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Recently, deep convolutional neural networks (CNNs) have achieved a high classification accuracy of hyperspectral images (HSIs). However, high accuracy is not the only goal of a good HSI classifier. In real-world applications, it is necessary to tell whether the classifier is certain about its classification result, which is critical for safe usage. Unfortunately, most of the existing models do not consider the issue. In this study, uncertainty is estimated and reduced to build a trustworthy HSI classifier. First, since the output probabilities of the softmax layer cannot represent the confidence scores, the distance measurement scheme is used to measure the confidence scores. Then, a trustworthy HSI classifier, which reduces the predictive uncertainty in CNN (i.e., PU-CNN), is obtained by minimizing the distance to the correct centroid. Second, the fact that a training sample of HSI usually contains many pixel vectors that belong to different classes brings label uncertainty. Then, label uncertainty CNN (i.e., LU-CNN), which uses a classifier-consistent estimator to recover the multiple classes in each HSI sample, is proposed. LU-CNN computes loss over candidate label sets to find the optimal classes, which leads to a trustworthy HSI classifier. Finally, the combination of PU-CNN and LU-CNN [i.e., predictive uncertainty and label uncertainty (PL-CNN)] is proposed to address predictive uncertainty and label uncertainty at the same time. Experimental results on the three popular hyperspectral datasets show that the proposed methods yield improvements in both accuracy and confidence. The proposed trustworthy classifier opens a new window for the safe usage of HSI. [ABSTRACT FROM AUTHOR]

In recent years, deep convolutional neural networks (CNNs) have been widely investigated for hyperspectral image (HSI) classification. The CNN-based HSI classifiers obtained good performance under the condition of sufficient training samples. In order to address the problem of limited training samples, in this letter, transfer learning is combined with CNN to address the issue of HSI classification. Pretrained models on large-scale data sets (e.g., ImageNet) can extract the general and discriminative features. Due to the fact that the extracted low-level and mid-level features can be reused for the HSI feature extraction, the CNN-based methods usually obtain good classification performance with insufficient training samples. The ImageNet data set has three channels, while the HSI data set contains hundreds of channels. Therefore, three channels of the HSI are randomly selected to formulate a transferring CNN. Then, several transferring CNNs are combined to establish an ensemble classification system with diversity. Moreover, an improved label smoothing technique is proposed to further improve the classification accuracy of the HSI. Experimental results on two popular hyperspectral data sets [i.e., Indian Pines and Kennedy Space Center (KSC)] show that the transferring CNN ensemble obtains good classification performance compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

In recent years, deep convolutional neural networks (CNNs) have been widely used for hyperspectral image (HSI) classification. The powerful feature extraction capability and high classification performance of CNN highly depend on sufficient training samples. Unfortunately, it is not a common situation because collecting training samples is time-consuming and expensive. In this letter, in order to make the most of deep CNN with limited training samples, dual-path siamese CNN (Dual-SCNN) is proposed for HSI classification. Specifically, the proposed classification framework is a combination of extended morphological profiles, CNN, siamese network, and spectral–spatial feature fusion. In order to solve the problem of insufficiency in hard negative pairs during the training of a siamese network, adversarial training is combined with Dual-SCNN (Dual-SCNN-AT) for HSI classification. Moreover, a data augmentation method titled mixup is combined with Dual-SCNN and Dual-SCNN-AT to further improve the classification performance of HSI. The obtained results on widely used hyperspectral data sets reveal that the proposed methods provide the competitive results in terms of classification accuracy, especially with limited training samples. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (CNNs) have shown their outstanding performance in the hyperspectral image (HSI) classification. The success of CNN-based HSI classification relies on the availability sufficient training samples. However, the collection of training samples is expensive and time consuming. Besides, there are many pretrained models on large-scale data sets, which extract the general and discriminative features. The proper reusage of low-level and midlevel representations will significantly improve the HSI classification accuracy. The large-scale ImageNet data set has three channels, but HSI contains hundreds of channels. Therefore, there are several difficulties to simply adapt the pretrained models for the classification of HSIs. In this article, heterogeneous transfer learning for HSI classification is proposed. First, a mapping layer is used to handle the issue of having different numbers of channels. Then, the model architectures and weights of the CNN trained on the ImageNet data sets are used to initialize the model and weights of the HSI classification network. Finally, a well-designed neural network is used to perform the HSI classification task. Furthermore, attention mechanism is used to adjust the feature maps due to the difference between the heterogeneous data sets. Moreover, controlled random sampling is used as another training sample selection method to test the effectiveness of the proposed methods. Experimental results on four popular hyperspectral data sets with two training sample selection strategies show that the transferred CNN obtains better classification accuracy than that of state-of-the-art methods. In addition, the idea of heterogeneous transfer learning may open a new window for further research. [ABSTRACT FROM AUTHOR]

