Hyperspectral Unmixing Based on Spectral and Sparse Deep Convolutional Neural Networks

Wan, Lulu; Chen, Tao; Cai, Haojie

doi:10.1109/jstars.2021.3126755

Cited by 12 publications

(3 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, several methods employing deep neural networks were proposed to address the hyperspectral mixing problem in a supervised scenario [32,33,[57][58][59][60][61][62][63][64][65]. Most of these supervised unmixing algorithms utilize CNN structures.…”

Section: Related Workmentioning

confidence: 99%

Beyond Pixel-Wise Unmixing: Spatial–Spectral Attention Fully Convolutional Networks for Abundance Estimation

Huang,

Zhang

2023

Remote Sensing

View full text Add to dashboard Cite

Spectral unmixing poses a significant challenge within hyperspectral image processing, traditionally addressed by supervised convolutional neural network (CNN)-based approaches employing patch-to-pixel (pixel-wise) methods. However, such pixel-wise methodologies often necessitate image splitting into overlapping patches, resulting in redundant computations and potential information leakage between training and test samples, consequently yielding overoptimistic outcomes. To overcome these challenges, this paper introduces a novel patch-to-patch (patch-wise) framework with nonoverlapping splitting, mitigating the need for repetitive calculations and preventing information leakage. The proposed framework incorporates a novel neural network structure inspired by the fully convolutional network (FCN), tailored for patch-wise unmixing. A highly efficient band reduction layer is incorporated to reduce the spectral dimension, and a specialized abundance constraint module is crafted to enforce both the Abundance Nonnegativity Constraint and the Abundance Sum-to-One Constraint for unmixing tasks. Furthermore, to enhance the performance of abundance estimation, a spatial–spectral attention module is introduced to activate the most informative spatial areas and feature maps. Extensive quantitative experiments and visual assessments conducted on two synthetic datasets and three real datasets substantiate the superior performance of the proposed algorithm. Significantly, the method achieves an impressive RMSE loss of 0.007, which is at least 4.5 times lower than that of other baselines on Urban hyperspectral images. This outcome demonstrates the effectiveness of our approach in addressing the challenges of spectral unmixing.

show abstract

Section: Related Workmentioning

confidence: 99%

Beyond Pixel-Wise Unmixing: Spatial–Spectral Attention Fully Convolutional Networks for Abundance Estimation

Huang,

Zhang

2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…After entering the new century, numerous powerful deep learning (DL) models have been launched and put to scientific use. As the most mature and widely applied DL model, the convolutional neural network (CNN) has exceptional advantages in high-level feature extraction and representation [23], which make up for the defects of traditional ML models [24]. CNN has now been widely used in the geoscience domain, such as scene classification [25], land-cover classification [26,27], change detection [28,29] and ground target detection [30].…”

Section: Introductionmentioning

confidence: 99%

Susceptibility-Guided Landslide Detection Using Fully Convolutional Neural Network

Chen

Ming

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Automatic landslide detection based on very high spatial resolution (VHR) remote sensing images is crucial for disaster prevention and mitigation applications. With the rapid development of deep learning techniques, state-of-the-art semantic segmentation methods based on fully convolutional network (FCNN) have achieved outstanding performance in the landslide detection task. However, most of the existing studies only utilize visual features. Even if the advanced FCNN models are applied, there is still a certain amount of falsely detected and miss detected landslides. In this article, we innovatively introduce landslide susceptibility as prior knowledge and propose an innovative susceptibility-guided landslide detection method based on FCNN (SG-FCNN) to detect landslides from single temporal images. In addition, an unsupervised change detection method based on the mean changing magnitude of objects (MCMO) is further proposed and integrated with the SG-FCNN to detect newly occurred landslides from bi-temporal images. The effectiveness of the proposed SG-FCNN and MCMO has been tested in Lantau Island, Hong Kong. The experimental results show that the SG-FCNN can significantly reduce the amount of falsely detected and miss detected landslides compared with the FCNN. It can conclude that applying landslide susceptibility as prior knowledge is much more effective than using visual features only, which introduces a new methodology of landslide detection and lifts the detection performance to a new level.

show abstract

“…Auto Associative NN combined with Multilayer Perceptron have been proposed in [12]. Recent works focus on CNN architectures to solve the problem of abundance and endmember inference [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Proportion Inference Using Deep Neural Networks. Applications to X-Ray Diffraction and Hyperspectral Imaging

Simonnet,

Fall,

Galerne

et al. 2023

2023 31st European Signal Processing Conference (EUSIPCO)

View full text Add to dashboard Cite

Deep learning is considered as a disruptive method in the field of mineralogy and hyperspectral imaging. Many techniques exist to gain mineralogical information. Amongst them powder X-Ray diffraction (XRD) is very popular and powerful, while hyperspectral imaging is used in many applications such as Earth observation. A key issue for both XRD and hyperspectral imaging is not only to identify the endmembers constituting a mixture but also quantify the abundance of each endmember. In this study, we propose completly novel neural network (NN) training losses specifically designed for proportion inference. Extensive experiments illustrate that the proposed approach allows validated NN architectures to be trained to infer accurately on proportions.

show abstract

Hyperspectral Unmixing Based on Spectral and Sparse Deep Convolutional Neural Networks

Cited by 12 publications

References 43 publications

Beyond Pixel-Wise Unmixing: Spatial–Spectral Attention Fully Convolutional Networks for Abundance Estimation

Beyond Pixel-Wise Unmixing: Spatial–Spectral Attention Fully Convolutional Networks for Abundance Estimation

Susceptibility-Guided Landslide Detection Using Fully Convolutional Neural Network

Proportion Inference Using Deep Neural Networks. Applications to X-Ray Diffraction and Hyperspectral Imaging

Contact Info

Product

Resources

About