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LiDAR-aided Spectral Variability Decreasing in Hyperspectral Imagery Based on an Automated Waveband Selection Approach

Yıl 2020, Cilt: 35 Sayı: 4, 983 - 992, 31.12.2020
https://doi.org/10.21605/cukurovaummfd.869160

Öz

Hyperspectral (HS) and Light Detection and Ranging (LiDAR) sensors are the two of the newest remote sensing technologies. In recent decades, hyperspectral unmixing analysis has achieved a great importance in remote sensing applications. Spectral variability can occur in hyperspectral images due to some reasons.
This spectral variability can cause serious abundance estimation errors in hyperspectral image analysis. On the other hand, LiDAR data provides the Digital Surface Model (DSM) data that does not affected by spectral variability. In this study, in order to decrease the spectral variability on hyperspectral imagery, Stable Zone Unmixing (SZU) approach is used by segmenting of LiDAR-DSM information. Experimental results are carried out on simulation and real data sets and spectral variability is reduced in both images.

Kaynakça

  • 1. Hu, Y.H., Lee, H.B., Scarpace, F.L., 1999. Optimal Linear Spectral Unmixing. IEEE, Transactions on Geoscience and Remote Sensing, 37(1), 639 – 644.
  • 2. Keshava, N., Mustard, J.F., 2002. Spectral unmixing. IEEE Signal Processing Magazine,19(1), 44-57.
  • 3. Somers, B., Delalieux, S., Verstraeten, W.W., van Aardt, J.A.N., Albrigo, G.L., Coppin, P., 2010. An Automated Waveband Selection Technique for 2010 Optimized Hyperspectral Mixture Analysis, International Journal of Remote Sensing, 31(20), 5549–5568.
  • 4. Healey, G., Slater, D., 1999. Models and Methods for Automated Material Identification in Hyperspectral Imagery Acquired Under Unknown Illumination and Atmospheric Conditions. IEEE Trans. Geosci. Remote Sensing, 37(6), 2706–2717.
  • 5. Adams, J., Sabol, D., Kapos, V., Filho, R., Roberts, D., Smith, M., Gillespie, A., 1995. Classification of Multispectral Images Based on Fractions of Endmembers: Application to Land-cover Change in the Brazilian Amazon. Remote Sensing Environ., 52(2), 137–154.
  • 6. Drumetz, L., Veganzones, M.A., Henrot, S., Phlypo, R., Chanussot, J., Jutten, C., 2016. Blind Hyperspectral Unmixing Using an Extended Linear Mixing Model to Address Spectral Variability. IEEE Trans. Image Process., 25(8), 3890–3905.
  • 7. Uezato, T., Fauvel, M., Dobigeon, N., 2019. Hyperspectral Unmixing with Spectral Variability Using Adaptive Bundles and Double Sparsity, IEEE Transactions on Geoscience and Remote Sensing 57(6), 3980-3992.
  • 8. Zhang, J., Rivard, B., Sanchez-Azofeifa, A., Castro-Esau, K., 2006. Intra- and Inter-class Spectral Variability of Tropical Tree Species at La Selva, Costa Rica: Implications for species identification using HYDICE imagery. Remote Sensing of Environment, 105, 129−141.
  • 9. Drumetz, L., Chanussot, J., Jutten, C., 2016. Variability of the Endmembers in Spectral Unmixing: Recent Advances. 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 1-5.
  • 10. Somers, B., Asner, G.P., Tits, L., Coppin, P., 2011. Endmember Variability in Spectral Mixture Analysis: A review. Remote Sensing of Environment, Remote Sensing of Environment, 115, 1603–1616.
  • 11. Zare, A., Ho, K.C., 2014. Endmember Variability in Hyperspectral Analysis: Addressing Spectral Variability During Spectral Unmixing. IEEE Signal Processing Magazine, 31(1), 95-104.
  • 12. Theiler, J., Ziemann, A., Matteoli, S., Diani, M., 2019. Spectral Variability of Remotely Sensed Target Materials: Causes, Models, and Strategies for Mitigation and Robust Exploitation. IEEE Geoscience and Remote Sensing Magazine, 7(2), 8-30.
  • 13. Veganzones, M.A., Drumetz, L., Tochon, G., Dalla Mura, M., Plaza, A., Bioucas-Dias, J., Chanussot, J., 2014. A New Extended Linear Mixing Model to Address Spectral Variability. 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 1–4.
  • 14. Thouvenin, P., Dobigeon, N., Tourneret, J., 2016. Hyperspectral Unmixing with Spectral Variability Using a Perturbed Linear Mixing Model. IEEE Transactions on Signal Processing, 64(2), 525–538.
  • 15. Uezato, T., Murphy, R.J., Melkumyan, A., Chlingaryan, A., 2016. A Novel Spectral Unmixing Method Incorporating Spectral Variability Within Endmember Classes. IEEE Transactions on Geoscience and Remote Sensing, 54(5), 2812–2831.
  • 16. H ong, D., Yokoya, N., Chanussot, J., Xiang Zhu, X., 2019. An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing. IEEE Transactions on Image Processing, 28(4), 1923–1938.
  • 17. Uezato, T., Fauvel Ma., Dobigeon, N., 2019. Hyperspectral Unmixing with Spectral Variability Using Adaptive Bundles and Double Sparsity. IEEE Transactions on Geoscience and Remote Sensing, 1–13.
  • 18. D rumetz, L., Meyer, T.R., Chanussot, J., Bertozzi, A.L., Christian, J., 2019. Hyperspectral Image Unmixing with Endmember Bundles and Group Sparsity Inducing Mixed Norms. IEEE Transactions on Image Processing, 28(7), 3435 – 3450.
  • 19. Ibarrola-Ulzurrun, E., Drumetz, L., Marcello, J., Gonzalo-Martín, C., Chanussot, J., 2019. Hyperspectral Classification Through Unmixing Abundance Maps Addressing Spectral Variability. IEEE Transactions on Geoscience and Remote Sensing, 57(7), 4775 – 4788.
  • 20. Borsoi, R.A., Imbiriba, T., Bermudez, J.C.M., 2020. A Data Dependent Multiscale Model for Hyperspectral Unmixing with Spectral Variability, IEEE Transactions on Image Processing, 29, 3638 – 3651.
  • 21. Benhalouche, F.Z., Karoui, M.S., Deville, Y., 2019. An NMF-Based Approach for Hyperspectral Unmixing Using a New Multiplicative-tuning Linear Mixing Model to Address Spectral Variability. 2019 27th European Signal Processing Conference (EUSIPCO), 24-27.
  • 22. Jung, J., Pasolli, E., Prasad, S., Tilton, J., Crawford, M., 2014. A Framework for Land Cover Classification Using Discrete Return LiDAR data: Adopting Pseudo-waveform and Hierarchical Segmentation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(2), 491–502.
  • 23. Uezato, T., Yokoya, N., He, W., 2020. Illumination Invariant Hyperspectral Image Unmixing Based on a Digital Surface Model, IEEE Transactions on Image Processing, 29, 3652 – 3664.
  • 24. Kahraman, S., Bacher, R., Uezato, T., Chanussot, J., Tangel, A., 2019. LiDAR- Guided Reduction of Spectral Variability in Hyperspectral Imagery. 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2019.
  • 25. Somers, B., Delalieux, S., Verstraeten, W.W., van Aardt, J.A.N., Albrigo, G.L., Coppin, P., 2010. An Automated Waveband Selection Technique for Optimized Hyperspectral Mixture Analysis, International Journal of Remote Sensing, 31(20), 5549–5568.
  • 26. Bioucas-Dias, J., Plaza, A., Dobigeon, N., Parente, M., Du, Q., Gader, P., Chanussot, J., 2012. Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-based Approaches. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(2), 354-379.
  • 27. Lee, D., Seung, H., 1999. Learning the Parts of Objects by Non-negative Matrix Factorization. Nature, 401, 788–791.
  • 28. Salembier, P., Garrido, L., 2000. Binary Partition Tree as an Efficient Representation for Image Processing, Segmentation, and Information Retrieval, IEEE Trans. Image Process., Apr., 9(4), 561–576.
  • 29. Tu, T.N., Chen, C.H., Wu, J.L., Chang, C.I., 1998. A Fast Two-stage Classification Method for High-dimensional Remote Sensing Data, IEEE Transactions on Geoscience and Remote Sensing, 36, 182–191.
  • 30. As ner, G.P., Lobell, D.B., 2000. A Biogeophysical Approach for Automated SWIR Unmixing of Soils and Vegetation, Remote Sensing of Environment, 74, 99–112.
  • 31. Uezato, T., Murphy, R.J., Melkumyan, A., Chlingaryan, A., 2016. A Novel Endmember Bundle Extraction and Clustering Approach for Capturing Spectral Variability Within Endmember Classes, IEEE Trans. Geosci. Remote Sens., 54(11), 6712 – 6731.
  • 32. http://www.lx.it.pt/~bioucas/code.htm.
  • 33. Drumetz, L., Veganzones, M.A., Henrot, S., Phlypo, R., Chanussot, J., Jutten, C., 2016. Blind Hyperspectral Unmixing Using an Extended Linear Mixing Model to Address Spectral Variability, IEEE Transactions on Image Processing, 25(8), 3890 – 3905.

