scholarly journals Superpixel Spectral Unmixing for Hyperspectral Image Superresolution Using a Coupled Encoder-Decoder Network

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Shao-lei Zhang ◽  
Guang-yuan Fu ◽  
Hong-qiao Wang ◽  
Yu-qing Zhao
Keyword(s):  
Hyperspectral Image ◽  
Visual Quality ◽  
Spectral Unmixing ◽  
The Other ◽  
Hyperspectral Images ◽  
Low Rank ◽  
Spectral Information ◽  
Multispectral Images ◽  
Self Similarity ◽  
Rank Constraint

In this paper, we propose a novel hyperspectral image superresolution method based on superpixel spectral unmixing using a coupled encoder-decoder network. The hyperspectral image and multispectral images are fused to generate high-resolution hyperspectral images through the spectral unmixing framework with low-rank constraint. Specifically, the endmember and abundance information is extracted via a coupled encoder-decoder network integrating the priori for unmixing. The coupled network consists of two encoders and one shared decoder, where spectral information is preserved through the encoder. The multispectral image is clustered into superpixels to explore self-similarity, and then, the superpixels are unmixed to obtain an abundance matrix. By imposing a low-rank constraint on the abundance matrix, we further improve the superresolution performance. Experiments on the CAVE and Harvard datasets indicate that our superresolution method outperforms the other compared methods in terms of quantitative evaluation and visual quality.

scholarly journals Leveraging colour-based pseudo-labels to supervise saliency detection in hyperspectral image datasets

2021 ◽  
Author(s):  
Annalisa Appice ◽  
Angelo Cannarile ◽  
Antonella Falini ◽  
Donato Malerba ◽  
Francesca Mazzia ◽  
...  
Keyword(s):  
Visual Attention ◽  
Hyperspectral Image ◽  
Saliency Detection ◽  
Hyperspectral Images ◽  
Spectral Information ◽  
Imaging Analysis ◽  
Analysis Task ◽  
Visual Attention Mechanism ◽  
Land Use Monitoring ◽  
Research Resources

AbstractSaliency detection mimics the natural visual attention mechanism that identifies an imagery region to be salient when it attracts visual attention more than the background. This image analysis task covers many important applications in several fields such as military science, ocean research, resources exploration, disaster and land-use monitoring tasks. Despite hundreds of models have been proposed for saliency detection in colour images, there is still a large room for improving saliency detection performances in hyperspectral imaging analysis. In the present study, an ensemble learning methodology for saliency detection in hyperspectral imagery datasets is presented. It enhances saliency assignments yielded through a robust colour-based technique with new saliency information extracted by taking advantage of the abundance of spectral information on multiple hyperspectral images. The experiments performed with the proposed methodology provide encouraging results, also compared to several competitors.

scholarly journals A Distributed Parallel Algorithm Based on Low-Rank and Sparse Representation for Anomaly Detection in Hyperspectral Images

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3627 ◽  
Author(s):  
Yi Zhang ◽  
Zebin Wu ◽  
Jin Sun ◽  
Yan Zhang ◽  
Yaoqin Zhu ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Sparse Representation ◽  
Parallel Algorithm ◽  
Hyperspectral Image ◽  
Hyperspectral Images ◽  
Low Rank ◽  
Detection Methods ◽  
Matrix Computations ◽  
Hyperspectral Image Processing ◽  
Mapreduce Model

Anomaly detection aims to separate anomalous pixels from the background, and has become an important application of remotely sensed hyperspectral image processing. Anomaly detection methods based on low-rank and sparse representation (LRASR) can accurately detect anomalous pixels. However, with the significant volume increase of hyperspectral image repositories, such techniques consume a significant amount of time (mainly due to the massive amount of matrix computations involved). In this paper, we propose a novel distributed parallel algorithm (DPA) by redesigning key operators of LRASR in terms of MapReduce model to accelerate LRASR on cloud computing architectures. Independent computation operators are explored and executed in parallel on Spark. Specifically, we reconstitute the hyperspectral images in an appropriate format for efficient DPA processing, design the optimized storage strategy, and develop a pre-merge mechanism to reduce data transmission. Besides, a repartitioning policy is also proposed to improve DPA’s efficiency. Our experimental results demonstrate that the newly developed DPA achieves very high speedups when accelerating LRASR, in addition to maintaining similar accuracies. Moreover, our proposed DPA is shown to be scalable with the number of computing nodes and capable of processing big hyperspectral images involving massive amounts of data.

