Hyperspectral Image Classification Based on Active Learning and Spectral-Spatial Feature Fusion Using Spatial Coordinates

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 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 spectral-spatial features fusion has become a research focus in hyperspectral image (HSI) classification. However, how to extract more robust spectral-spatial features is still a challenging problem. In this article, a novel deep multilayer fusion dense network (MFDN) is proposed to improve the performance of HSI classification. The proposed MFDN simultaneously extracts the spatial and spectral features based on different sample input sizes, which can extract abundant spectral and spatial correlation information. First, the principal component analysis algorithm is performed on hyperspectral data to extract low-dimensional HSI data, and then the spatial features are extracted from the low-dimensional 3-D HSI data through 2-D convolutional, 2-D dense block, and average-pooling layers. Second, the spectral features are extracted directly from the raw 3-D HSI data by means of 3-D convolutional, 3-D dense block, and average-pooling layers. Third, the spatial and spectral features are fused together through 3-D convolutional, 3-D dense block, and average-pooling layers. Finally, the fused spectral-spatial features are sent into two full connection layers to extract high-level abstract features. Furthermore, densely connected structures can help alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and improve the HSI classification accuracy. The proposed fusion network outperforms the other state-of-the-art methods especially with a small number of labeled samples. Experimental results demonstrate that it can achieve outstanding hyperspectral classification performance.

Deep learning has achieved great success in hyperspectral image (HSI) classification. However, its success relies on the availability of sufficient training samples. Unfortunately, the collection of training samples is expensive, time-consuming, and even impossible in some cases. Natural image datasets that are different from HSI, such as Image Net and mini-ImageNet, have abundant texture and structure information. Effective knowledge transfer between two heterogeneous datasets can significantly improve the accuracy of HSI classification. In this letter, heterogeneous few-shot learning (HFSL) for HSI classification is proposed with only a few labeled samples per class. First, few-shot learning is performed on the mini-ImageNet datasets to learn the transferable knowledge. Then, to make full use of the spatial and spectral information, a spectral&#x2013;spatial fusion network is devised. Spectral information is obtained by the residual network with pure 1-D operators. Spatial information is extracted by a convolution network with pure 2-D operators, and the weights of the spatial network are initialized by the weights of the model trained on the mini-ImageNet datasets. Finally, few-shot learning is fine-tuned on HSI to extract discriminative spectral&#x2013;spatial features and individual knowledge, which can improve the classification performance of the new classification task. Experiments conducted on two public HSI datasets demonstrate that the HFSL outperforms the existing few-shot learning methods and supervised learning methods for HSI classification with only a few labeled samples. Our source code is available at <uri>https://github.com/Li-ZK/HFSL</uri>.

Deep learning has achieved significant success in the field of hyperspectral image (HSI) classification, but challenges are still faced when the number of training samples is small. Feature fusing approaches based on multi-channel and multi-scale feature extractions are attractive for HSI classification where few samples are available. In this paper, based on feature fusion, we proposed a simple yet effective CNN-based Dual-channel Spectral Enhancement Network (DSEN) to fully exploit the features of the small labeled HSI samples for HSI classification. We worked with the observation that, in many HSI classification models, most of the incorrectly classified pixels of HSI are at the border of different classes, which is caused by feature obfuscation. Hence, in DSEN, we specially designed a spectral feature extraction channel to enhance the spectral feature representation of the specific pixel. Moreover, a spatial–spectral channel was designed using small convolution kernels to extract the spatial–spectral features of HSI. By adjusting the fusion proportion of the features extracted from the two channels, the expression of spectral features was enhanced in terms of the fused features for better HSI classification. The experimental results demonstrated that the overall accuracy (OA) of HSI classification using the proposed DSEN reached 69.47%, 80.54%, and 93.24% when only five training samples for each class were selected from the Indian Pines (IP), University of Pavia (UP), and Salinas Scene (SA) datasets, respectively. The performance improved when the number of training samples increased. Compared with several related methods, DSEN demonstrated superior performance in HSI classification.

