Approches and challenges in classification for hyperspectral data: A review

Kishore, M; Kulkarni, S.B.

doi:10.1109/iceeot.2016.7755339

Cited by 9 publications

(5 citation statements)

References 13 publications

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“…In our proposed MFSuDF framework, most of the parameters have been set by default. Concretely, the KPCA dimension can be decided by the 99% energy contained in the principal components, while the scale and orientation parameters of Gabor wavelets have been presented in (16) and (19).…”

Section: Parameter Settingsmentioning

confidence: 99%

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Multiple Feature-Based Superpixel-Level Decision Fusion for Hyperspectral and LiDAR Data Classification

Jia

Zhan

Zhang

et al. 2021

IEEE Trans. Geosci. Remote Sensing

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Section: Parameter Settingsmentioning

confidence: 99%

“…However, the rich details contained in both the spatial and spectral domains of HSIs not only bring opportunities to improve material classification accuracies but also pose a series of challenges in this field [15], [16]. On the one hand, raw HSIs have a large amount of redundant and correlated spectral information.…”

Section: Introductionmentioning

confidence: 99%

Multiple Feature-Based Superpixel-Level Decision Fusion for Hyperspectral and LiDAR Data Classification

Jia

Zhan

Zhang

et al. 2021

IEEE Trans. Geosci. Remote Sensing

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“…HSIs have hundreds or thousands of narrow spectral bands, covering the spectral region from the visible to the infrared field [3]. In particular, HSIs have both spatial and spectral smoothness, which not only produces detailed and accurate descriptions of objects but also results in a high correlation between adjacent bands [4][5][6]. Based on the above reasons, some obstacles and challenges exist regarding the interpretation of HSI information.…”

Section: Introductionmentioning

confidence: 99%

Shearlet-Based Structure-Aware Filtering for Hyperspectral and LiDAR Data Classification

Jia

Zhan

2021

J Remote Sens

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The joint interpretation of hyperspectral images (HSIs) and light detection and ranging (LiDAR) data has developed rapidly in recent years due to continuously evolving image processing technology. Nowadays, most feature extraction methods are carried out by convolving the raw data with fixed-size filters, whereas the structural and texture information of objects in multiple scales cannot be sufficiently exploited. In this article, a shearlet-based structure-aware filtering approach, abbreviated as ShearSAF, is proposed for HSI and LiDAR feature extraction and classification. Specifically, superpixel-guided kernel principal component analysis (KPCA) is firstly adopted on raw HSIs to reduce the dimensions. Then, the KPCA-reduced HSI and LiDAR data are converted to the shearlet domain for texture and area feature extraction. In contrast, superpixel segmentation algorithm utilizes the raw HSI data to obtain the initial oversegmentation map. Subsequently, by utilizing a well-designed minimum merging cost that fully considers spectral (HSI and LiDAR data), texture, and area features, a region merging procedure is gradually conducted to produce a final merging map. Further, a scale map that locally indicates the filter size is achieved by calculating the edge distance. Finally, the KPCA-reduced HSI and LiDAR data are convolved with the locally adaptive filters for feature extraction, and a random forest (RF) classifier is thus adopted for classification. The effectiveness of our ShearSAF approach is verified on three real-world datasets, and the results show that the performance of ShearSAF can achieve an accuracy higher than that of comparison methods when exploiting small-size training sample problems. The codes of this work will be available at http://jiasen.tech/papers/ for the sake of reproducibility.

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“…Hyperspectral imaging is one of the potential solutions, but it has difficulties in data storage and transmission due to the large data volume. Moreover, the imaging equipment is also too expensive to upgrade [8]. By contrast, near-infrared (NIR) spectroscopy is a viable option.…”

Section: Introductionmentioning

confidence: 99%

Rapid and Nondestructive On-Site Classification Method for Consumer-Grade Plastics Based on Portable NIR Spectrometer and Machine Learning

Yang

Zhang

Yin

et al. 2020

Journal of Spectroscopy

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The classification of plastic waste before recycling is of great significance to achieve effective recycling. In order to achieve rapid, nondestructive, and on-site detection, a portable near-infrared spectrometer was used in this study to obtain the diffuse reflectance spectrum for both standard and commercial plastics made by ABS, PC, PE, PET, PP, PS, and PVC. After applying a series of pretreatments, the principal component analysis (PCA) was used to analyze the cluster trend. K-nearest neighbor (KNN), support vector machine (SVM), and back propagation neural network (BPNN) classification models were developed and evaluated, respectively. The result showed that different plastics could be well separated in top three principal components space after pretreatment, and the classification models performed excellent classification results and high generalization capability. This study indicated that the portable NIR spectrometer, integrated with chemometrics, could achieve excellent performance and has great potential in the field of commercial plastic identification.

show abstract

Approches and challenges in classification for hyperspectral data: A review

Cited by 9 publications

References 13 publications

Multiple Feature-Based Superpixel-Level Decision Fusion for Hyperspectral and LiDAR Data Classification

Multiple Feature-Based Superpixel-Level Decision Fusion for Hyperspectral and LiDAR Data Classification

Shearlet-Based Structure-Aware Filtering for Hyperspectral and LiDAR Data Classification

Rapid and Nondestructive On-Site Classification Method for Consumer-Grade Plastics Based on Portable NIR Spectrometer and Machine Learning

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