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[IEEE 2018 Second International Conference on Advances in Electronics, Computers and Communications (ICAECC) - Bangalore (2018.2.9-2018.2.10)] 2018 Second International Conference on Advances in Electronics, Computers and Communications (ICAECC) - Sparse Reconstruction of Hyperspectral Image using Bregman Iterations
摘要： Hyperspectral image processing plays an important role in satellite communication. Hyperspectral Image (HSI) processing requires very high ‘computational resources’ in terms of computational time and storage due to extremely large volumes of data collected by imaging spectrometers on-board the satellite. The bandwidth available to transmit the image data from satellite to the ground station is limited. As a result, Hyperspectral image compression is an active research area in the research community in past few years. The research work in the paper proposes a new scheme, Sparsification of HSI and reconstruction (SHSIR) for the reconstruction of hyperspectral image data acquired in Compressive sensing (CS) fashion. Compressed measurements similar to compressive sensing acquisition are generated using measurement matrices containing gaussian i.i.d entries. Now the reconstruction is solving the constrained optimization problem with non smooth terms. Adaptive Bregman iterations method of multipliers is used to convert the difficult optimization problem into a simple cyclic sequence problem. Experimental results from research work indicates that the proposed method performs better than the other existing techniques.
关键词： SHSIR algorithm,Hyperspectral image (HSI),Compressive sensing (CS)
更新于2025-09-23 15:22:29
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Discriminative Transfer Joint Matching for Domain Adaptation in Hyperspectral Image Classification
摘要： Domain adaptation, which aims at learning an accurate classifier for a new domain (target domain) using labeled information from an old domain (source domain), has shown promising value in remote sensing fields yet still been a challenging problem. In this letter, we focus on knowledge transfer between hyperspectral remotely sensed images in the context of land-cover classification under unsupervised setting where labeled samples are available only for the source image. Specifically, a discriminative transfer joint matching (DTJM) method is proposed, which matches source and target features in the kernel principal component analysis space by minimizing the empirical maximum mean discrepancy, performs instance reweighting by imposing an l2,1-norm on the embedding matrix, and preserves the local manifold structure of data from different domains and meanwhile maximizes the dependence between the embedding and labels. The proposed approach is compared with some state-of-the-art feature extraction techniques with and without using label information of source data. Experimental results on two benchmark hyperspectral data sets show the effectiveness of the proposed DTJM.
关键词： hyperspectral image classification,domain adaptation,Discriminative transfer joint matching (DTJM),manifold
更新于2025-09-23 15:22:29
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Hyperspectral Coastal Wetland Classification Based on a Multiobject Convolutional Neural Network Model and Decision Fusion
摘要： The phenomenon of spectral aliasing exists for coastal wetland object types, which leads to class mixing. This letter proposes a multiobject convolutional neural network (CNN) decision fusion classification method for hyperspectral images of coastal wetlands. This method adopts decision fusion based on fuzzy membership rules applied to single-object CNN classification to obtain higher classification accuracy. Experimental results demonstrate the effectiveness of the proposed method for the six object types, including water, tidal flat, reed, and other vegetation types. The overall accuracy of the decision fusion classification method based on fuzzy membership is 82.11%, which is 3.33% and 6.24% higher than those of single-object feature band CNN and support vector machine methods. The classification method based on multiobject CNN decision fusion inherits the characteristics of single-object feature bands of the CNN, making it a practical approach to image classification under the challenging conditions in which class mixing occurs.
关键词： decision fusion,convolutional neural network (CNN),hyperspectral image,Classification
更新于2025-09-23 15:22:29
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Domain Adaptation With Discriminative Distribution and Manifold Embedding for Hyperspectral Image Classification
摘要： Hyperspectral remote sensing image classification has drawn a great attention in recent years due to the development of remote sensing technology. To build a high confident classifier, the large number of labeled data is very important, e.g., the success of deep learning technique. Indeed, the acquisition of labeled data is usually very expensive, especially for the remote sensing images, which usually needs to survey outside. To address this problem, in this letter, we propose a domain adaptation method by learning the manifold embedding and matching the discriminative distribution in source domain with neural networks for hyperspectral image classification. Specifically, we use the discriminative information of source image to train the classifier for the source and target images. To make the classifier can work well on both domains, we minimize the distribution shift between the two domains in an embedding space with prior class distribution in the source domain. Meanwhile, to avoid the distortion mapping of the target domain in the embedding space, we try to keep the manifold relation of the samples in the embedding space. Then, we learn the embedding on source domain and target domain by minimizing the three criteria simultaneously based on a neural network. The experimental results on two hyperspectral remote sensing images have shown that our proposed method can outperform several baseline methods.
关键词： neural network,hyperspectral image classification,maximum mean discrepancy (MMD),remote sensing,Domain adaptation,manifold embedding
更新于2025-09-23 15:22:29
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Deep Belief Network for Spectral–Spatial Classification of Hyperspectral Remote Sensor Data
摘要： With the development of high-resolution optical sensors, the classification of ground objects combined with multivariate optical sensors is a hot topic at present. Deep learning methods, such as convolutional neural networks, are applied to feature extraction and classification. In this work, a novel deep belief network (DBN) hyperspectral image classification method based on multivariate optical sensors and stacked by restricted Boltzmann machines is proposed. We introduced the DBN framework to classify spatial hyperspectral sensor data on the basis of DBN. Then, the improved method (combination of spectral and spatial information) was verified. After unsupervised pretraining and supervised fine-tuning, the DBN model could successfully learn features. Additionally, we added a logistic regression layer that could classify the hyperspectral images. Moreover, the proposed training method, which fuses spectral and spatial information, was tested over the Indian Pines and Pavia University datasets. The advantages of this method over traditional methods are as follows: (1) the network has deep structure and the ability of feature extraction is stronger than traditional classifiers; (2) experimental results indicate that our method outperforms traditional classification and other deep learning approaches.
