An experiment-based quantitative and comparative analysis of target detection and image classification algorithms for hyperspectral imagery

Chang, Chein‐I; Ren, Hsuan

doi:10.1109/36.841984

Cited by 103 publications

(6 citation statements)

References 28 publications

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“…The research employed an experiment-based quantitative approach, drawing on simulation and implementation methods as outlined in references [30], [31]. The study's focus was on constructing a decision support model that identifies distinct LBP types through ML, utilizing data primarily derived from secondary sources related to LBP patients.…”

Section: Methodsmentioning

confidence: 99%

Implementing decision support tool for low-back pain diagnosis and prediction based on the range of motions

Gambo,

Mbada,

Aina

et al. 2024

IJEECS

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Low-back pain (LBP) is a complex health problem requiring accurate diagnosis and effective treatment. However, the current decision support system (DSS) for LBP only considers the patient’s pain intensity and treatment suitability, which may not lead to optimal outcomes. This paper proposes a novel DSS that combines machine learning (ML) and expert input to classify LBP types and provide more reliable and personalized recommendations. We used an open-source dataset to train and test various ML models, including an ensemble model that combines multiple classifiers. We also performed data analysis and feature extraction to enhance the model’s predictive power. We developed a prototype tool to demonstrate the model’s performance and usability. Our results show that the ensemble model achieved the highest accuracy of 92.02%, followed by random forest (RF) (91.01%), multilayer perceptron (MP) (91.01%), and support vector machine (SVM) (87.88%). Our findings suggest that ML can help LBP specialists diagnose and treat LBP more effectively by learning from historical data and predicting LBP categories. Our DSS can potentially improve the quality of life for LBP patients and reduce the burden on the healthcare system.

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Section: Methodsmentioning

confidence: 99%

Implementing decision support tool for low-back pain diagnosis and prediction based on the range of motions

Gambo,

Mbada,

Aina

et al. 2024

IJEECS

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“…In the field of computer vision, image classification and detection are the focus for research [13,14]. An image classification model is used to divide each image into a single category, usually corresponding to the most prominent object in the image [15][16][17]; but in the real world, in general, images do not only contain one object, so it is not accurate to simply assign images to a single label.…”

Section: Methodsmentioning

confidence: 99%

Automatic Identification of Overpass Structures: A Method of Deep Learning

Huang

2019

IJGI

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The identification of overpass structures in road networks has great significance for multi-scale modeling of roads, congestion analysis, and vehicle navigation. The traditional vector-based methods identify overpasses by the methodologies coming from computational geometry and graph theory, and they overly rely on the artificially designed features and have poor adaptability to complex scenes. This paper presents a novel method of identifying overpasses based on a target detection model (Faster-RCNN). This method utilizes raster representation of vector data and convolutional neural networks (CNNs) to learn task adaptive features from raster data, then identifies the location of an overpass by a Region Proposal network (RPN). The contribution of this paper is: (1) An overpass labelling geodatabase (OLGDB) for the OpenStreetMap (OSM) road network data of six typical cities in China is established; (2) Three different CNNs (ZF-net, VGG-16, Inception-ResNet V2) are integrated into Faster-RCNN and evaluated by accuracy performance; (3) The optimal combination of learning rate and batchsize is determined by fine-tuning; and (4) Five geometric metrics (perimeter, area, squareness, circularity, and W/L) are synthetized into image bands to enhance the training data, and their contribution to the overpass identification task is determined. The experimental results have shown that the proposed method has good accuracy performance (around 90%), and could be improved with the expansion of OLGDB and switching to more sophisticated target detection models. The deep learning target detection model has great application potential in large-scale road network pattern recognition, it can task-adaptively learn road structure features and easily extend to other road network patterns.

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“…To verify the efficacy of the proposed method, few traditional feature extraction methods were considered for comparison. Widely studied methods such as SVM, 34 SVM-PCA, 9 ICDA, 37 and LDA 38 were compared. The 3-D DWT, a transform-based feature extraction method, is also considered.…”

Section: Support Vector Machine Classifiermentioning

confidence: 99%

Three-dimensional discrete cosine transform-based feature extraction for hyperspectral image classification

Manoharan

Sawant

Samiappan

et al. 2018

J. Appl. Rem. Sens.

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The hyperspectral remote sensor acquires hundreds of contiguous spectral images, resulting in large data that contain a significant amount of redundant information. This highdimensional and redundant data always influence the efficiency of the data processing. Therefore, feature extraction becomes one of the critical tasks in hyperspectral image classification. A transform-domain-based feature extraction technique, three-dimensional discrete cosine transform (3-D DCT), is proposed. The reason behind the transform domains is that, generally, an invertible linear transform reconstructs the image data to provide the independent information about the spectra or more separable transformation coefficients. Moreover, DCT has excellent energy compaction properties for highly correlated images, such as hyperspectral images, which reduces the complexity of the separation significantly. Unlike the discrete wavelet transform that requires sequential transform to obtain the approximation and detailed coefficients, DCT extracts all coefficients simultaneously. As a result, computation time in the feature extraction can be reduced. The experimental results on three benchmark datasets (Indian Pines, Pavia University, and Salinas) show that the proposed approach produces a good classification in terms of overall accuracy, average accuracy as well as Cohen's kappa coefficient (κ) when compared with some traditional as well as transform-based feature extraction algorithms. Experimental result also shows that the proposed method requires less computational time than the transform-based feature extraction method.

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An experiment-based quantitative and comparative analysis of target detection and image classification algorithms for hyperspectral imagery

Cited by 103 publications

References 28 publications

Implementing decision support tool for low-back pain diagnosis and prediction based on the range of motions

Implementing decision support tool for low-back pain diagnosis and prediction based on the range of motions

Automatic Identification of Overpass Structures: A Method of Deep Learning

Three-dimensional discrete cosine transform-based feature extraction for hyperspectral image classification

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