Joint multi-mode cooperative classification algorithm for hyperspectral images

In recent years, deep neural networks have been widely used for hyperspectral image (HSI) classification and have shown excellent performance using numerous labeled samples. The acquisition of HSI labels is usually based on the field investigation, which is expensive and time consuming. Hence, the available labels are usually limited, which affects the efficiency of deep HSI classification methods. To improve the classification performance while reducing the labeling cost, this article proposes a semisupervised deep learning (DL) method for HSI classification, named pyramidal dilation attention convolutional network with active and self-paced learning (PDAC-ASPL), which integrates active learning (AL), self-paced learning (SPL), and DL into a unified framework. First, a densely connected pyramidal dilation attention convolutional network is trained with a limited number of labeled samples. Then, the most informative samples from the unlabeled set are selected by AL and queried real labels, and the highest confidence samples with corresponding pseudo labels are extracted by SPL. Finally, the samples from AL and SPL are added to the training set to retrain the network. Compared with some DL- and AL-based HSI classification methods, our PDAC-ASPL achieves better performance on four HSI datasets.

Hyperspectral image (HSI) classification is one of the most popular applications in remote sensing. In practice, due to the high cost of manual labeling, only a few hyperspectral image samples with labels can be obtained. A small number of labeled training samples tend to overfit the deep network method, resulting in a sharp decline in classification accuracy. In order to solve this problem, this paper proposes a classification method for hyperspectral images based on knowledge distillation and heterogeneous few-shot learning. Firstly, the model pretrain the feature extraction network on miniImageNet, a small sample natural image dataset with abundant labeled images, and introduces knowledge distillation to improve the feature expression capability of shallow network in small sample. Then, effective knowledge transfer is carried out between two heterogeneous data sets, and the weights obtained from the model on the natural data set are transferred to the backbone network of hyperspectral image classification to improve the accuracy of HSI classification. Finally, the classifier is fine-tuned on HSI using the paradigm of small sample learning to extract discriminative hyperspectral image features and further enhance the model's detail expression. Experimental results on two hyperspectral image classification datasets show that the proposed method can effectively improve the accuracy of small sample hyperspectral image classification.

Hyperspectral image classification is a challenging problem. Among existing approaches to addressing this problem, the active learning (AL) and semisupervised learning (SSL) techniques have attracted much attention in recent years. AL usually involves a labor-intensive human-labeling process while SSL, although avoiding human labeling by assigning pseudolabels to unlabeled data, may introduce incorrect pseudolabels and thus deteriorate classification performance. To overcome these drawbacks, a novel approach named collaborative active and semisupervised learning (CASSL) is proposed in this paper. CASSL combines AL and SSL to invoke a collaborative labeling process by both human experts and classifiers. Specifically, an AL-based pseudolabel verification procedure is performed for gradually improving the pseudolabeling accuracy to facilitate SSL. Meanwhile, only those unlabeled data with low pseudolabeling confidence in SSL will become the query candidates in AL. We evaluate the performance of CASSL on three hyperspectral data sets and compare it with that of two state-of-the-art hyperspectral image classification methods. Experimental results reveal the superiority of CASSL.

Applying limited labeled samples to improve classification results is a challenge in hyperspectral images. Active Learning (AL) and Semisupervised Learning (SSL) are two promising techniques to achieve this challenge. Combining AL with SSL is an excellent idea for hyperspectral image classification. The traditional method, such as the Collaborative Active and Semisupervised Learning algorithm (CASSL), may introduce many incorrect pseudolabels and shows premature convergence. To overcome these drawbacks, a novel framework named Double-Strategy-Check Collaborative Active and Semisupervised Learning (DSC-CASSL) is proposed in this paper. This framework combines two different AL algorithms and SSL in a collaborative mode. The double-strategy verification can gradually improve the pseudolabeling accuracy and facilitate SSL. We evaluate the performance of DSC-CASSL on four hyperspectral data sets and compare it with that of four hyperspectral image classification methods. Our results suggest that DSC-CASSL leads to consistent improvement for hyperspectral image classification.