关键词： classification,feature extraction,multi-sensor fusion,remote sensors,deep learning,hyperspectral image
更新于2025-09-23 15:22:29
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GPU Acceleration of Clustered DPCM for Lossless Compression of Hyperspectral Images
摘要： With the development of remote sensing technology, spatial and spectral resolutions of hyperspectral images have become increasingly dense. In order to overcome difficulties in the storage, transmission and manipulation of hyperspectral images, an effective compression algorithm is requisite. The Clustered Differential Pulse Code Modulation (C-DPCM), which is a prediction-based hyperspectral lossless compression algorithm, can achieve a relatively high compression ratio, but its efficiency still requires improvement. This paper presents a parallel implementation of the C-DPCM algorithm on Graphics Processing Units (GPUs) with the Compute Unified Device Architecture (CUDA), which is a parallel computing platform and programming model developed by NVIDIA. Three optimization strategies are utilized to implement the C-DPCM algorithm in parallel, including a version that uses shared memory and registers, a version that employs multi-stream, and a version that uses multi-GPU. In addition, we studied how to assign all classes to each GPU to minimize the processing time. Finally, we reduced the compression time from approximately half an hour to an hour to several seconds, with almost no loss in accuracy.
关键词： C-DPCM,GPU,CUDA,Hyperspectral image lossless compression
更新于2025-09-23 15:22:29
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Nonlocal Patch Tensor Sparse Representation for Hyperspectral Image Super-Resolution
摘要： This paper presents a hyperspectral image (HSI) super-resolution method which fuses a low-resolution hyperspectral image (LR-HSI) with a high-resolution multispectral image (HR-MSI) to get high-resolution HSI (HR-HSI). The proposed method first extracts the nonlocal similar patches to form a nonlocal patch tensor (NPT). A novel tensor-tensor product (t-product) based tensor sparse representation is proposed to model the extracted NPTs. Through the tensor sparse representation, both the spectral and spatial similarities between the nonlocal similar patches are well preserved. Then, the relationship between the HR-HSI and LR-HSI is built using t-product which allows us to design a unified objective function to incorporate the nonlocal similarity, tensor dictionary learning, and tensor sparse coding together. Finally, Alternating Direction Method of Multipliers (ADMM) is used to solve the optimization problem. Experimental results on three data sets and one real data set demonstrate that the proposed method substantially outperforms the existing state-of-the-art HSI super-resolution methods.
关键词： tensor dictionary learning,Hyperspectral image,nonlocal patch tensor,tensor sparse coding,super-resolution
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Compression of Hyperspectral Images Using Luminance Transform and 3D-DCT
摘要： DCT based transform techniques are popular in image compression. In this paper, luminance transform is applied to improve the compression performance of 3-D discrete cosine transform (3D-DCT) in hyperspectral images. The proposed scheme consists of two main steps. Firstly, luminance transform is performed on spectral band groups taking the first band image in a group as the reference. The aim of using luminance transform is to reduce the brightness and contrast difference within spectral band groups. Secondly, compression is performed by 3D-DCT followed by entropy encoding. The performance of the proposed approach is compared to 3D-DCT in terms of signal-to-noise ratio (SNR) and mean spectral angle (MSA). It is observed that applying luminance transform before 3D-DCT provides better results especially at low bit-rates.
关键词： hyperspectral image compression,luminance transform,3D-DCT
更新于2025-09-23 15:21:21
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Hyperspectral image classification via compact-dictionary-based sparse representation
摘要： In this paper, a compact-dictionary-based sparse representation (CDSR) method is proposed for hyperspectral image (HSI) classification. The proposed dictionary in CDSR is dynamically generated according to the spatial and spectral context of each pixel. It can effectively shrink the decision range for classification, and reduce the computational burden since the compact dictionary is composed of the classes correlated with the target pixel in terms of spatial location and spectral information. In order to obtain better spatial context information, a spatial location expanding strategy is designed for spreading local explicit label information to a wider region. Experimental results demonstrate the effectiveness and superiority of the proposed method when compared with some widely used HSI classification approaches.
关键词： Compact dictionary,Hyperspectral image,Sparse representation,Classification
更新于2025-09-23 15:21:21
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Hierarchical Sub-Pixel Anomaly Detection Framework for Hyperspectral Imagery
摘要： Anomaly detection is an important task in hyperspectral processing. Some previous works, based on statistical information, focus on Reed-Xiaoli (RX), as it is one of the most classical and commonly used methods. However, its performance tends to be affected when anomaly target size is smaller than spatial resolution. Those sub-pixel anomaly target spectra are usually much similar with background spectra, and may results in false alarm for traditional RX method. To address this issue, this paper proposes a hierarchical RX (H-RX) anomaly detection framework to enhance the performance. The proposed H-RX method consists of several different layers of original RX anomaly detector. In each layer, the RX’s output of each pixel is restrained by a nonlinear function and then imposed as a coef?cient on its spectrum for the next iteration. Furthermore, we design a spatial regularization layer to enhance the sub-pixel anomaly detection performance. To better illustrate the hierarchical framework, we provide a theoretical explanation of the hierarchical background spectra restraint and regularization process. Extensive experiments on three hyperspectral images illustrate that the proposed anomaly detection algorithm outperforms the original RX algorithm and some other classical methods.
关键词： hyperspectral image (HSI) analysis,RX,hierarchical structure,anomaly detection
更新于2025-09-23 15:21:01