Adaptive spectral-spatial feature fusion network for hyperspectral image classification using limited training samples

Hyperspectral Image (HSI) classification is one of the most persistent issue in remote sensing field. Recently, deep learning has attracted attention in HSI Classification field due to its accuracy and stronger generalization. This paper proposes a new spectral-spatial HSI classification approach developed on the deep learning concept of stacked-auto-encoders (SAE) based deep feature extraction and hidden Markov random field based segmentation. Specifically, First the SAE model is implemented as a spectral information-based classifier to extract the deep spectral features. Second, spatial information is obtained by using effective Hidden Markov random field (HMRF) based segmentation technique. Finally, maximum voting based criteria is employed to merge the extracted spectral and spatial information, which results in the precise spectral-spatial HSI classification. The characterization of the HSI with spectral spatial features results into more comprehensive analysis of HSI and to a more accurate classification. In general, use of spectral information resulted from the SAE process and spatial information by means of HMRF based segmentation and merging of spectral and spatial information by means of maximum voting based criteria, has a significant effect on the accuracy of the HSI classification. Experiments on real diverse hyperspectral data sets with different contexts and resolutions acquired by AVIRIS and ROSIS sensors show the accuracy of the proposed method and confirms that results of the proposed classification approach are comparable to several recently proposed HSI classification techniques.

Hyperspectral image (HSI) classification is an active and important research task driven by many practical applications. To leverage deep learning models especially convolutional neural networks (CNNs) for HSI classification, this paper proposes a simple yet effective method to extract hierarchical deep spatial feature for HSI classification by exploring the power of off-the-shelf CNN models, without any additional retraining or fine-tuning on the target data set. To obtain better classification accuracy, we further propose a unified metric learning-based framework to alternately learn discriminative spectral–spatial features, which have better representation capability and train support vector machine (SVM) classifiers. To this end, we design a new objective function that explicitly embeds a metric learning regularization term into SVM training. The metric learning regularization term is used to learn a powerful spectral–spatial feature representation by fusing spectral feature and deep spatial feature, which has small intraclass scatter but big between class separation. By transforming HSI data into new spectral–spatial feature space through CNN and metric learning, we can pull the pixels from the same class closer, while pushing the different class pixels farther away. In the experiments, we comprehensively evaluate the proposed method on three commonly used HSI benchmark data sets. State-of-the-art results are achieved when compared with the existing HSI classification methods.

Hyperspectral image(HSI) Classification is one of the most prevalent issue in remote sensing area. Recently, application of deep learning in HSI classification has emerged. However, merging spatial features with spectral properties in deep learning is a pervasive problem. This paper presents, a discriminative spatial updated deep belief network (SDBN) which effectively utilizes spatial information within spectrally identical contiguous pixels for HSI classification. In the proposed approach, HSI is first segmented into adaptive boundary adjustment based spatially similar regions with similar spectral features, following which an object-level feature extraction and classification is undertaken using deep belief network (DBN) based decision fusion approach that incorporate spatial-segmented contextual and spectral information into a DBN framework for effective spectral-spatial HSI classification. Moreover, for improved accuracy, band preference/correlation based feature selection approach is used to select the most informative bands without compromising the original content in HSI. Usage of local contextual features and spectral similarity from adaptive boundary adjustment based approach, and integration of spatial and spectral features into DBN results into improved accuracy of the final HSI classification. Experimental results on well known hyperspectral data indicates the classification accuracy of the proposed method over several existing techniques.

