A Neural Network Based Hybrid Mixture Model to Extract Information from Non-linear Mixed Pixels
Signals acquired by sensors in the real world are non-linear combinations, requiring non-linear mixture models to describe the resultant mixture spectra for the endmember’s (pure pixel’s) distribution. This communication discusses inferring class fraction through a novel hybrid mixture model (HMM)....
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MDPI AG
2012-09-01
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| Online Access: | http://www.mdpi.com/2078-2489/3/3/420 |
| _version_ | 1828398459865006080 |
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| author | Uttam Kumar Kumar S. Raja Chiranjit Mukhopadhyay T.V. Ramachandra |
| author_facet | Uttam Kumar Kumar S. Raja Chiranjit Mukhopadhyay T.V. Ramachandra |
| author_sort | Uttam Kumar |
| collection | DOAJ |
| description | Signals acquired by sensors in the real world are non-linear combinations, requiring non-linear mixture models to describe the resultant mixture spectra for the endmember’s (pure pixel’s) distribution. This communication discusses inferring class fraction through a novel hybrid mixture model (HMM). HMM is a three-step process, where the endmembers are first derived from the images themselves using the N-FINDR algorithm. These endmembers are used by the linear mixture model (LMM) in the second step that provides an abundance estimation in a linear fashion. Finally, the abundance values along with the training samples representing the actual ground proportions are fed into neural network based multi-layer perceptron (MLP) architecture as input to train the neurons. The neural output further refines the abundance estimates to account for the non-linear nature of the mixing classes of interest. HMM is first implemented and validated on simulated hyper spectral data of 200 bands and subsequently on real time MODIS data with a spatial resolution of 250 m. The results on computer simulated data show that the method gives acceptable results for unmixing pixels with an overall RMSE of 0.0089 ± 0.0022 with LMM and 0.0030 ± 0.0001 with the HMM when compared to actual class proportions. The unmixed MODIS images showed overall RMSE with HMM as 0.0191 ± 0.022 as compared to the LMM output considered alone that had an overall RMSE of 0.2005 ± 0.41, indicating that individual class abundances obtained from HMM are very close to the real observations. |
| first_indexed | 2024-12-10T09:01:50Z |
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| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-12-10T09:01:50Z |
| publishDate | 2012-09-01 |
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| spelling | doaj.art-4d9aa108bb3c400185831514c3b957802022-12-22T01:55:15ZengMDPI AGInformation2078-24892012-09-013342044110.3390/info3030420A Neural Network Based Hybrid Mixture Model to Extract Information from Non-linear Mixed PixelsUttam KumarKumar S. RajaChiranjit MukhopadhyayT.V. RamachandraSignals acquired by sensors in the real world are non-linear combinations, requiring non-linear mixture models to describe the resultant mixture spectra for the endmember’s (pure pixel’s) distribution. This communication discusses inferring class fraction through a novel hybrid mixture model (HMM). HMM is a three-step process, where the endmembers are first derived from the images themselves using the N-FINDR algorithm. These endmembers are used by the linear mixture model (LMM) in the second step that provides an abundance estimation in a linear fashion. Finally, the abundance values along with the training samples representing the actual ground proportions are fed into neural network based multi-layer perceptron (MLP) architecture as input to train the neurons. The neural output further refines the abundance estimates to account for the non-linear nature of the mixing classes of interest. HMM is first implemented and validated on simulated hyper spectral data of 200 bands and subsequently on real time MODIS data with a spatial resolution of 250 m. The results on computer simulated data show that the method gives acceptable results for unmixing pixels with an overall RMSE of 0.0089 ± 0.0022 with LMM and 0.0030 ± 0.0001 with the HMM when compared to actual class proportions. The unmixed MODIS images showed overall RMSE with HMM as 0.0191 ± 0.022 as compared to the LMM output considered alone that had an overall RMSE of 0.2005 ± 0.41, indicating that individual class abundances obtained from HMM are very close to the real observations.http://www.mdpi.com/2078-2489/3/3/420mixture modelsub-pixel classificationnon-linear unmixingMODIS |
| spellingShingle | Uttam Kumar Kumar S. Raja Chiranjit Mukhopadhyay T.V. Ramachandra A Neural Network Based Hybrid Mixture Model to Extract Information from Non-linear Mixed Pixels Information mixture model sub-pixel classification non-linear unmixing MODIS |
| title | A Neural Network Based Hybrid Mixture Model to Extract Information from Non-linear Mixed Pixels |
| title_full | A Neural Network Based Hybrid Mixture Model to Extract Information from Non-linear Mixed Pixels |
| title_fullStr | A Neural Network Based Hybrid Mixture Model to Extract Information from Non-linear Mixed Pixels |
| title_full_unstemmed | A Neural Network Based Hybrid Mixture Model to Extract Information from Non-linear Mixed Pixels |
| title_short | A Neural Network Based Hybrid Mixture Model to Extract Information from Non-linear Mixed Pixels |
| title_sort | neural network based hybrid mixture model to extract information from non linear mixed pixels |
| topic | mixture model sub-pixel classification non-linear unmixing MODIS |
| url | http://www.mdpi.com/2078-2489/3/3/420 |
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