Hyperspectral Imaging: Techniques for Spectral Detection and Classification is an outgrowth of the research conducted over the years in the Remote Sensing Signal and Image Processing Laboratory (RSSIPL) at the University of Maryland, Baltimore County. It explores applications of statistical signal processing to hyperspectral imaging and further develops non-literal (spectral) techniques for subpixel detection and mixed pixel classification. This text is the first of its kind on the topic and can be considered a recipe book offering various techniques for hyperspectral data exploitation. In particular, some known techniques, such as OSP (Orthogonal Subspace Projection) and CEM (Constrained Energy Minimization) that were previously developed in the RSSIPL, are discussed in great detail. This book is self-contained and can serve as a valuable and useful reference for researchers in academia and practitioners in government and industry.

Hyperspectral Imaging Techniques For Spectral Detection And Classification

In deep learning research, self-supervised learning (SSL) has received great attention, triggering interest within both the computer vision and remote sensing communities. While there has been big success in computer vision, most of the potential of SSL in the domain of Earth observation remains locked. In this article, we provide an introduction to and a review of the concepts and latest developments in SSL for computer vision in the context of remote sensing. Further, we provide a preliminary benchmark of modern SSL algorithms on popular remote sensing datasets, verifying the potential of SSL in remote sensing and providing an extended study on data augmentations. Finally, we identify a list of promising directions of future research in SSL for Earth observation (SSL4EO) to pave the way for the fruitful interaction of both domains. 

/pdf/self-supervised-learning-in-remote-sensing-a-review-2v8jveba.pdf

Self-Supervised Learning in Remote Sensing: A review

In deep learning research, self-supervised learning (SSL) has received great attention, triggering interest within both the computer vision and remote sensing communities. While there has been big success in computer vision, most of the potential of SSL in the domain of Earth observation remains locked. In this article, we provide an introduction to and a review of the concepts and latest developments in SSL for computer vision in the context of remote sensing. Further, we provide a preliminary benchmark of modern SSL algorithms on popular remote sensing datasets, verifying the potential of SSL in remote sensing and providing an extended study on data augmentations. Finally, we identify a list of promising directions of future research in SSL for Earth observation (SSL4EO) to pave the way for the fruitful interaction of both domains.

It is difficult to obtain expensive labeled data in industrial fault diagnosis applications, which easily leads to overfitting of deep learning and restricts its extensive usage. Aiming at this issue, this article proposed an improved few-shot semisupervised learning method, called semisupervised momentum prototype network (SSMPN), to realize gearbox fault diagnosis under limited labeled samples. First, the proposed SSMPN utilizes the powerful few-shot learning ability of the prototype network to learn the feature mapping and obtains prototypes by using limited labeled samples. Then, a threshold selection based on Monte Carlo uncertainty is adopted in pseudo label learning to increase the confidence of pseudo labels. Finally, the expended labeled dataset is utilized to optimize feature extraction and the momentum prototype method is proposed to fine-tune the prototype of each category. The experiments on both test-bench and wind turbine gearbox fault diagnosis demonstrated that SSMPN is more effective than the comparable methods under the same situation.

Semisupervised Momentum Prototype Network for Gearbox Fault Diagnosis Under Limited Labeled Samples

For both traditional classification and current popular deep learning methods, the limited sample classification problem is very challenging, and the lack of samples is an important factor affecting the classification performance Our work includes two aspects First, the unsupervised data augmentation for all hyperspectral samples not only improves the classification accuracy greatly with the newly added training samples, but also further improves the classification accuracy of the classifier by optimizing the augmented test samples Second, an effective spectral structure extraction method is designed, and the effective spectral structure features have a better classification accuracy than the true spectral features

Data Augmentation and Spectral Structure Features for Limited Samples Hyperspectral Classification

Despite the success of deep learning based methods for hyperspectral imagery (HSI) classification, they demand amounts of labeled samples for training whereas the labeled samples in lots of applications are always insufficient due to the expensive manual annotation cost. To address this problem, we propose a two-branch deep learning based method for few-shot HSI classification, where two branches separately accomplish HSI classification in a cube-wise level and a cube-pair level. With a shared feature extractor sub-network, the self-supervised knowledge contained in the cube-pair branch provides an effective way to regularize the original few-shot HSI classification branch (i.e., cube-wise branch) with limited labeled samples, which thus improves the performance of HSI classification. The superiority of the proposed method on few-shot HSI classification is demonstrated experimentally on two HSI benchmark datasets.