With the continuous innovation in deep learning, it has become a major direction for scholars to introduce the knowledge of deep learning into hyperspectral image classification to enhance its classification accuracy. Convolutional Neural Networks (CNN) are one of the most commonly used deep learning-based visual data processing methods, and are widely used in hyperspectral image (HSI) classification by virtue of their excellent contextual modeling capability. Since the performance of HSI classification is highly dependent on spatial and spectral information, this paper proposes a hyperspectral image classification method using 3D attention mechanism in collaboration with Transformer for hyperspectral image classification in view of the problems that the current hyperspectral image classification models with the framework of CNN have insufficient spatial spectral feature extraction and fail to excavate and represent the sequence properties of spectral features well. In this paper, we introduce a variant Transformer model based on a hybrid model of both improved 3D-CNN and 2D-CNN, combining complementary information of spatial spectrum and spectra in the form of 3D convolution and 2D convolution on CNN, and adding a variant attention mechanism module to strengthen spatial texture features, while combining grouped transfer Transformer to jump connection to enable the lower layer to better learn the upper layer features. Firstly, a variant channel attention mechanism is introduced on 3D-CNN to enhance the acquisition of spectral information of image features by 3D-CNN. Secondly, a variant spatial attention mechanism is introduced to enable 3D-CNN to better acquire the spatial information of hyperspectral images in the network, and subsequently the acquired spatial and spectral feature information is passed to 2D-CNN to enable it to better acquire local feature information. Finally, the acquired image feature information is passed to the variant Transformer model to make up for the fact that CNN can only acquire hyperspectral image features in local contexts, enabling it to better acquire global feature information on feature sequences. The experimental results show that the proposed model is experimented on two hyperspectral datasets, Indian Pines and Pavia University, and the overall classification accuracy (OA), average classification accuracy (AA), and Kappa coefficient reach up to 99.59%, 99.31%, and 99.45%, respectively, on the PU dataset, compared with the current cutting-edge techniques. The classification accuracy has been improved.

Hyperspectral image (HSI) classification is the subject of intense research in remote sensing. The tremendous success of deep learning in computer vision has recently sparked the interest in applying deep learning in hyperspectral image classification. However, most deep learning methods for hyperspectral image classification are based on convolutional neural networks (CNN). Those methods require heavy GPU memory resources and run time. Recently, another deep learning model, the transformer, has been applied for image recognition, and the study result demonstrates the great potential of the transformer network for computer vision tasks. In this paper, we propose a model for hyperspectral image classification based on the transformer, which is widely used in natural language processing. Besides, we believe we are the first to combine the metric learning and the transformer model in hyperspectral image classification. Moreover, to improve the model classification performance when the available training samples are limited, we use the 1-D convolution and Mish activation function. The experimental results on three widely used hyperspectral image data sets demonstrate the proposed model’s advantages in accuracy, GPU memory cost, and running time.

Hyperspectral Image (HSI) classification plays a key role remote sensing field. Recently, deep learning has demonstrated its effectiveness in HSI Classification field. This paper presents a spectral-spatial HSI classification technique established on the deep learning based deep belief network (DBN) for deep and abstract feature extraction and adaptive boundary adjustment based segmentation. Proposed approach focuses on integrating the deep learning based spectral features and segmentation based spatial features into a framework for improved performance. Specifically, first the deep DBN model is exploited as a spectral feature extraction based classifier to extract the deep spectral features. Second, spatial contextual features are obtained by utilizing effective adaptive boundary adjustment based segmentation technique. Finally, maximum voting based criteria is operated to integrate the results of extracted spectral and spatial information for improved HSI classification. In general, exploiting spectral features from DBN process and spatial features from segmentation and integration of spectral and spatial information by maximum voting based criteria, has a substantial effect on the performance of HSI classification. Experimental performance on real and widely used hyperspectral data sets with different contexts and resolutions demonstrates the accuracy of the proposed technique and performance is comparable to several recently proposed HSI classification techniques.

