Ensemble EMD-Based Spectral-Spatial Feature Extraction for Hyperspectral Image Classification

Li, Qianming; Zheng, Bohong; Tu, Bing; Wang, Jinping; Zhou, Chengle

doi:10.1109/jstars.2020.3018710

Cited by 26 publications

(8 citation statements)

References 61 publications

(75 reference statements)

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“…The convolutional layer and residual module still play an important role in hyperspectral classification 60 – 64 . In addition, support vector machines 65 , self-learning 66 , multi-view feature 67 are used to obtain discriminative features to perform hyperspectral classification. Some methods based on RNN and LSTM are also widely used in the field of histopathology 68 – 70 .…”

Section: Related Workmentioning

confidence: 99%

Asymmetric coordinate attention spectral-spatial feature fusion network for hyperspectral image classification

Cheng

Wang

2021

Sci Rep

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In recent years, the hyperspectral classification algorithm based on deep learning has received widespread attention, but the existing network models have higher model complexity and require more time consumption. In order to further improve the accuracy of hyperspectral image classification and reduce model complexity, this paper proposes an asymmetric coordinate attention spectral-spatial feature fusion network (ACAS2F2N) to capture distinguishing hyperspectral features. Specifically, adaptive asymmetric iterative attention was proposed to obtain the discriminative spectral-spatial features. Different from the common feature fusion method, this feature fusion method can adapt to most skip connection tasks. In addition, there is no manual parameter setting. Coordinate attention is used to obtain accurate coordinate information and channel relationship. The strip pooling module was introduced to increase the network’s receptive field and avoid irrelevant information brought by conventional convolution kernels. The proposed algorithm is tested on the mainstream hyperspectral datasets (IP, KSC, and Botswana), experimental results show that the proposed ACAS2F2N can achieve state-of-the-art performance with lower time complexity.

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Section: Related Workmentioning

confidence: 99%

Asymmetric coordinate attention spectral-spatial feature fusion network for hyperspectral image classification

Cheng

Wang

2021

Sci Rep

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“…This model produced relatively high classification accuracy. Another successful approach for classification of HSI is to use ensemble techniques and some techniques have been proposed to extract features by varying the spatial dimension of the pixel patch using different CNN models and then combining all the extracted features to perform classification [18]- [20]. For HSI classification, a multi-scale three-dimensional convolutional neural network (M3DCNN) has been proposed that extracts multi-scale spatial features and spectral features from HSI [21].…”

Section: Introductionmentioning

confidence: 99%

Multi-scale 3D-convolutional neural network for hyperspectral image classification

Kanthi

Sarma²,

Bindu

2022

IJEECS

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Deep Learning methods are state-of-the-art approaches for pixel-based hyperspectral images (HSI) classification. High classification accuracy has been achieved by extracting deep features from both spatial-spectral channels. However, the efficiency of such spatial-spectral approaches depends on the spatial dimension of each patch and there is no theoretically valid approach to find the optimum spatial dimension to be considered. It is more valid to extract spatial features by considering varying neighborhood scales in spatial dimensions. In this regard, this article proposes a deep convolutional neural network (CNN) model wherein three different multi-scale spatial-spectral patches are used to extract the features in both the spatial and spectral channels. In order to extract these potential features, the proposed deep learning architecture takes three patches various scales in spatial dimension. 3D convolution is performed on each selected patch and the process runs through entire image. The proposed is named as multi-scale three-dimensional convolutional neural network (MS-3DCNN). The efficiency of the proposed model is being verified through the experimental studies on three publicly available benchmark datasets including Pavia University, Indian Pines, and Salinas. It is empirically proved that the classification accuracy of the proposed model is improved when compared with the remaining state-of-the-art methods.

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“…W ITH the advance of spectroscopy sensor technology, hyperspectral image (HSI) with high spectral dimensionality and spatial resolution has been constantly becoming more available. Considering the abundance of spatial and spectral information, numerous classification algorithms exploiting remote sensing images have played a primary role in a variety of applications, such as precision agriculture [1], [2], environmental monitoring [3], land cover [4], and urban expansion [5].…”

Section: Introductionmentioning

confidence: 99%

Spectral-Spatial Active Learning With Structure Density for Hyperspectral Classification

Zheng

Yang

2021

IEEE Access

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In this paper, a spectral-spatial active learning (AL) method is proposed based on an upto-date unlabeled samples sampling strategy concentrated on the structure density supported by breaking ties. The proposed sampling criterion in AL is used for hyperspectral image classification, which involves several steps: First, superpixel segmentation algorithm is conducted on the HSI to cluster pixels with similar spectral-spatial signature into the same superpixel block. Then, density peak clustering technique is performed on the each superpixel block to obtain structure density of the pixels. Meanwhile, probabilitybased classifier is employed to achieve the probability distributions of pixel. Next, breaking ties (BT) score of each pixel can be calculated by exploiting the probabilities. Additionally, a fusion mechanism is introduced to select the unlabeled samples with representativeness and informativeness advantages by employing the BT-assisted structure density(SD sampling criterion) of each pixel. Finally, the samples with manual labeled class labels are put into the training set to retrain the classifier. Experimental results manifest that the proposed SD-based sampling criterion in active learning can significantly improve the classification accuracy in few labor costs. Thus, it has certain feasibility in practical application.INDEX TERMS Hyperspectral imagery, active learning, structure density, breaking ties, sampling criterion.

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Ensemble EMD-Based Spectral-Spatial Feature Extraction for Hyperspectral Image Classification

Cited by 26 publications

References 61 publications

Asymmetric coordinate attention spectral-spatial feature fusion network for hyperspectral image classification

Asymmetric coordinate attention spectral-spatial feature fusion network for hyperspectral image classification

Multi-scale 3D-convolutional neural network for hyperspectral image classification

Spectral-Spatial Active Learning With Structure Density for Hyperspectral Classification

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