In recent years, hyperspectral image (HSI) classification has become a hot research direction in remote sensing image processing. Benefiting from the development of deep learning, convolutional neural networks (CNNs) have shown extraordinary achievements in HSI classification. Numerous methods combining CNNs and attention mechanisms (AMs) have been proposed for HSI classification. However, to fully mine the features of HSI, some of the previous methods apply dense connections to enhance the feature transfer between each convolution layer. Although dense connections allow these methods to fully extract features in a few training samples, it decreases the model efficiency and increases the computational cost. Furthermore, to balance model performance against complexity, the AMs in these methods compress a large number of channels or spatial resolutions during the training process, which results in a large amount of useful information being discarded. To tackle these issues, in this article, a novel one-shot dense network with polarized attention, namely, OSDN, was proposed for HSI classification. More precisely, since HSI contains rich spectral and spatial information, the OSDN has two independent branches to extract spectral and spatial features, respectively. Similarly, the polarized AMs contain two components: channel-only AMs and spatial-only AMs. Both polarized AMs can use a specially designed filtering method to reduce the complexity of the model while maintaining high internal resolution in both the channel and spatial dimensions. To verify the effectiveness and lightness of OSDN, extensive experiments were carried out on five benchmark HSI datasets, namely, Pavia University (PU), Kennedy Space Center (KSC), Botswana (BS), Houston 2013 (HS), and Salinas Valley (SV). Experimental results consistently showed that the OSDN can greatly reduce computational cost and parameters while maintaining high accuracy in a few training samples.

Hyperspectral image (HSI) classification is a procedure of interest in remote sensing. HSIs contain complex spectral and spatial information, so classification tasks remain difficult. Although current deep-learning models have made significant progress in HSI classification, dealing with spectral and spatial information still requires careful investigation. To better manage spectral and spatial information and improve classification accuracy, we introduce a multiscale residual weakly dense network with an attention mechanism. First, we designed two residual weakly dense (Res-WDens) branches to extract spectral and spatial feature information and then applied the Concat method to fuse the two kinds of information. We also designed a plug-and-play hybrid attention module to refine the fused information so the network could focus on the essential spectral and spatial features. Finally, considering the relevance of spectral and spatial information, a dual-channel multiscale feature extraction module was used to extract the spectral–spatial multiscale information of HSIs. The overall accuracies of our proposed method reached 99.76%, 99.97%, and 100% on three publicly available datasets. A series of experiments demonstrated that our method is comparable to current state-of-the-art methods.

In recent years, hyperspectral image classification techniques have attracted a lot of attention from many scholars because they can be used to model the development of different cities and provide a reference for urban planning and construction. However, due to the difficulty in obtaining hyperspectral images, only a limited number of pixels can be used as training samples. Therefore, how to adequately extract and utilize the spatial and spectral information of hyperspectral images with limited training samples has become a difficult problem. To address this issue, we propose a hyperspectral image classification method based on dense pyramidal convolution and multi-feature fusion (DPCMF). In this approach, two branches are designed to extract spatial and spectral features, respectively. In the spatial branch, dense pyramid convolutions and non-local blocks are used to extract multi-scale local and global spatial features in image samples, which are then fused to obtain spatial features. In the spectral branch, dense pyramidal convolution layers are used to extract spectral features in image samples. Finally, the spatial and spectral features are fused and fed into fully connected layers to obtain classification results. The experimental results show that the overall accuracy (OA) of the method proposed in this paper is 96.74%, 98.10%, 98.92% and 96.67% on the four hyperspectral datasets, respectively. Significant improvements are achieved compared to the five methods of SVM, SSRN, FDSSC, DBMA and DBDA for hyperspectral classification. Therefore, the proposed method can better extract and exploit the spatial and spectral information in image samples when the number of training samples is limited. Provide more realistic and intuitive terrain and environmental conditions for urban planning, design, construction and management.

