Learning Hyperspectral Feature Extraction and Classification with ResNeXt Network

Nyasaka, Divinah; Wang, Jing; Tinega, Haron Chweya

doi:10.48550/arxiv.2002.02585

Cited by 2 publications

(2 citation statements)

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“…To compare the classification results of the proposed model, comparative experiments were conducted and the models used were SVM [37], 2D-CNN [38], 3D-CNN [39], Hybrid [22], DFFN [41], Bam-CM [42], ViT [26], SwinT [33], SSFTT [30], and CT Mixer [31].…”

Section: Resultsmentioning

confidence: 99%

Multiscale Feature-Learning with a Unified Model for Hyperspectral Image Classification

Arshad,

Zhang,

Ullah

et al. 2023

Sensors

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In the realm of hyperspectral image classification, the pursuit of heightened accuracy and comprehensive feature extraction has led to the formulation of an advance architectural paradigm. This study proposed a model encapsulated within the framework of a unified model, which synergistically leverages the capabilities of three distinct branches: the swin transformer, convolutional neural network, and encoder–decoder. The main objective was to facilitate multiscale feature learning, a pivotal facet in hyperspectral image classification, with each branch specializing in unique facets of multiscale feature extraction. The swin transformer, recognized for its competence in distilling long-range dependencies, captures structural features across different scales; simultaneously, convolutional neural networks undertake localized feature extraction, engendering nuanced spatial information preservation. The encoder–decoder branch undertakes comprehensive analysis and reconstruction, fostering the assimilation of both multiscale spectral and spatial intricacies. To evaluate our approach, we conducted experiments on publicly available datasets and compared the results with state-of-the-art methods. Our proposed model obtains the best classification result compared to others. Specifically, overall accuracies of 96.87%, 98.48%, and 98.62% were obtained on the Xuzhou, Salinas, and LK datasets.

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Section: Resultsmentioning

confidence: 99%

Multiscale Feature-Learning with a Unified Model for Hyperspectral Image Classification

Arshad,

Zhang,

Ullah

et al. 2023

Sensors

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“…Clustering-based techniques do not require prior knowledge and they are commonly used for HSI unsupervised classification, but still face challenges due to high spectral resolution and the presence of complex spatial structures [7]. The optimal selection of spectral bands is one of the major tasks for the HSI classification and feature extraction (FE) is often used as a pre-processing step for HSI classification methods to address the high spectral resolution problem [8]. FE techniques for HSI analysis can be either supervised, such as embedded feature selection with support vector machines (EFS-SVM) [9], linear discriminant analysis (LDA) [10], or unsupervised, including principal component analysis (PCA) [11], mainfold learning [12], and maximum noise factoring (MNF) [13].…”

Section: Introductionmentioning

confidence: 99%

Segment-Based Clustering of Hyperspectral Images Using Tree-Based Data Partitioning Structures

Ismail

Orlandić

2020

Algorithms

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Hyperspectral image classification has been increasingly used in the field of remote sensing. In this study, a new clustering framework for large-scale hyperspectral image (HSI) classification is proposed. The proposed four-step classification scheme explores how to effectively use the global spectral information and local spatial structure of hyperspectral data for HSI classification. Initially, multidimensional Watershed is used for pre-segmentation. Region-based hierarchical hyperspectral image segmentation is based on the construction of Binary partition trees (BPT). Each segmented region is modeled while using first-order parametric modelling, which is then followed by a region merging stage using HSI regional spectral properties in order to obtain a BPT representation. The tree is then pruned to obtain a more compact representation. In addition, principal component analysis (PCA) is utilized for HSI feature extraction, so that the extracted features are further incorporated into the BPT. Finally, an efficient variant of k-means clustering algorithm, called filtering algorithm, is deployed on the created BPT structure, producing the final cluster map. The proposed method is tested over eight publicly available hyperspectral scenes with ground truth data and it is further compared with other clustering frameworks. The extensive experimental analysis demonstrates the efficacy of the proposed method.

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Learning Hyperspectral Feature Extraction and Classification with ResNeXt Network

Cited by 2 publications

References 0 publications

Multiscale Feature-Learning with a Unified Model for Hyperspectral Image Classification

Multiscale Feature-Learning with a Unified Model for Hyperspectral Image Classification

Segment-Based Clustering of Hyperspectral Images Using Tree-Based Data Partitioning Structures

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