared, which include preprocessing, unmixing and classification. A summary is made to demonstrate the application scope and results of different methods, which may play an important role in the application of underwater hyperspectral object detection in the future. Originality/value This paper introduced several methods of hyperspectral image process, give out the conclusion of the advantages and disadvantages of each method, then demonstrated the challenges we face and the possible way to deal with them.

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Section: Underwater Applications Of Hyperspectral Imagesmentioning

confidence: 99%

Hyperspectral imaging for underwater object detection

Wang

Liu

et al. 2021

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“…Recently, classifier models have been successfully applied in HSI classification, such as the statistical Gaussian (Yao et al , 2010) and Bayesian models (Mohamed and Farag, 2005), neural networks model (Hou, 2011), support vector machine (SVM; Melgani and Bruzzone, 2004), kernel-based methods (Camps-Valls and Bruzzone, 2005) and the random forest. Random forest operates by constructing a multitude of decision trees at training time, and outputting the mode of the class output by individual trees (Ham et al , 2005; Wu et al , 2008).…”

Section: Classification Approachesmentioning

confidence: 99%

“…The Gaussian process (GP) method is discussed by Yao et al (2010). Considering that the variables belonging to different classes interact independently, this theory extends the traditional multivariate GP to the infinite dimensionality.…”

Section: Classification Approachesmentioning

confidence: 99%

Survey of supervised classification techniques for hyperspectral images

Qiu

et al. 2017

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Purpose This paper aims to provide a framework of the supervised hyperspectral classification, to study the traditional flowchart of hyperspectral image (HIS) analysis and processing. HSI technology has been proposed for many years, and the applications of this technology were promoted by technical advancements. Design/methodology/approach First, the properties and current situation of hyperspectral technology are summarized. Then, this paper introduces a series of common classification approaches. In addition, a comparison of different classification approaches on real hyperspectral data is conducted. Finally, this survey presents a discussion on the classification results and points out the classification development tendency. Findings The core of this survey is to review of the state of the art of the classification for hyperspectral images, to study the performance and efficiency of certain implementation measures and to point out the challenges still exist. Originality value The study categorized the supervised classification for hyperspectral images, demonstrated the comparisons among these methods and pointed out the challenges that still exist.

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“…Since these models only contain unary and pairwise interactions, they are called pairwise models. Unary potentials are derived from pixel-wise classifiers, such as Gaussian maximum likelihood classifier [34], logistic regressions [41], probabilistic support vector machines [75], Gaussian mixture models [44], Gaussian processes [87], and ensemble methods [53,86,39]. Potts model (including its contrast sensitive version) is the most popular pairwise potential function.…”

Section: Introductionmentioning

confidence: 99%

A tutorial on modelling and inference in undirected graphical models for hyperspectral image analysis

Gewali

Monteiro

2018

International Journal of Remote Sensing

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Undirected graphical models have been successfully used to jointly model the spatial and the spectral dependencies in earth observing hyperspectral images. They produce less noisy, smooth, and spatially coherent land cover maps and give top accuracies on many datasets. Moreover, they can easily be combined with other state-of-the-art approaches, such as deep learning. This has made them an essential tool for remote sensing researchers and practitioners. However, graphical models have not been easily accessible to the larger remote sensing community as they are not discussed in standard remote sensing textbooks and not included in the popular remote sensing software and toolboxes. In this tutorial, we provide a theoretical introduction to Markov random fields and conditional random fields based spatial-spectral classification for land cover mapping along with a detailed step-by-step practical guide on applying these methods using freely available software. Furthermore, the discussed methods are benchmarked on four public hyperspectral datasets for a fair comparison among themselves and easy comparison with the vast number of methods in literature which use the same datasets. The source code necessary to reproduce all the results in the paper is published on-line to make it easier for the readers to apply these techniques to different remote sensing problems.illumination, shadows, purity of pixels, viewing geometry, atmospheric conditions, and noise across the image. This problem can be alleviated by combining spatial contextual information with the spectral information. Spatial-spectral classificationSpatial information can be utilized together with spectral information to produce more accurate and spatially coherent land cover maps [24]. Land covers in the environment tend to be much larger than the ground pixel size of the sensors leading to regions of pixels belonging to a common material class. Additionally, some land cover classes are more likely to exists in close vicinity than others and some land cover classes are highly unlikely to occur together. This leads to strong relationships between the neighboring pixel labels in an image. For example, if a pixel belongs to a class, say building, there is a high probability that the surrounding pixels also belong to the same class, building. Similarly, the probability of neighboring pixels of a building pixel belonging to the road class or the parking lot class is typically much higher than them belonging to the forest class or the bare soil class in an urban scene. These kind of relationships can be exploited by spatial-spectral classifiers. Even though there are exceptions, e.g., [12], [82], and [45], the vast majority of spatial-spectral classifiers can be categorized into two distinct groups-methods that perform spatial-spectral feature extraction followed by pixel-wise classification and methods that combine undirected graphical model and pixel-wise classification.Spatial-spectral feature extraction utilizes the spectra of the neighborhood of pixels around th...

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A novel hyperspectral remote sensing images classification using Gaussian Processes with conditional random fields

Cited by 10 publications

References 16 publications

Hyperspectral imaging for underwater object detection

Hyperspectral imaging for underwater object detection

Survey of supervised classification techniques for hyperspectral images

A tutorial on modelling and inference in undirected graphical models for hyperspectral image analysis

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