Gravitation-Based Edge Detection in Hyperspectral Images

Sun, Guifan; Zhang, Aizhu; Ren, Jinchang; Ma, Jingsheng; Peng, Wang; Zhang, Yuanzhi; Jia, Xiuping

doi:10.3390/rs9060592

Cited by 25 publications

(10 citation statements)

References 54 publications

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“…Sun et al [19] have used six hyperspectral images, consisting of artificial images and real natural scene images, for their evaluation. The artificial images were generated using This work is licensed under a Creative Commons Attribution 4.0 License.…”

Section: ) Visual Inspectionmentioning

confidence: 99%

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Quality Assessment Methods to Evaluate the Performance of Edge Detection Algorithms for Digital Image: A Systematic Literature Review

Tariq

Hamzah

et al. 2021

IEEE Access

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A segmentation process is usually required in order to analyze an image. One of the available segmentation approaches is by detecting the edges on the image. Up to now, there are many edge detection algorithms that researchers have proposed. Thus, the purpose of this systematic literature review is to investigate the available quality assessment methods that researchers have utilized to evaluate the performance of the edge detection algorithms. Due to the vast number of available literature in this area, we limit our search to only open-access publications. A systematic search in five publisher websites (i.e., IEEExplore, IET digital library, Wiley, MDPI, and Hindawi) and Scopus database was carried out to gather resources that are related to the edge detection algorithms. Seventy-three publications that are about developing or comparing edge detection algorithms have been chosen. From these publication samples, we have identified 17 quality assessment methods used by researchers. Among the popular quality assessment methods are visual inspection, processing time, confusion-matrix based measures, mean square error (MSE)-based measures, and figure of merit (FOM). This survey also indicates that although most of the researchers only use a small number of test images (i.e., less than 10 test images), there are available datasets with a larger number of images for digital image segmentation that researchers can utilize.INDEX TERMS Digital image processing, edge detection algorithm, image segmentation, assessment, validation, quality measures, reviews.

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Section: ) Visual Inspectionmentioning

confidence: 99%

“…Sun et al [19] have used F-measure as their quantitative evaluation. Their definition of F-measure (F α ) is: 11) with α ∈ [0, 1] is the weighting parameter.…”

Section: ) Confusion Matrix-based Measuresmentioning

confidence: 99%

Quality Assessment Methods to Evaluate the Performance of Edge Detection Algorithms for Digital Image: A Systematic Literature Review

Tariq

Hamzah

et al. 2021

IEEE Access

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“…As a result, in this paper, we employ the metrics of precision and recall. Regarding object segmentation, evaluation measures can be categorized into three types: area-based measures, location-based measures, and combined measures [37][38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

Conceptual Cognitive Modeling for Fine-Grained Annotation Quality Assessment of Object Detection Datasets

Guo

Xie

et al. 2020

Discrete Dynamics in Nature and Society

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In many supervised computer vision tasks such as object detection, manual annotation crowdsourcing platforms are widely used for acquiring large-scale labeled data. However, the annotation quality may suffer low quality that can severely affect the training of models. As a result, the evaluation of the annotations within the dataset is critical, yet it has seldom been addressed in object detection. In this paper, we present a fine-grained annotation quality assessment (FGAQA) framework for evaluating the quality of object detection datasets. First, we formulate a generic annotation quality assessment framework based on the core generalpurpose data quality dimensions, using the bounding box and the label. Second, cognition theory in terms of hierarchy and continuity is utilized to refine the basic framework, including the consistency of the bounding box, completeness of the category, hierarchical accuracy of the label, and the consistency of the label. Comprehensive experiments on the two object detection datasets are used for performance evaluation. It is found that the ground truth annotations of the Urban Traffic Surveillance dataset have more quality issues than the ones of the PASCAL VOC 2007 detection dataset. e proposed FGAQA framework performs an effective fine-grained evaluation of the annotations, which is significant for quality assurance of annotations from crowdsourcing platforms and the subsequent model's training.

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“…With rich spectral information contained in tens or hundreds of spectral bands, hyperspectral images (HSI) has been successfully applied in a wide range of remote sensing applications such as land cover analysis [1][2][3], military surveillance [4,5], object detection [6], and precision agriculture [7][8][9][10][11], etc. Among these applications, image classification is an active topic, which aims to assign each pixel in the HSI into one unique semantic category or class.…”

Section: Introductionmentioning

confidence: 99%

Superpixel based Feature Specific Sparse Representation for Spectral-Spatial Classification of Hyperspectral Images

et al. 2019

Self Cite

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To improve the performance of the sparse representation classification (SRC), we propose a superpixel-based feature specific sparse representation framework (SPFS-SRC) for spectral-spatial classification of hyperspectral images (HSI) at superpixel level. First, the HSI is divided into different spatial regions, each region is shape- and size-adapted and considered as a superpixel. For each superpixel, it contains a number of pixels with similar spectral characteristic. Since the utilization of multiple features in HSI classification has been proved to be an effective strategy, we have generated both spatial and spectral features for each superpixel. By assuming that all the pixels in a superpixel belongs to one certain class, a kernel SRC is introduced to the classification of HSI. In the SRC framework, we have employed a metric learning strategy to exploit the commonalities of different features. Experimental results on two popular HSI datasets have demonstrated the efficacy of our proposed methodology.

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Gravitation-Based Edge Detection in Hyperspectral Images

Cited by 25 publications

References 54 publications

Quality Assessment Methods to Evaluate the Performance of Edge Detection Algorithms for Digital Image: A Systematic Literature Review

Quality Assessment Methods to Evaluate the Performance of Edge Detection Algorithms for Digital Image: A Systematic Literature Review

Conceptual Cognitive Modeling for Fine-Grained Annotation Quality Assessment of Object Detection Datasets

Superpixel based Feature Specific Sparse Representation for Spectral-Spatial Classification of Hyperspectral Images

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