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Recently, deep learning methods, especially convolutional neural networks (CNNs), have achieved good performance for hyperspectral image (HSI) classification. However, due to limited training samples of HSIs and the high volume of trainable parameters in deep models, training deep CNN-based models is still a challenge. To address this issue, this study investigates contrastive learning (CL) as a pre-training strategy for HSI classification. Specifically, a supervised contrastive learning (SCL) framework, which pre-trains a feature encoder using an arbitrary number of positive and negative samples in a pair-wise optimization perspective, is proposed. Additionally, three techniques for better generalization in the case of limited training samples are explored in the proposed SCL framework. First, a spatial–spectral HSI data augmentation method, which is composed of multiscale and 3D random occlusion, is designed to generate diverse views for each HSI sample. Second, the features of the augmented views are stored in a queue during training, which enriches the positives and negatives in a mini-batch and thus leads to better convergence. Third, a multi-level similarity regularization method (MSR) combined with SCL (SCL–MSR) is proposed to regularize the similarities of the data pairs. After pre-training, a fully connected layer is combined with the pre-trained encoder to form a new network, which is then fine-tuned for final classification. The proposed methods (SCL and SCL–MSR) are evaluated on four widely used hyperspectral datasets: Indian Pines, Pavia University, Houston, and Chikusei. The experiment results show that the proposed SCL-based methods provide competitive classification accuracy compared to the state-of-the-art methods. [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]

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]

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]

The multisensory fusion of remote sensing data has obtained a great attention in recent years. In this letter, we propose a new feature fusion framework based on deep neural networks (DNNs). The proposed framework employs deep convolutional neural networks (CNNs) to effectively extract features of multi-/hyperspectral and light detection and ranging data. Then, a fully connected DNN is designed to fuse the heterogeneous features obtained by the previous CNNs. Through the aforementioned deep networks, one can extract the discriminant and invariant features of remote sensing data, which are useful for further processing. At last, logistic regression is used to produce the final classification results. Dropout and batch normalization strategies are adopted in the deep fusion framework to further improve classification accuracy. The obtained results reveal that the proposed deep fusion model provides competitive results in terms of classification accuracy. Furthermore, the proposed deep learning idea opens a new window for future remote sensing data fusion. [ABSTRACT FROM PUBLISHER]

Recovering hyperspectral image (HSI) from mixed noise degradation is a challenging and promising theme in remote sensing, particularly when stripes and deadlines exist in several contiguous bands. This paper proposes a HSI’s restoration method making use of adaptive morphological filtering (AMF) and fusing structure information of an auxiliary color image. An adaptive structuring element (ASE) indicating morphological features of each pixel is generated through information fusion, to simultaneously remove the mixed noise and preserve fine spatial structures. This key technology contains three main steps. First, edges are extracted from the auxiliary image exploiting its color information; then, an edge-constraint growing algorithm is used to generate the clustering kernel; finally, the ASE is obtained via goal-guided k-means clustering. The ASE has extensive application value, for it can be an enhancing module for most filters-based restoration methods, to mitigate the structural damage due to the fixed mask. Among these methods, Gaussian filter for preprocessing and majority voting for postprocessing are introduced in this paper as representatives. In addition, the auxiliary image can be both visible image of multisensor and false RGB component of the undamaged bands of the HSI, so it is relatively available. Experiments on simulated and real data sets show obvious effects on denoising and destriping both subjectively and objectively. The advantage of ASE on structure details preserving, compared to conventional approaches, is clearly demonstrated. The application value of the proposed restoration frame and ASE is further proved through the decision-level postprocessing experiments. [ABSTRACT FROM PUBLISHER]

Hyperspectral images (HSI) regularly contain hundreds of bands, which are of different importance in the application. Most HSI compression methods usually deal with most bands in the same way, and they do not take the difference of different bands into consideration, which may cause the loss of important spectral information. In order to preserve the spectral information of interest for applications, a new band-of-interest (BOI)-preserving-based HSI compression method is proposed. The conception of BOI is proposed because some bands are significant in the specific applications, and BOI selection methods are chosen according to application requirements. BOI selection is first performed according to application measurements. Then, BOI information is fed into recursive bidirection prediction (RBP) and set partition in hierarchical trees (SPIHT) compression scheme which uses RBP for spectral decorrelation followed by SPIHT algorithm for coding the resulting decorrelated residual images. More bits are allocated to BOI to preserve BOI by two approaches, respectively. Compress BOI and non-BOI bands directly with low distortion and high distortion, respectively, and compress all bands with low distortion and perform a postcompression truncation. Experiments are implemented with different settings using AVIRIS images. Results indicate that the proposed two methods both can achieve excellent compression efficiency and reconstructed quality. In addition, they can improve the application effect in both material classification and target recognition. Compared with non-BOI compression algorithm, at the compression ratio of 80, the proposed methods improve the classification accuracy by 2% and target recognition accuracy by 9%. [ABSTRACT FROM AUTHOR]

Recently, a great many deep convolutional neural network (CNN)-based methods have been proposed for hyperspectral image (HSI) classification. Although the proposed CNN-based methods have the advantages of spatial feature extraction, they are difficult to handle the sequential data with and CNNs are not good at modeling the long-range dependencies. However, the spectra of HSI are a kind of sequential data, and HSI usually contains hundreds of bands. Therefore, it is difficult for CNNs to handle HSI processing well. On the other hand, the Transformer model, which is based on an attention mechanism, has proved its advantages in processing sequential data. To address the issue of capturing relationships of sequential spectra in HSI in a long distance, in this study, Transformer is investigated for HSI classification. Specifically, in this study, a new classification framework titled spatial-spectral Transformer (SST) is proposed for HSI classification. In the proposed SST, a well-designed CNN is used to extract the spatial features, and a modified Transformer (a Transformer with dense connection, i.e., DenseTransformer) is proposed to capture sequential spectra relationships, and multilayer perceptron is used to finish the final classification task. Furthermore, dynamic feature augmentation, which aims to alleviate the overfitting problem and therefore generalize the model well, is proposed and added to the SST (SST-FA). In addition, to address the issue of limited training samples in HSI classification, transfer learning is combined with SST, and another classification framework titled transferring-SST (T-SST) is proposed. At last, to mitigate the overfitting problem and improve the classification accuracy, label smoothing is introduced for the T-SST-based classification framework (T-SST-L). The proposed SST, SST-FA, T-SST, and T-SST-L are tested on three widely used hyperspectral datasets. The obtained results reveal that the proposed models provide competitive results compared to the state-of-the-art methods, which shows that the concept of Transformer opens a new window for HSI classification. [ABSTRACT FROM AUTHOR]