In recent years, the use of deep neural network in effective network feature extraction and the design of efficient and high-precision hyperspectral image classification algorithms has gradually become a research hotspot for scholars. However, due to the difficulty of obtaining hyperspectral images and the high cost of annotation, the training samples are very limited. In order to cope with the small sample problem, researchers often deepen the network model and use the attention mechanism to extract features; however, as the network model continues to deepen, the gradient disappears, the feature extraction ability is insufficient, and the computational cost is high. Therefore, how to make full use of the spectral and spatial information in limited samples has gradually become a difficult problem. In order to cope with such problems, this paper proposes two-branch multiscale spatial–spectral feature aggregation with a self-attention mechanism for a hyperspectral image classification model (FHDANet); the model constructs a dense two-branch pyramid structure, which can achieve the high efficiency extraction of joint spatial–spectral feature information and spectral feature information, reduce feature loss to a large extent, and strengthen the model’s ability to extract contextual information. A channel–space attention module, ECBAM, is proposed, which greatly improves the extraction ability of the model for salient features, and a spatial information extraction module based on the deep feature fusion strategy HLDFF is proposed, which fully strengthens feature reusability and mitigates the feature loss problem brought about by the deepening of the model. Compared with five hyperspectral image classification algorithms, SVM, SSRN, A2S2K-ResNet, HyBridSN, SSDGL, RSSGL and LANet, this method significantly improves the classification performance on four representative datasets. Experiments have demonstrated that FHDANet can better extract and utilise the spatial and spectral information in hyperspectral images with excellent classification performance under small sample conditions.

Convolutional neural network (CNN) has been successfully introduced to hyperspectral image (HSI) classification and achieved effective performance. With the depth of the CNN increases, it may cause the gradient to become zero, and the structure lacks the utilization of the correlated spatial feature information between different convolutional layers. At the same time, this single-scale convolution kernel is insufficient in expressing the complex spatial structure information of HSI. Additionally, the CNN-based methods treat the HSIs spectral band data as a disordered vector in the process of feature extraction, which abandons the exploitation of its internal spectral correlations. To address these issues, we propose a novel spectral-spatial network classification framework based on multi-scale dense connected convolutional network (DenseNet) and bi-direction recurrent neural network (Bi-RNN) with attention mechanism network (MDRN). For the proposed MDRN, in terms of spatial feature extraction, a multi-scale DenseNet is exploited to combine shallow and deep convolution features to extract the multi-scale and complex spatial structure features at each layer. In the aspects of spectral feature extraction, Bi-RNN with attention mechanism is used to capture the inner spectral correlations within a continuous spectrum. Three standard real hyperspectral datasets were used to verify the effectiveness of the proposed MDRN approach. Experimental results indicate that the proposed MDRN method can make full use of the spectral and spatial information of the image, and it has better performance than some advanced algorithms in HSI classification. Finally, in the application of hyperspectral data captured by Gaofen-5 (GF-5) satellite, the practicability of the proposed MDRN method is also superior to other methods.

A deep-learning-based feature extraction has recently been proposed for HyperSpectral Images (HSI) classification. A Deep Belief Network (DBN), as part of deep learning, has been used in HSI classification for deep and abstract feature extraction. However, DBN has to simultaneously deal with hundreds of features from the HSI hyper-cube, which results into complexity and leads to limited feature abstraction and performance in the presence of limited training data. Moreover, a dimensional-reduction-based solution to this issue results in the loss of valuable spectral information, thereby affecting classification performance. To address the issue, this paper presents a Spectral-Adaptive Segmented DBN (SAS-DBN) for spectral-spatial HSI classification that exploits the deep abstract features by segmenting the original spectral bands into small sets/groups of related spectral bands and processing each group separately by using local DBNs. Furthermore, spatial features are also incorporated by first applying hyper-segmentation on the HSI. These results improved data abstraction with reduced complexity and enhanced the performance of HSI classification. Local application of DBN-based feature extraction to each group of bands reduces the computational complexity and results in better feature extraction improving classification accuracy. In general, exploiting spectral features effectively through a segmented-DBN process and spatial features through hyper-segmentation and integration of spectral and spatial features for HSI classification has a major effect on the performance of HSI classification. Experimental evaluation of the proposed technique on well-known HSI standard data sets with different contexts and resolutions establishes the efficacy of the proposed techniques, wherein the results are comparable to several recently proposed HSI classification techniques.