Key Laboratory for Satellite Mapping Technology and Applications of National Administration of Surveying

Nanjing, China

Key Laboratory for Satellite Mapping Technology and Applications of National Administration of Surveying

Nanjing, China
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Du P.,Key Laboratory for Satellite Mapping Technology and Applications of National Administration of Surveying | Du P.,Nanjing University | Xue Z.,Key Laboratory for Satellite Mapping Technology and Applications of National Administration of Surveying | Xue Z.,Nanjing University | And 2 more authors.
IEEE Journal on Selected Topics in Signal Processing | Year: 2015

In sparse representation (SR) driven hyperspectral image classification, signal-to-reconstruction rule-based classification may lack generalization performance. In order to overcome this limitation, we presents a new method for discriminative sparse representation of hyperspectral data by learning a reconstructive dictionary and a discriminative classifier in a SR model regularized with total variation (TV). The proposed method features the following components. First, we adopt a spectral unmixing by variable splitting augmented Lagrangian and TV method to guarantee the spatial homogeneity of sparse representations. Second, we embed dictionary learning in the method to enhance the representative power of sparse representations via gradient descent in a class-wise manner. Finally, we adopt a sparse multinomial logistic regression (SMLR) model and design a class-oriented optimization strategy to obtain a powerful classifier, which improves the performance of the learnt model for specific classes. The first two components are beneficial to produce discriminative sparse representations. Whereas, adopting SMLR allows for effectively modeling the discriminative information. Experimental results with both simulated and real hyperspectral data sets in a number of experimental comparisons with other related approaches demonstrate the superiority of the proposed method. © 2007-2012 IEEE.


Xie X.,Key Laboratory for Satellite Mapping Technology and Applications of National Administration of Surveying | Xie X.,Nanjing University | Du P.,Key Laboratory for Satellite Mapping Technology and Applications of National Administration of Surveying | Du P.,Nanjing University | And 5 more authors.
IEEE Geoscience and Remote Sensing Letters | Year: 2015

The quantitative estimation of the fractional cover of carbonate rock (CR) is critical for natural resource management and ecological conservation in karst areas. Based on the analysis of spectral properties of CR together with other land cover types, we proposed two CR indices (CRIs) and established the model that represents the relationships between the CRIs and the fractional cover of CR. Then, the fractional cover of CR was estimated by using the developed model. Experimental results on Landsat-8 Operational Land Imager images acquired at Southwestern China demonstrated the effectiveness of the developed model. Compared with other indices, the proposed CRIs show the highest correlations with the fractional cover of CR. © 2004-2012 IEEE.

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