Deep neural network has been extensively applied to hyperspectral image (HSI) classification recently. However, its success is greatly attributed to numerous labeled samples, whose acquisition costs a large amount of time and money. In order to improve the classification performance while reducing the labeling cost, this article presents an active deep learning approach for HSI classification, which integrates both active learning and deep learning into a unified framework. First, we train a convolutional neural network (CNN) with a limited number of labeled pixels. Next, we actively select the most informative pixels from the candidate pool for labeling. Then, the CNN is fine-tuned with the new training set constructed by incorporating the newly labeled pixels. This step together with the previous step is iteratively conducted. Finally, Markov random field (MRF) is utilized to enforce class label smoothness to further boost the classification performance. Compared with the other state-of-the-art traditional and deep learning-based HSI classification methods, our proposed approach achieves better performance on three benchmark HSI data sets with significantly fewer labeled samples.

Recently, convolutional neural networks (CNNs) have been introduced for hyperspectral image (HSI) classification and shown considerable classification performance. However, the previous CNNs designed for spectral-spatial HSI classification lay stress on the learning for the spatial correlation of HSI data and neglect the channel responses of feature maps. Furthermore, the lack of training samples remains the major challenge for CNN-based HSI classification methods to achieve better performance. To address the aforementioned issues, this paper proposes a new end-to-end pre-activation residual attention network (PRAN) for HSI classification. The pre-activation mechanism and attention mechanism are introduced into the proposed network, and a pre-activation residual attention block (PRAB) is designed, which allows the proposed network to carry adaptively feature recalibration of channel responses and learn more robust spectral-spatial joint feature representations. The proposed PRAN is equipped with two PRABs and several convolutional layers with different kernel sizes, which enables the PRAN to extract high-level discriminative features. Experimental results on three benchmark HSI datasets reveal that the proposed method is provided with competitive performance over several state-of-the-art HSI classification methods, especially when the training set size is relatively small.

Convolutional neural networks (CNN) have achieved excellent performance for the hyperspectral image (HSI) classification problem due to better extracting spectral and spatial information. However, CNN can only perform convolution calculations on Euclidean datasets. To solve this problem, recently, the graph convolutional neural network (GCN) is proposed, which can be applied to the semisupervised HSI classification problem. However, the GCN is a direct push learning method, which requires all nodes to participate in the training process to get the node embedding. This may bring great computational cost for the hyperspectral data with a large number of pixels. Therefore, in this article, we propose an inductive learning method to solve the problem. It constructs the graph by sampling and aggregating (GraphSAGE) feature from a node's local neighborhood. This could greatly reduce the space complexity. Moreover, to enhance the classification performance, we also construct the graph using spectral and spatial information (spectra-spatial GraphSAGE). Experiments on several hyperspectral image datasets show that the proposed method can achieve better classification performance compared with state-of-the-art HSI classification methods.

Supervised hyperspectral image classification is a difficult task due to the unbalance between the high dimensionality of the data and the limited availability of labeled training samples in real analysis scenarios. While the collection of labeled samples is generally difficult, expensive and time-consuming, unlabeled samples can be generated in a much easier way. This observation has fostered the idea of adopting semi-supervised learning (SSL) techniques in hyperspectral image classification. The main assumption of such techniques is that the new (unlabeled) training samples can be obtained from a (limited) set of available labeled samples without significant effort/cost. In this paper, we develop a new framework for SSL which exploits active learning (AL) for unlabeled sample selection. Specifically, we use AL to select the most informative unlabeled training samples and further evaluate two different strategies for active sample selection. In this work, the proposed approach is illustrated with the sparse multinomial logistic regression (SMLR) classifier learned with the MLR via variable splitting and augmented Lagrangian (LORSAL) algorithm. Our experimental results with a real hyperspectral image collected by the NASA Jet Propulsion Laboratory's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) indicate that the use of AL for unlabeled sample selection represents an effective and promising strategy in the context of semi-supervised hyperspectral data classification.

