Blind spectral unmixing based on sparse component analysis for hyperspectral remote sensing imagery

Zhong, Yanfei; Zhao, Lin; Feng, Ruyi; Zhang, Liangpei; Xu, Yinghui

doi:10.1016/j.isprsjprs.2016.04.008

Cited by 68 publications

(23 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To study the effect of the SPP layer number of the proposed pre-trained AlexNet-SPP-SS model for the UC Merced dataset, the Google image dataset of SIRI-WHU dataset, and the WHU-RS dataset, the other parameters generated by the pre-trained AlexNet and SS strategy were kept the same. The number of SPP layers was then varied over the range of [1][2][3][4] for the proposed pre-trained AlexNet-SPP-SS model.…”

Section: Discussionmentioning

confidence: 99%

“…With the recent launch of remote sensing satellites around the world, a large volume of multi-level, multi-angle, and multi-resolution HSR remote sensing images can now be obtained, where the remote sensing big data brings new understandings for the traditional definition of big data [1][2][3]. These multi-source remote sensing images allow the ground object observation from multiple perspectives.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Pre-Trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification

et al. 2017

Self Cite

View full text Add to dashboard Cite

Abstract:The rapid development of high spatial resolution (HSR) remote sensing imagery techniques not only provide a considerable amount of datasets for scene classification tasks but also request an appropriate scene classification choice when facing with finite labeled samples. AlexNet, as a relatively simple convolutional neural network (CNN) architecture, has obtained great success in scene classification tasks and has been proven to be an excellent foundational hierarchical and automatic scene classification technique. However, current HSR remote sensing imagery scene classification datasets always have the characteristics of small quantities and simple categories, where the limited annotated labeling samples easily cause non-convergence. For HSR remote sensing imagery, multi-scale information of the same scenes can represent the scene semantics to a certain extent but lacks an efficient fusion expression manner. Meanwhile, the current pre-trained AlexNet architecture lacks a kind of appropriate supervision for enhancing the performance of this model, which easily causes overfitting. In this paper, an improved pre-trained AlexNet architecture named pre-trained AlexNet-SPP-SS has been proposed, which incorporates the scale pooling-spatial pyramid pooling (SPP) and side supervision (SS) to improve the above two situations. Extensive experimental results conducted on the UC Merced dataset and the Google Image dataset of SIRI-WHU have demonstrated that the proposed pre-trained AlexNet-SPP-SS model is superior to the original AlexNet architecture as well as the traditional scene classification methods.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pre-Trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…To analyze the sensitivity in relation to parameter NP, the other parameters, i.e., CR and F, were determined adaptively and NP assumed the following values for the experimental images: NP = {5, 10,15,20,25,30,35,40,45, 50}. Figure 10c shows the sensitivity of ODF-ADE in relation to parameter NP by analyzing the relationship between OA and NP.…”

Section: Sensitivity Of Parameter Npmentioning

confidence: 99%

Optimal Decision Fusion for Urban Land-Use/Land-Cover Classification Based on Adaptive Differential Evolution Using Hyperspectral and LiDAR Data

et al. 2017

Self Cite

View full text Add to dashboard Cite

Hyperspectral images and light detection and ranging (LiDAR) data have, respectively, the high spectral resolution and accurate elevation information required for urban land-use/land-cover (LULC) classification. To combine the respective advantages of hyperspectral and LiDAR data, this paper proposes an optimal decision fusion method based on adaptive differential evolution, namely ODF-ADE, for urban LULC classification. In the ODF-ADE framework the normalized difference vegetation index (NDVI), gray-level co-occurrence matrix (GLCM) and digital surface model (DSM) are extracted to form the feature map. The three different classifiers of the maximum likelihood classifier (MLC), support vector machine (SVM) and multinomial logistic regression (MLR) are used to classify the extracted features. To find the optimal weights for the different classification maps, weighted voting is used to obtain the classification result and the weights of each classification map are optimized by the differential evolution algorithm which uses a self-adaptive strategy to obtain the parameter adaptively. The final classification map is obtained after post-processing based on conditional random fields (CRF). The experimental results confirm that the proposed algorithm is very effective in urban LULC classification.

show abstract

“…Hyperspectral images have been widely applied to remote sensing image applications, such as land cover classification [1], target detection [2], anomaly detection [3], spectral unmixing [4] and others. Each pixel in HSI has hundreds of narrow contiguous bands, spanning from visible to infrared spectrum [5], which makes it possible to detect and distinguish various objects with higher accuracy [6].…”

Section: Introductionmentioning

confidence: 99%

Multiscale Union Regions Adaptive Sparse Representation for Hyperspectral Image Classification

Tong

Jiang

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

Sparse Representation has been widely applied to classification of hyperspectral images (HSIs). Besides spectral information, the spatial context in HSIs also plays an important role in the classification. The recently published Multiscale Adaptive Sparse Representation (MASR) classifier has shown good performance in exploiting spatial information for HSI classification. But the spatial information is exploited by multiscale patches with fixed sizes of square windows. The patch can include all nearest neighbor pixels but these neighbor pixels may contain some noise pixels. Then another research proposed a Multiscale Superpixel-Based Sparse Representation (MSSR) classifier. Shape-adaptive superpixels can provide more accurate representation than patches. But it is difficult to select scales for superpixels. Therefore, inspired by the merits and demerits of multiscale patches and superpixels, we propose a novel algorithm called Multiscale Union Regions Adaptive Sparse Representation (MURASR). The union region, which is the overlap of patch and superpixel, can make full use of the advantages of both and overcome the weaknesses of each one. Experiments on several HSI datasets demonstrate that the proposed MURASR is superior to MASR and union region is better than the patch in the sparse representation.

show abstract

Blind spectral unmixing based on sparse component analysis for hyperspectral remote sensing imagery

Cited by 68 publications

References 44 publications

Pre-Trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification

Pre-Trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification

Optimal Decision Fusion for Urban Land-Use/Land-Cover Classification Based on Adaptive Differential Evolution Using Hyperspectral and LiDAR Data

Multiscale Union Regions Adaptive Sparse Representation for Hyperspectral Image Classification

Contact Info

Product

Resources

About