scholarly journals Visualizing Near Infrared Hyperspectral Images with Generative Adversarial Networks

2020 ◽  
Vol 12 (23) ◽  
pp. 3848
Author(s):  
Rongxin Tang ◽  
Hualin Liu ◽  
Jingbo Wei
Keyword(s):  
Near Infrared ◽  
Hyperspectral Image ◽  
Geographic Location ◽  
Hyperspectral Images ◽  
Generative Adversarial Networks ◽  
Landsat 8 ◽  
Multispectral Images ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Visualization Algorithms

The visualization of near infrared hyperspectral images is valuable for quick view and information survey, whereas methods using band selection or dimension reduction fail to produce good colors as reasonable as corresponding multispectral images. In this paper, an end-to-end neural network of hyperspectral visualization is proposed, based on the convolutional neural networks, to transform a hyperspectral image of hundreds of near infrared bands to a three-band image. Supervised learning is used to train the network where multispectral images are targeted to reconstruct naturally looking images. Each pair of the training images shares the same geographic location and similar moments. The generative adversarial framework is used with an adversarial network to improve the training of the generating network. In the experimental procedure, the proposed method is tested for the near infrared bands of EO-1 Hyperion images with LandSat-8 images as the benchmark, which is compared with five state-of-the-art visualization algorithms. The experimental results show that the proposed method performs better in producing naturally looking details and colors for near infrared hyperspectral images.

scholarly journals SLRL4D: Joint Restoration of Subspace Low-Rank Learning and Non-Local 4-D Transform Filtering for Hyperspectral Image

2020 ◽  
Vol 12 (18) ◽  
pp. 2979
Author(s):  
Le Sun ◽  
Chengxun He ◽  
Yuhui Zheng ◽  
Songze Tang
Keyword(s):  
Hyperspectral Image ◽  
Dimensional Subspace ◽  
Low Rank ◽  
Spatial Correlations ◽  
Self Similarity ◽  
Multiple Datasets ◽  
Alternating Direction ◽  
Non Local ◽  
Low Dimensional ◽  
Spatial Redundancy

During the process of signal sampling and digital imaging, hyperspectral images (HSI) inevitably suffer from the contamination of mixed noises. The fidelity and efficiency of subsequent applications are considerably reduced along with this degradation. Recently, as a formidable implement for image processing, low-rank regularization has been widely extended to the restoration of HSI. Meanwhile, further exploration of the non-local self-similarity of low-rank images are proven useful in exploiting the spatial redundancy of HSI. Better preservation of spatial-spectral features is achieved under both low-rank and non-local regularizations. However, existing methods generally regularize the original space of HSI, the exploration of the intrinsic properties in subspace, which leads to better denoising performance, is relatively rare. To address these challenges, a joint method of subspace low-rank learning and non-local 4-d transform filtering, named SLRL4D, is put forward for HSI restoration. Technically, the original HSI is projected into a low-dimensional subspace. Then, both spectral and spatial correlations are explored simultaneously by imposing low-rank learning and non-local 4-d transform filtering on the subspace. The alternating direction method of multipliers-based algorithm is designed to solve the formulated convex signal-noise isolation problem. Finally, experiments on multiple datasets are conducted to illustrate the accuracy and efficiency of SLRL4D.

scholarly journals ADVANCES IN HYPERSPECTRAL AND MULTISPECTRAL IMAGE FUSION AND SPECTRAL UNMIXING

2015 ◽  
Vol XL-3/W3 ◽  
pp. 451-458 ◽  
Author(s):  
C. Lanaras ◽  
E. Baltsavias ◽  
K. Schindler
Keyword(s):  
Image Fusion ◽  
Hyperspectral Image ◽  
Mathematical Optimization ◽  
Spectral Unmixing ◽  
Semantic Interpretation ◽  
Multispectral Images ◽  
Hyperspectral Unmixing ◽  
Environmental Mapping ◽  
Fused Image ◽  
Linear Spectral Unmixing

