Hyperspectral Image Classification via Superpixel Correlation Coefficient Representation

Tu, Bing; Yang, Xianchang; Li, Nanying; Ou, Xianfeng; He, Wei

doi:10.1109/jstars.2018.2866901

Cited by 19 publications

(5 citation statements)

References 65 publications

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“…A multiscale superpixel-level subspace-based SVM for HSI classification was then proposed by Yu et.al [22]. The pixel correlation within each superpixel was also considered by Tu et al [23] in order to exploit spatial-spectral features. Liu et.al [24] developed a multi-morphological superpixel model in for HS image classification.…”

Section: Related Workmentioning

confidence: 99%

“…Liu et.al [24] developed a multi-morphological superpixel model in for HS image classification. In remote sensing community, Entropy Rate Superpixels (ERS) [23] and SLIC [24], algorithms are vastly applied for superpixel segmentation. This is mainly because both these algorithms are faster and they can generate compact superpixels which adhere well with the object boundaries [25].…”

Section: Related Workmentioning

confidence: 99%

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Texture Based Superpixel Segmentation Algorithm for Hyperspectral Image Classification

Subudhi¹,

Patro²,

Biswal³

2022

Preprint

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Superpixel segmentation is a potential preprocessing tool that can simplify an image by creating semantic sub-regions of the image. This is quite effective in reducing the misclassification of isolated pixels. Image segmentation using superpixels has been successfully applied in the computer vision field. Recently, superpixel segmentation is being adopted into HSI processing domain also, due to its superior properties. In this work, for the accurate classification of the Hyperspectral Image, a texture-based superpixel segmentation algorithm is proposed. Local Binary Pattern (LBP) and Gabor filters are first incorporated to extract the local and global image texture information. Next, Simple Linear Iterative Clustering (SLIC) superpixel segmentation algorithm is applied over the extracted texture features and a superpixel segmentation map is obtained. Finally, majority voting strategy is performed between the superpixel segmentation map and the pixel-wise classification map to acquire the final classification map. For validating the effectiveness of the proposed algorithm, experiments were conducted on four popular HSI datasets, namely: Indian Pines, Pavia University, Houston 2013, and Houston 2018 datasets. Superior classification performance was observed by the proposed method in comparison to other state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Texture Based Superpixel Segmentation Algorithm for Hyperspectral Image Classification

Subudhi¹,

Patro²,

Biswal³

2022

Preprint

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“…In most cases, the value of K is chosen manually ( [20], [24], [39], [113], [21]) based on observation and experience of the user. In very few works, automatic estimation of the correct K value is done.…”

Section: A Determination Of Number Of Superpixelsmentioning

confidence: 99%

A Survey on Superpixel Segmentation as a Preprocessing Step in Hyperspectral Image Analysis

Subudhi

Patro

Biswal

et al. 2021

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

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Recent developments in hyperspectral sensors have made it possible to acquire HyperSpectral Images (HSI) with higher spectral and spatial resolution. Hence, it is now possible to extract detailed information about relatively smaller structures. Despite these advantages, HSI suffers from many challenges also, like higher spatial variability of spectral signatures, the Hughes effect due to higher dimensionality, and a limited number of labeled training samples compared to the dimensions of the spectral space. Superpixels can be a potentially effective tool in tackling these challenges. Superpixel segmentation is a process of segmenting the spatial image into several semantic sub-regions with similar characteristic features. Such grouping by similarity can significantly ease the subsequent processing steps. Because of this, superpixels have been successfully applied to various fields of HSI processing like Classification, Spectral Unmixing, Dimensionality Reduction, Band Selection, Active Learning, Denoising, and Anomaly Detection. This paper focuses on Classification, presenting a detailed survey of superpixel segmentation approaches for the classification of HSI. The Superpixel creation algorithm framework and post-processing frameworks for superpixels in HSI are also analyzed. Also, a brief description of various application areas of superpixels is provided. An experimental analysis of existing superpixel segmentation approaches is also provided in this paper, supported by quantitative results on standard benchmark datasets. The challenges and future research directions for the implementation of superpixel algorithms are also discussed.

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“…These methods only consider spectral information, ignoring the spatial correlation of neighboring pixels. Therefore, various spatial-spectral classification methods have been developed [32][33][34]. For example, Kang et al proposed a spectral-spatial classification method based on edge-preserving filtering (EPF) [35].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification with Multi-Scale Feature Extraction

Fang

et al. 2019

Remote Sensing

Self Cite

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Spectral features cannot effectively reflect the differences among the ground objects and distinguish their boundaries in hyperspectral image (HSI) classification. Multi-scale feature extraction can solve this problem and improve the accuracy of HSI classification. The Gaussian pyramid can effectively decompose HSI into multi-scale structures, and efficiently extract features of different scales by stepwise filtering and downsampling. Therefore, this paper proposed a Gaussian pyramid based multi-scale feature extraction (MSFE) classification method for HSI. First, the HSI is decomposed into several Gaussian pyramids to extract multi-scale features. Second, we construct probability maps in each layer of the Gaussian pyramid and employ edge-preserving filtering (EPF) algorithms to further optimize the details. Finally, the final classification map is acquired by a majority voting method. Compared with other spectral-spatial classification methods, the proposed method can not only extract the characteristics of different scales, but also can better preserve detailed structures and the edge regions of the image. Experiments performed on three real hyperspectral datasets show that the proposed method can achieve competitive classification accuracy.

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Hyperspectral Image Classification via Superpixel Correlation Coefficient Representation

Cited by 19 publications

References 65 publications

Texture Based Superpixel Segmentation Algorithm for Hyperspectral Image Classification

Texture Based Superpixel Segmentation Algorithm for Hyperspectral Image Classification

A Survey on Superpixel Segmentation as a Preprocessing Step in Hyperspectral Image Analysis

Hyperspectral Image Classification with Multi-Scale Feature Extraction

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