Semisupervised Hyperspectral Image Classification Using Spatial-Spectral Information and Landscape Features

Ji, Xiaowei; Cui, Ying; Wang, Heng; Li, Teng; Wang, Lingxiu; Wang, Liguo

doi:10.1109/access.2019.2946220

Cited by 15 publications

(9 citation statements)

References 49 publications

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“…More non-diagonal neighbors are present for each hexagon rather than a square. Also, hexagonal grids generate less distance distortion of boundary pixels [77].…”

Section: B Position Of Superpixel Seed Pointsmentioning

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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“…More non-diagonal neighbors are present for each hexagon rather than a square. Also, hexagonal grids generate less distance distortion of boundary pixels [77].…”

Section: B Position Of Superpixel Seed Pointsmentioning

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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show abstract

“…The JAFFE dataset contains seven different expressions of ten Japanese women [47]. The ORL dataset [48] The FEI 1, FEI 1, FLOWER, VGDB, and KTH datasets are split using a commonly used proportion, i.e., 75% images for training and 25% for testing [51]. The JAFFE dataset uses 20 images per class for training and the remaining images for testing because it only has about 30 images per class.…”

Section: A Original Datasetsmentioning

confidence: 99%

Genetic Programming-Based Discriminative Feature Learning for Low-Quality Image Classification

Bi¹,

Xue²,

Zhang³

2021

Preprint

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Being able to learn discriminative features from low-quality images has raised much attention recently due to their wide applications ranging from autonomous driving to safety surveillance. However, this task is difficult due to high variations across images, such as scale, rotation, illumination, and viewpoint, and distortions in images, such as blur, low contrast, and noise. Image preprocessing could improve the quality of the images, but it often requires human intervention and domain knowledge. Genetic programming (GP) with a flexible representation can automatically perform image preprocessing and feature extraction without human intervention. Therefore, this study proposes a new evolutionary learning approach using GP (EFLGP) to learn discriminative features from images with blur, low contrast, and noise for classification. In the proposed approach, we develop a new program structure (individual representation), a new function set, and a new terminal set. With these new designs, EFLGP can detect small regions from a large input low-quality image, select image operators to process the regions or detect features from the small regions, and output a flexible number of discriminative features. A set of commonly used image preprocessing operators is employed as functions in EFLGP to allow it to search for solutions that can effectively handle low-quality image data. The performance of EFLGP is comprehensively investigated on eight datasets of varying difficulty under the original (clean), blur, low contrast, and noise scenarios, and compared with a large number of benchmark methods using handcrafted features and deep features. The experimental results show that EFLGP achieves significantly better or similar results in most comparisons. The results also reveal that EFLGP is more invariant than the benchmark methods to blur, low contrast, and noise.

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“…Several studies based on self-training are related to our work. For example, the authors in [64] utilized simple linear iterative cluster segmentation method to extract spatial information, and multiple classifiers were assembled to find the most confident pseudo-labeled samples. Of particular interest, [65] used the cluster results based on deep features and classification results based on the output of deep model, to determine whether to select the confident samples or not.…”

Section: Weakly Supervised Learning-based Hsi Classificationmentioning

confidence: 99%

Weakly Supervised Classification of Hyperspectral Image Based on Complementary Learning

Huang

Chen

2021

Remote Sensing

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In recent years, supervised learning-based methods have achieved excellent performance for hyperspectral image (HSI) classification. However, the collection of training samples with labels is not only costly but also time-consuming. This fact usually causes the existence of weak supervision, including incorrect supervision where mislabeled samples exist and incomplete supervision where unlabeled samples exist. Focusing on the inaccurate supervision and incomplete supervision, the weakly supervised classification of HSI is investigated in this paper. For inaccurate supervision, complementary learning (CL) is firstly introduced for HSI classification. Then, a new method, which is based on selective CL and convolutional neural network (SeCL-CNN), is proposed for classification with noisy labels. For incomplete supervision, a data augmentation-based method, which combines mixup and Pseudo-Label (Mix-PL) is proposed. And then, a classification method, which combines Mix-PL and CL (Mix-PL-CL), is designed aiming at better semi-supervised classification capacity of HSI. The proposed weakly supervised methods are evaluated on three widely-used hyperspectral datasets (i.e., Indian Pines, Houston, and Salinas datasets). The obtained results reveal that the proposed methods provide competitive results compared to the state-of-the-art methods. For inaccurate supervision, the proposed SeCL-CNN has outperformed the state-of-the-art method (i.e., SSDP-CNN) by 0.92%, 1.84%, and 1.75% in terms of OA on the three datasets, when the noise ratio is 30%. And for incomplete supervision, the proposed Mix-PL-CL has outperformed the state-of-the-art method (i.e., AROC-DP) by 1.03%, 0.70%, and 0.82% in terms of OA on the three datasets, with 25 training samples per class.

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Semisupervised Hyperspectral Image Classification Using Spatial-Spectral Information and Landscape Features

Cited by 15 publications

References 49 publications

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

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

Genetic Programming-Based Discriminative Feature Learning for Low-Quality Image Classification

Weakly Supervised Classification of Hyperspectral Image Based on Complementary Learning

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