Hyperspectral Image Classification

Gogineni, Rajesh; Chaturvedi, Ashvini

doi:10.5772/intechopen.88925

Cited by 11 publications

(4 citation statements)

References 80 publications

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“…We employed tensor decomposition to solve the problem of the presence of mixed pixels in the hyperspectral dataset [ 31 ]. In a hyperspectral image, there are pure pixels but also mixed pixels.…”

Section: Methodsmentioning

confidence: 99%

Efficient Unsupervised Classification of Hyperspectral Images Using Voronoi Diagrams and Strong Patterns

Bilius

Pentiuc

2020

Sensors

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Hyperspectral images (HSIs) are a powerful tool to classify the elements from an area of interest by their spectral signature. In this paper, we propose an efficient method to classify hyperspectral data using Voronoi diagrams and strong patterns in the absence of ground truth. HSI processing consumes a great deal of computing resources because HSIs are represented by large amounts of data. We propose a heuristic method that starts by applying Parafac decomposition for reduction and to construct the abundances matrix. Furthermore, the representative nodes from the abundances map are searched for. A multi-partition of these nodes is found, and based on this, strong patterns are obtained. Then, based on the hierarchical clustering of strong patterns, an optimum partition is found. After strong patterns are labeled, we construct the Voronoi diagram to extend the classification to the entire HSI.

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Section: Methodsmentioning

confidence: 99%

Efficient Unsupervised Classification of Hyperspectral Images Using Voronoi Diagrams and Strong Patterns

Bilius

Pentiuc

2020

Sensors

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“…The recent year witnessed hyper-spectral image (HSI) classification in the military, irrigation, mining, and route detection [1], [2], as shown in Fig 1 . Most machine learning ML algorithms such as K-nearest neighbor [3], support vector machine [4], Bayesian classifier [5], kernel-based method [6], and regression model [7] have been used for HSI classification [8], [9]. The major drawback of this ML is the manual extraction of features, which is time-consuming.…”

Section: Introductionmentioning

confidence: 99%

Deep Feature Segmentation Model Driven by Hybrid Convolution Network for Hyper Spectral Image Classification

K Ghotekar,

Shaw,

Rout

2024

IJCDS

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Hyper-spectral image (HSI) classification can support different applications, such as agriculture, military, city planning, land utilization, and identifying distinct regions. It is treated as a crucial topic in the research community. Recent advancements in convolution neural networks (CNN) have shown the unique capability of extracting meaningful features and classification. However, CNN works with square images with fixed dimensions and cannot extract local information of images having distinct geometric variations with context and content relationships; hence, there is a scope for improvement in correctly identifying class boundaries. Encouraged by the facts, we propose an HSI feature segmentation model by the hybrid convolution network (GCNN-RESNET152) for the HSI classification. First, pre-trained CNN on ImageNet is used to obtain the multi-layer feature. Second, the 3D discrete wavelet transform image is fed into the graph convolution network GCN model to gain patch-to-patch correlations feature maps. Finally, the features are integrated using the concatenation method of the three-weighted coefficients. Finally, the linear classifier is used to predict the semantic classes of pixel HSI. The proposed model is tested on four benchmark datasets: Houston University (HU), Indian Pines (IP), Kennedy Space Station (KSS), and Pavia University (PU). The result is compared with state-of-art algorithms and is superior in terms of overall, average, and kappa accuracy.

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“…Some of the most prominent methods are maximum likelihood (ML), nearest neighbor classifier, decision trees, random forest, support vector machines (SVM), etc. [8]. K-nearest neighbor (kNN) was the most widely used simple HSI classifier at that time.…”

Section: Introductionmentioning

confidence: 99%

Spectral Swin Transformer Network for Hyperspectral Image Classification

Liu

Zhang

et al. 2023

Remote Sensing

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Hyperspectral images are complex images that contain more spectral dimension information than ordinary images. An increasing number of HSI classification methods are using deep learning techniques to process three-dimensional data. The Vision Transformer model is gradually occupying an important position in the field of computer vision and is being used to replace the CNN structure of the network. However, it is still in the preliminary research stage in the field of HSI. In this paper, we propose using a spectral Swin Transformer network for HSI classification, providing a new approach for the HSI field. The Swin Transformer uses group attention to enhance feature representation, and the sliding window attention calculation can take into account the contextual information of different windows, which can retain the global features of HSI and improve classification results. In our experiments, we evaluated our proposed approach on several public hyperspectral datasets and compared it with several methods. The experimental results demonstrate that our proposed model achieved test accuracies of 97.46%, 99.7%, and 99.8% on the IP, SA, and PU public HSI datasets, respectively, when using the AdamW optimizer. Our approach also shows good generalization ability when applied to new datasets. Overall, our proposed approach represents a promising direction for hyperspectral image classification using deep learning techniques.

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Hyperspectral Image Classification

Cited by 11 publications

References 80 publications

Efficient Unsupervised Classification of Hyperspectral Images Using Voronoi Diagrams and Strong Patterns

Efficient Unsupervised Classification of Hyperspectral Images Using Voronoi Diagrams and Strong Patterns

Deep Feature Segmentation Model Driven by Hybrid Convolution Network for Hyper Spectral Image Classification

Spectral Swin Transformer Network for Hyperspectral Image Classification

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