Deep learning-based methods, especially deep convolutional neural networks (CNNs), have shown their effectiveness for hyperspectral image (HSI) classification. In previous deep CNN-based HSI classification methods, a cuboid is empirically determined as the input. The dimensionalities of the cuboid, including height and weight, are crucial to the final classification results. Unfortunately, these superparameters (i.e., the dimensionalities of input cube) are hand-crafted, which means the inputs of a classifier are not optimized according to the specific hyperspectral dataset. In this letter, spatial transformation network (STN) is explored to obtain the optimal input for CNN-based HSI classification for the first time. STN is used to translate, rotate, and scale the original input to obtain optimized input for the following CNN. Moreover, in order to mitigate the overfitting problem in CNN-based HSI classification, DropBlock is introduced as a regularization technique for HSI accurate classification. Compared with dropout, which is a popular regularization technique, DropBlock obtains better classification accuracy. The proposed methods are tested on two widely used hyperspectral data sets (i.e., Salinas and Kennedy Space Center). The obtained experimental results show that the proposed methods provide competitive results compared with state-of-the-art methods including deep CNN-based methods. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) classification is a core task in the remote sensing community, and recently, deep learning-based methods have shown their capability of accurate classification of HSIs. Among the deep learning-based methods, deep convolutional neural networks (CNNs) have been widely used for the HSI classification. In order to obtain a good classification performance, substantial efforts are required to design a proper deep learning architecture. Furthermore, the manually designed architecture may not fit a specific data set very well. In this paper, the idea of automatic CNN for the HSI classification is proposed for the first time. First, a number of operations, including convolution, pooling, identity, and batch normalization, are selected. Then, a gradient descent-based search algorithm is used to effectively find the optimal deep architecture that is evaluated on the validation data set. After that, the best CNN architecture is selected as the model for the HSI classification. Specifically, the automatic 1-D Auto-CNN and 3-D Auto-CNN are used as spectral and spectral–spatial HSI classifiers, respectively. Furthermore, the cutout is introduced as a regularization technique for the HSI spectral–spatial classification to further improve the classification accuracy. The experiments on four widely used hyperspectral data sets (i.e., Salinas, Pavia University, Kennedy Space Center, and Indiana Pines) show that the automatically designed data-dependent CNNs obtain competitive classification accuracy compared with the state-of-the-art methods. In addition, the automatic design of the deep learning architecture opens a new window for future research, showing the huge potential of using neural architectures’ optimization capabilities for the accurate HSI classification. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) classification has become a hot topic in the field of remote sensing. In general, the complex characteristics of hyperspectral data make the accurate classification of such data challenging for traditional machine learning methods. In addition, hyperspectral imaging often deals with an inherently nonlinear relation between the captured spectral information and the corresponding materials. In recent years, deep learning has been recognized as a powerful feature-extraction tool to effectively address nonlinear problems and widely used in a number of image processing tasks. Motivated by those successful applications, deep learning has also been introduced to classify HSIs and demonstrated good performance. This survey paper presents a systematic review of deep learning-based HSI classification literatures and compares several strategies for this topic. Specifically, we first summarize the main challenges of HSI classification which cannot be effectively overcome by traditional machine learning methods, and also introduce the advantages of deep learning to handle these problems. Then, we build a framework that divides the corresponding works into spectral-feature networks, spatial-feature networks, and spectral–spatial-feature networks to systematically review the recent achievements in deep learning-based HSI classification. In addition, considering the fact that available training samples in the remote sensing field are usually very limited and training deep networks require a large number of samples, we include some strategies to improve classification performance, which can provide some guidelines for future studies on this topic. Finally, several representative deep learning-based classification methods are conducted on real HSIs in our experiments. [ABSTRACT FROM AUTHOR]