Recently, Hyperspectral Image (HSI) classification methods based on deep learning models have shown encouraging performance. However, the limited numbers of training samples, as well as the mixed pixels due to low spatial resolution, have become major obstacles for HSI classification. To tackle these problems, we propose a resource-efficient HSI classification framework which introduces adaptive spectral unmixing into a 3D/2D dense network with early-exiting strategy. More specifically, on one hand, our framework uses a cascade of intermediate classifiers throughout the 3D/2D dense network that is trained end-to-end. The proposed 3D/2D dense network that integrates 3D convolutions with 2D convolutions is more capable of handling spectral-spatial features, while containing fewer parameters compared with the conventional 3D convolutions, and further boosts the network performance with limited training samples. On another hand, considering the existence of mixed pixels in HSI data, the pixels in HSI classification are divided into hard samples and easy samples. With the early-exiting strategy in these intermediate classifiers, the average accuracy can be improved by reducing the amount of computation cost for easy samples, thus focusing on classifying hard samples. Furthermore, for hard samples, an adaptive spectral unmixing method is proposed as a complementary source of information for classification, which brings considerable benefits to the final performance. Experimental results on four HSI benchmark datasets demonstrate that the proposed method can achieve better performance than state-of-the-art deep learning-based methods and other traditional HSI classification methods.

Over the past few years, convolutional neural network (CNN) has been broadly adopted in remote sensing (RS) imagery processing areas due to its impressive capabilities in feature extraction. Nevertheless, it is still a challenge for CNN-based hyperspectral image (HSI) classification methods to extract more effective spectral-spatial features considering all spectral bands. Driven by this issue, we propose a novel approach to cope with the HSI classification task, referring to the multi-level joint feature extraction network (MJFEN). The proposed network makes full use of the information on each channel of HSI and transforms it into valid channel-wised spatial features through a designed convolution process. Moreover, these feature maps form global attention details to guide the extraction of spectral-spatial features, which are taken to the next level for further feature mining. Then, the features obtained at different levels are integrated for ground object classification. In contrast with several state-of-the-art HSI classification methods on four public datasets, experimental results demonstrate the effectiveness and remarkable feature extraction capability of our proposed approach.

Hyperspectral image (HSI) classification has become one of the most significant tasks in the field of hyperspectral analysis. However, classifying each pixel in HSI accurately is challenging due to the curse of dimensionality and limited training samples. In this paper, we present an HSI classification architecture called camera spectral response network (CSR-Net), which can learn the optimal camera spectral response (CSR) function for HSI classification problems and effectively reduce the spectral dimensions of HSI. Specifically, we design a convolutional layer to simulate the capturing process of cameras, which learns the optimal CSR function for HSI classification. Then, spectral and spatial features are further extracted by spectral and spatial attention modules. On one hand, the learned CSR can be implemented physically and directly used to capture scenes, which makes the image acquisition process more convenient. On the other hand, compared with ordinary HSIs, we only need images with far fewer bands, without sacrificing the classification precision and avoiding the curse of dimensionality. The experimental results of four popular public hyperspectral datasets show that our method, with only a few image bands, outperforms state-of-the-art HSI classification methods which utilize the full spectral bands of images.

Hyperspectral image (HSI) classification aims to distinguish the category of a land coverage object for each pixel. In an effective way, the transformer architecture has been successfully introduced for the HSI classification task with promising performance. However, existing transformer-based HSI classification methods still suffer from the inability to fully explore both spectral information and spatial information in HSIs. To this end, we propose a Spectral-Spatial Token Enhanced Transformer (SSTE-Former) method with the hash-based positional embedding, which is the first to exploit multiscale spectral-spatial information for transformer-based HSI classification in-depth. Specifically, SSTE-Former accepts multiscale HSI cubes centered on the target pixel, that are preprocessed by PCA. Then, a designed multiscale CNN architecture is utilized to extract short-range spectral-spatial features and generate token embeddings. In parallel, a novel hash-based spatially enhanced positional embedding tailored for HSI cubes is developed to model the correlations within and across multiscale token embeddings. Finally, multiscale token embeddings and hash-based positional embeddings are concatenated and flattened into the transformer encoder for long-range spectral-spatial feature fusion. We conduct extensive experiments on four benchmark HSI datasets and achieve superior performance compared with the state-of-the-art HSI classification methods.