In this work, we jointly process high spectral and high geometric resolution images and exploit their synergies to (a) generate a fused image of high spectral and geometric resolution; and (b) improve (linear) spectral unmixing of hyperspectral endmembers at subpixel level w.r.t. the pixel size of the hyperspectral image. We assume that the two images are radiometrically corrected and geometrically co-registered. The scientific contributions of this work are (a) a simultaneous approach to image fusion and hyperspectral unmixing, (b) enforcing several physically plausible constraints during unmixing that are all well-known, but typically not used in combination, and (c) the use of efficient, state-of-the-art mathematical optimization tools to implement the processing. The results of our joint fusion and unmixing has the potential to enable more accurate and detailed semantic interpretation of objects and their properties in hyperspectral and multispectral images, with applications in environmental mapping, monitoring and change detection. In our experiments, the proposed method always improves the fusion compared to competing methods, reducing RMSE between 4% and 53%.

scholarly journals Hyperspectral Image Classification Based on Sparse Superpixel Graph

2021 ◽  
Vol 13 (18) ◽  
pp. 3592
Author(s):  
Yifei Zhao ◽  
Fengqin Yan
Keyword(s):  
Classification Accuracy ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Hyperspectral Images ◽  
Spectral Information ◽  
Hyperspectral Image Classification ◽  
Computational Burden ◽  
Large Size ◽  
High Classification Accuracy ◽  
Good Classification

Hyperspectral image (HSI) classification is one of the major problems in the field of remote sensing. Particularly, graph-based HSI classification is a promising topic and has received increasing attention in recent years. However, graphs with pixels as nodes generate large size graphs, thus increasing the computational burden. Moreover, satisfactory classification results are often not obtained without considering spatial information in constructing graph. To address these issues, this study proposes an efficient and effective semi-supervised spectral-spatial HSI classification method based on sparse superpixel graph (SSG). In the constructed sparse superpixels graph, each vertex represents a superpixel instead of a pixel, which greatly reduces the size of graph. Meanwhile, both spectral information and spatial structure are considered by using superpixel, local spatial connection and global spectral connection. To verify the effectiveness of the proposed method, three real hyperspectral images, Indian Pines, Pavia University and Salinas, are chosen to test the performance of our proposal. Experimental results show that the proposed method has good classification completion on the three benchmarks. Compared with several competitive superpixel-based HSI classification approaches, the method has the advantages of high classification accuracy (>97.85%) and rapid implementation (<10 s). This clearly favors the application of the proposed method in practice.

scholarly journals Research and Application of Several Key Techniques in Hyperspectral Image Preprocessing

2021 ◽  
Vol 12 ◽  
Author(s):  
Yu-hang Li ◽  
Xin Tan ◽  
Wei Zhang ◽  
Qing-bin Jiao ◽  
Yu-xing Xu ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Classification Accuracy ◽  
Hyperspectral Image ◽  
Hyperspectral Images ◽  
Classification Model ◽  
Spectral Information ◽  
Convolution Kernel ◽  
Image Correction ◽  
Image Preprocessing ◽  
Segmentation Image

This paper focuses on image segmentation, image correction and spatial-spectral dimensional denoising of images in hyperspectral image preprocessing to improve the classification accuracy of hyperspectral images. Firstly, the images were filtered and segmented by using spectral angle and principal component analysis, and the segmented results are intersected and then used to mask the hyperspectral images. Hyperspectral images with a excellent segmentation result was obtained. Secondly, the standard reflectance plates with reflectance of 2 and 98% were used as a priori spectral information for image correction of samples with known true spectral information. The mean square error between the corrected and calibrated spectra is less than 0.0001. Comparing with the black-and-white correction method, the classification model constructed based on this method has higher classification accuracy. Finally, the convolution kernel of the one-dimensional Savitzky-Golay (SG) filter was extended into a two-dimensional convolution kernel to perform joint spatial-spectral dimensional filtering (TSG) on the hyperspectral images. The SG filter (m = 7,n = 3) and TSG filter (m = 3,n = 4) were applied to the hyperspectral image of Pavia University and the quality of the hyperspectral image was evaluated. It was found that the TSG filter retained most of the original features while the noise information of the filtered hyperspectral image was less. The hyperspectral images of sample 1–1 and sample 1–2 were processed by the image segmentation and image correction methods proposed in this paper. Then the classification models based on SG filtering and TSG filtering hyperspectral images were constructed, respectively. The results showed that the TSG filter-based model had higher classification accuracy and the classification accuracy is more than 98%.

Sign in / Sign up

Export Citation Format

Share Document