Deep learning models, especially deep convolutional neural networks (CNNs), have been intensively investigated for hyperspectral image (HSI) classification due to their powerful feature extraction ability. In the same manner, ensemble-based learning systems have demonstrated high potential to effectively perform supervised classification. In order to boost the performance of deep learning-based HSI classification, the idea of deep learning ensemble framework is proposed here, which is loosely based on the integration of deep learning model and random subspace-based ensemble learning. Specifically, two deep learning ensemble-based classification methods (i.e., CNN ensemble and deep residual network ensemble) are proposed. CNNs or deep residual networks are used as individual classifiers and random subspaces contribute to diversify the ensemble system in a simple yet effective manner. Moreover, to further improve the classification accuracy, transfer learning is investigated in this study to transfer the learnt weights from one individual classifier to another (i.e., CNNs). This mechanism speeds up the learning stage. Experimental results with widely used hyperspectral datasets indicate that the proposed deep learning ensemble system provides competitive results compared with state-of-the-art methods in terms of classification accuracy. The combination of deep learning and ensemble learning provides a significant potential for reliable HSI classification. [ABSTRACT FROM AUTHOR]

A generative adversarial network (GAN) usually contains a generative network and a discriminative network in competition with each other. The GAN has shown its capability in a variety of applications. In this paper, the usefulness and effectiveness of GAN for classification of hyperspectral images (HSIs) are explored for the first time. In the proposed GAN, a convolutional neural network (CNN) is designed to discriminate the inputs and another CNN is used to generate so-called fake inputs. The aforementioned CNNs are trained together: the generative CNN tries to generate fake inputs that are as real as possible, and the discriminative CNN tries to classify the real and fake inputs. This kind of adversarial training improves the generalization capability of the discriminative CNN, which is really important when the training samples are limited. Specifically, we propose two schemes: 1) a well-designed 1D-GAN as a spectral classifier and 2) a robust 3D-GAN as a spectral–spatial classifier. Furthermore, the generated adversarial samples are used with real training samples to fine-tune the discriminative CNN, which improves the final classification performance. The proposed classifiers are carried out on three widely used hyperspectral data sets: Salinas, Indiana Pines, and Kennedy Space Center. The obtained results reveal that the proposed models provide competitive results compared to the state-of-the-art methods. In addition, the proposed GANs open new opportunities in the remote sensing community for the challenging task of HSI classification and also reveal the huge potential of GAN-based methods for the analysis of such complex and inherently nonlinear data. [ABSTRACT FROM AUTHOR]

Recently, the capability of deep learning-based approaches, especially deep convolutional neural networks (CNNs), has been investigated for hyperspectral remote sensing feature extraction (FE) and classification. Due to the large number of learnable parameters in convolutional filters, lots of training samples are needed in deep CNNs to avoid the overfitting problem. On the other hand, Gabor filtering can effectively extract spatial information including edges and textures, which may reduce the FE burden of the CNNs. In this letter, in order to make the most of deep CNN and Gabor filtering, a new strategy, which combines Gabor filters with convolutional filters, is proposed for hyperspectral image classification to mitigate the problem of overfitting. The obtained results reveal that the proposed model provides competitive results in terms of classification accuracy, especially when only a limited number of training samples are available. [ABSTRACT FROM PUBLISHER]