Deep Self-Supervised Learning for Few-Shot Hyperspectral Image Classification

Hyperspectral image (HSI) classification plays an important role in lots of HSI analysis related tasks. In recent years, deep learning based methods draw much attention for HSI classification due to the powerful representation ability. The activation function is essential for deep learning based methods since it introduces nonlinearity into the network. However, existing activation functions such as Sigmoid and ReLU are pre-defined, which are handcrafted and general for any kinds of data. If they fit well to a specific dataset and thus lead to best classification result is seldom studied. In this paper, we propose to learn a specific data-driven function called data-specific (DS) activation function for HSI classification. Instead of using a hand-crafted function, we propose an HSI oriented activation function generation strategy, in which neural network (NN) architecture is utilized to learn the activation function suitable for HSI classification. Experiment results demonstrate the effectiveness of the learned activation function for HSI classification.

Data-Specific Activation Function Learning for Hyperspectral Image Classification

Deep neural networks have underpinned much of the recent progress in the field of hyperspectral image (HSI) classification owing to their powerful ability to learn discriminative features. However, training a deep neural network often requires the availability of a large number of labeled samples to mitigate over-fitting, and these labeled samples are not always available in practical applications. To adapt the deep neural network-based HSI classification approach to cases in which only a very limited number of labeled samples (i.e., few or even only one labeled sample) are provided, we propose a novel few-shot deep learning framework for HSI classification. In order to mitigate over-fitting, the framework borrows supervision from an auxiliary set of unlabeled samples with soft pseudo-labels to assist the training of the feature extractor on few labeled samples. By considering each labeled sample as a reference agent, the soft pseudo-label is assigned by computing the distances between the unlabeled sample and all agents. To demonstrate the effectiveness of the proposed method, we evaluate it on three benchmark HSI classification datasets. The results indicate that our method achieves better performance relative to existing competitors in few-shot and one-shot settings.

Boosting Few-Shot Hyperspectral Image Classification Using Pseudo-Label Learning

Hyperspectral imagery (HSI) classification is one of the fundamental applications in remote sensing domain, which aims at predicting the labels of unlabeled pixels in an image with a classifier trained on a certain amount of labeled pixels. However, due to the expensive cost on manual labeling, only limited labeled pixels can be obtained in real applications, which is prone to result in the training of classifier to be overfitting. To address this problem, we present an intraclass similarity structure representation-based HSI classification method. First, according to the intraclass spectrum similarity of pixels, we establish a mixed labels-based annotation model. Given some randomly selected unlabeled pixels, we employ the proposed annotation model to assign each pixel a mixed label from the top-two possible classes, and then augment the original training set with those labeled pixels. On the augmented training set, we train a deep convolutional neural network-based classification model. With several individual rounds of the annotation and classifier training procedures, we obtain several independent classification models and predict the final labels as their fusion results with a voting strategy. Experimental results demonstrate the effectiveness of the proposed method in terms of HSI classification with few training samples.

/pdf/intraclass-similarity-structure-representation-based-32knxf0tuc.pdf

Intraclass Similarity Structure Representation-Based Hyperspectral Imagery Classification With Few Samples

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Papers

Deep Self-Supervised Learning for Few-Shot Hyperspectral Image Classification

Data-Specific Activation Function Learning for Hyperspectral Image Classification

Boosting Few-Shot Hyperspectral Image Classification Using Pseudo-Label Learning

Intraclass Similarity Structure Representation-Based Hyperspectral Imagery Classification With Few Samples