A Fast Region Growing Based Superpixel Segmentation for Hyperspectral Image Classification

Xu, Qianqian; Fu, Peng; Sun, Quansen; Wang, Tao

doi:10.1007/978-3-030-31723-2_66

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…In this method, only the first principal component map was used for segmentation, which may cause the loss of important information. Based on the region growth method in [17], Xu et al proposed a super-pixel segmentation method [18]. In this method, on the basis of fast region growth, the low-dimensional representations of hyperspectral images were achieved without sacrificing subsequent classification performance.…”

Section: Introductionmentioning

confidence: 99%

Superpixel Segmentation of Hyperspectral Images Based on Entropy and Mutual Information

Lin

Zhang

2020

Applied Sciences

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Superpixel segmentation (SS) methods have been proven to be feasible in improving the performance of hybrid algorithms on hyperspectral images (HSIs). In this paper, a superpixel segmentation algorithm based on the information measures with color histogram driving (IM-CHD) was proposed. First, Shannon entropy was applied to measure the image information and preliminarily select spectral bands. Mutual information (MI) is derived from the concept of entropy and measures the statistical dependence between two random variables. Also, MI can effectively identify the redundant spectral bands. Therefore, in this paper, both MI and color matching functions (CMF) were used to select the most useful spectral bands. Second, the selected spectral bands were combined into a false color image containing the main spectral information. A local optimization algorithm named “hill climbing” was used to achieve the superpixel segmentation. Finally, parameter selection experiments and comparative experiments were performed on two hyperspectral data sets. The experimental results showed that the IM-CHD method was more efficient and accurate than other state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

Superpixel Segmentation of Hyperspectral Images Based on Entropy and Mutual Information

Lin

Zhang

2020

Applied Sciences

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Extraction of Spectral-Spatial 3-Dimensional Homogeneous Regions from Hyperspectral Images and Its Application to Fast Classification

Cai

Geng

Liang

et al. 2020

Pattern Recognition and Computer Vision

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High-Resolution Rice Mapping Based on SNIC Segmentation and Multi-Source Remote Sensing Images

et al. 2021

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High-resolution crop mapping is of great significance in agricultural monitoring, precision agriculture, and providing critical information for crop yield or disaster monitoring. Meanwhile, medium resolution time-series optical and synthetic aperture radar (SAR) images can provide useful phenological information. Combining high-resolution satellite data and medium resolution time-series images provides a great opportunity for fine crop mapping. Simple Non-Iterative Clustering (SNIC) is a state-of-the-art image segmentation algorithm that shows the advantages of efficiency and high accuracy. However, the application of SNIC in crop mapping based on the combination of high-resolution and medium-resolution images is unknown. Besides, there is still little research on the influence of the superpixel size (one of the key user-defined parameters of the SNIC method) on classification accuracy. In this study, we employed a 2 m high-resolution GF-1 pan-sharpened image and 10 m medium resolution time-series Sentinel-1 C-band Synthetic Aperture Radar Instrument (C-SAR) and Sentinel-2 Multispectral Instrument (MSI) images to carry out rice mapping based on the SNIC method. The results show that with the increase of the superpixel size, the classification accuracy increased at first and then decreased rapidly after reaching the summit when the superpixel size is 27. The classification accuracy of the combined use of optical and SAR data is higher than that using only Sentinel-2 MSI or Sentinel-1 C-SAR vertical transmitted and vertical received (VV) or vertical transmitted and horizontal received (VH) data, with overall accuracies of 0.8335, 0.8282, 0.7862, and 0.7886, respectively. Meanwhile, the results also indicate that classification based on superpixels obtained by SNIC significantly outperforms classification based on original pixels. The overall accuracy, producer accuracy, and user accuracy of SNIC superpixel-based classification increased by 9.14%, 17.16%, 27.35% and 1.36%, respectively, when compared with the pixel-based classification, based on the combination of optical and SAR data (using the random forest as the classifier). The results show that SNIC superpixel segmentation is a feasible method for high-resolution crop mapping based on multi-source remote sensing data. The automatic selection of the optimal superpixel size of SNIC will be focused on in future research.

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A Fast Region Growing Based Superpixel Segmentation for Hyperspectral Image Classification

Cited by 3 publications

References 19 publications

Superpixel Segmentation of Hyperspectral Images Based on Entropy and Mutual Information

Superpixel Segmentation of Hyperspectral Images Based on Entropy and Mutual Information

Extraction of Spectral-Spatial 3-Dimensional Homogeneous Regions from Hyperspectral Images and Its Application to Fast Classification

High-Resolution Rice Mapping Based on SNIC Segmentation and Multi-Source Remote Sensing Images

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