Due to the advantages of deep learning, in this paper, a regularized deep feature extraction (FE) method is presented for hyperspectral image (HSI) classification using a convolutional neural network (CNN). The proposed approach employs several convolutional and pooling layers to extract deep features from HSIs, which are nonlinear, discriminant, and invariant. These features are useful for image classification and target detection. Furthermore, in order to address the common issue of imbalance between high dimensionality and limited availability of training samples for the classification of HSI, a few strategies such as L2 regularization and dropout are investigated to avoid overfitting in class data modeling. More importantly, we propose a 3-D CNN-based FE model with combined regularization to extract effective spectral–spatial features of hyperspectral imagery. Finally, in order to further improve the performance, a virtual sample enhanced method is proposed. The proposed approaches are carried out on three widely used hyperspectral data sets: Indian Pines, University of Pavia, and Kennedy Space Center. The obtained results reveal that the proposed models with sparse constraints provide competitive results to state-of-the-art methods. In addition, the proposed deep FE opens a new window for further research. [ABSTRACT FROM AUTHOR]

In this letter, a self-improving convolutional neural network (CNN) based method is proposed for the classification of hyperspectral data. This approach solves the so-called curse of dimensionality and the lack of available training samples by iteratively selecting the most informative bands suitable for the designed network via fractional order Darwinian particle swarm optimization. The selected bands are then fed to the classification system to produce the final classification map. Experimental results have been conducted with two well-known hyperspectral data sets: Indian Pines and Pavia University. Results indicate that the proposed approach significantly improves a CNN-based classification method in terms of classification accuracy. In addition, this letter uses the concept of dither for the first time in the remote sensing community to tackle overfitting. [ABSTRACT FROM PUBLISHER]

Hyperspectral data classification is a hot topic in remote sensing community. In recent years, significant effort has been focused on this issue. However, most of the methods extract the features of original data in a shallow manner. In this paper, we introduce a deep learning approach into hyperspectral image classification. A new feature extraction (FE) and image classification framework are proposed for hyperspectral data analysis based on deep belief network (DBN). First, we verify the eligibility of restricted Boltzmann machine (RBM) and DBN by the following spectral information-based classification. Then, we propose a novel deep architecture, which combines the spectral–spatial FE and classification together to get high classification accuracy. The framework is a hybrid of principal component analysis (PCA), hierarchical learning-based FE, and logistic regression (LR). Experimental results with hyperspectral data indicate that the classifier provide competitive solution with the state-of-the-art methods. In addition, this paper reveals that deep learning system has huge potential for hyperspectral data classification. [ABSTRACT FROM PUBLISHER]

Classification is one of the most popular topics in hyperspectral remote sensing. In the last two decades, a huge number of methods were proposed to deal with the hyperspectral data classification problem. However, most of them do not hierarchically extract deep features. In this paper, the concept of deep learning is introduced into hyperspectral data classification for the first time. First, we verify the eligibility of stacked autoencoders by following classical spectral information-based classification. Second, a new way of classifying with spatial-dominated information is proposed. We then propose a novel deep learning framework to merge the two features, from which we can get the highest classification accuracy. The framework is a hybrid of principle component analysis (PCA), deep learning architecture, and logistic regression. Specifically, as a deep learning architecture, stacked autoencoders are aimed to get useful high-level features. Experimental results with widely-used hyperspectral data indicate that classifiers built in this deep learning-based framework provide competitive performance. In addition, the proposed joint spectral–spatial deep neural network opens a new window for future research, showcasing the deep learning-based methods’ huge potential for accurate hyperspectral data classification. [ABSTRACT FROM PUBLISHER]

In hyperspectral remote sensing image classification, ensemble systems with support vector machine (SVM), such as the Random Subspace SVM Ensemble (RSSE), have significantly outperformed single SVM on the robustness and overall accuracy. In this paper, we introduce a novel subspace mechanism, the Optimizing Subspace SVM Ensemble (OSSE), to improve RSSE by selecting discriminating subspaces for individual SVMs. The framework is based on Genetic Algorithm (GA), adopting the Jeffries–Matusita (JM) distance as a criterion, to optimize the selected subspaces. The combination of optimizing subspaces is more suitable for classification than the random one, at the same time having the ability to accommodate requisite diversity within the ensemble. The modifications have improved the accuracies of individual classifiers; as a result, better overall accuracies are present. Experiments on the classification of two hyperspectral datasets reveal that our proposed OSSE obtains sound performances compared with RSSE, single SVM, and other ensemble with GA to optimize SVM. [ABSTRACT FROM PUBLISHER]