Search

In recent years, deep learning methods have been widely used for the classification of hyperspectral images. However, their limited availability under the condition of small samples remains a serious issue. Moreover, the current mainstream approaches based on convolutional neural networks do well in local feature extraction but are also restricted by its limited receptive field. Hence, these models are unable to capture long-distance dependencies both on spatial and spectral dimension. To address above issues, this paper proposes a global-local U-Net augmented by transformers (TrmGLU-Net). First, whole hyperspectral images are input to the model for end-to-end training to capture the contextual information. Then, a transformer-augmented U-Net is designed with alternating transformers and convolutional layers to perceive both global and local information. Finally, a superpixel-based label expansion method is proposed to expand the labels and improve the performance under the condition of small samples. Extensive experiments on four hyperspectral scenes demonstrate that TrmGLU-Net has better performance than other advanced patch-level and image-level methods with limited training samples. The relevant code will be opened at [ABSTRACT FROM AUTHOR]

In recent years, the wide use of deep learning based methods has greatly improved the classification performance of hyperspectral image (HSI). As an effective method to improve the performance of deep convolution networks, attention mechanism is also widely used for HSI classification tasks. However, the majority of the existing attention mechanisms for HSI classification are based on the convolution layer, and the classification accuracy still has margins for improvement. Motivated by the latest self attention mechanism in natural language processing, a deep transformer is proposed for HSI classification in this paper. Specifically, deep transformer along the spectral dimension and the spatial dimension are explored respectively. Then, a deep spatial-spectral transformer (DSS-TRM) is proposed to improve the classification performance of HSI. The contribution of this paper is to make full use of self attention mechanism, that is to use transformer layer instead of convolution layer. More importantly, a DSS-TRM is proposed to realize end-to-end HSI classification. Extensive experiments are conducted on three HSI data sets. The experimental results demonstrates that the proposed DSS-TRM could outperform the traditional convolutional neural networks and attention based methods. [ABSTRACT FROM AUTHOR]

Classification with a few labeled samples has always been a longstanding problem in the field of hyperspectral image (HSI) processing and analysis. Aiming at the small sample characteristics of HSI classification, a novel ensemble self-supervised feature-learning (ES 2 FL) method is proposed in this paper. The proposed method can automatically learn deep features conducive to classification without any annotation information, significantly reducing the dependence of deep-learning models on massive labeled samples. Firstly, to utilize the spatial–spectral information in HSIs more fully and effectively, EfficientNet-B0 is introduced and used as the backbone to model input samples. Then, through constraining the cross-correlation matrix of different distortions of the same sample to the identity matrix, the designed model can extract the latent features of homogeneous samples gathering together and heterogeneous samples separating from each other in a self-supervised manner. In addition, two ensemble learning strategies, feature-level and view-level ensemble, are proposed to further improve the feature-learning ability and classification performance by jointly utilizing spatial contextual information at different scales and feature information at different bands. Finally, the concatenations of the learned features and the original spectral vectors are inputted into classifiers such as random forest or support vector machine to complete label prediction. Extensive experiments on three widely used HSI data sets show that the proposed ES 2 FL method can learn more discriminant deep features and achieve better classification performance than existing advanced methods in the case of small samples. [ABSTRACT FROM AUTHOR]

Deep learning based methods have recently been successfully explored in hyperspectral image classification field. However, training a deep learning model still requires a large number of labeled samples, which is usually impractical in hyperspectral images. In this paper, a simple but effective feature extraction method is proposed for hyperspectral image classification. Specifically, a pretrained deep convolutional neural network based on the ImageNet dataset is used to extract spatial features of a hyperspectral image. Recently, it is easy to obtain a pretrained convolutional neural network on the Internet. Note that the pretrained models are trained by using the ImageNet dataset. This means that the proposed method does not need labeled hyperspectral samples to train the deep model. Therefore, the proposed method alleviates the problem of lacking labeled samples and avoids the artificial design of feature extraction rules. Finally, the extracted features are stacked with spectral features as the input of a support vector machine classifier. The proposed method is conducted on three widely used hyperspectral image datasets. The experimental results demonstrate that the proposed method could outperform the conventional feature extraction methods and deep learning based methods. [ABSTRACT FROM AUTHOR]

Hyperspectral image classification has become a research focus in recent literature. However, well-designed features are still open issues that impact on the performance of classifiers. In this paper, a novel supervised deep feature extraction method based on siamese convolutional neural network (S-CNN) is proposed to improve the performance of hyperspectral image classification. First, a CNN with five layers is designed to directly extract deep features from hyperspectral cube, where the CNN can be intended as a nonlinear transformation function. Then, the siamese network composed by two CNNs is trained to learn features that show a low intraclass and high interclass variability. The important characteristic of the presented approach is that the S-CNN is supervised with a margin ranking loss function, which can extract more discriminative features for classification tasks. To demonstrate the effectiveness of the proposed feature extraction method, the features extracted from three widely used hyperspectral data sets are fed into a linear support vector machine (SVM) classifier. The experimental results demonstrate that the proposed feature extraction method in conjunction with a linear SVM classifier can obtain better classification performance than that of the conventional methods. [ABSTRACT FROM AUTHOR]