LiDAR Verisi Yardımıyla Otomatik Dalga Boyu Bandı Yaklaşımı Kullanılarak Hiperspektral Görüntülerde Spektral Değişkenliğin Azaltılması

Yıl 2020, Cilt: 35 Sayı: 4, 983 - 992, 31.12.2020
https://doi.org/10.21605/cukurovaummfd.869160

Öz

Hiperspektral (HS) ve kızılötesi (Light Detection and Ranging-LiDAR) algılayıcıları en yeni uzaktan algılama teknolojilerindendir. Son yıllarda, hiperspektral karışım giderimi analizi uzaktan algılama uygulamalarında büyük bir önem kazanmıştır. Spektral değişkenlik hiperspektral görüntülerde bazı nedenlerden dolayı meydana gelebilmektedir. Bu spektral değişkenlik hiperspektral görüntü analizinde ciddi bolluk değeri tahminleme hatalarına sebep olabilmektedir. LiDAR algılayıcısı spektral değişkenlikten etkilenmeyen Dijital Yüzey Modeli (DSM) bilgisini sunmaktadır. Bu çalışmada, hiperspektral görüntülerde spektral değişkenliği azaltmak için Kararlı Bölge Karışım Giderimi (Stable Zone Unmixing–SZU) yaklaşımı LiDAR-DSM verisinin kümeleme bilgisi kullanılarak uygulanmıştır.
Deneysel çalışmalar simulasyon ve gerçek veri setleri üzerinde gerçekleştirilmiş ve spektral değişkenliğin her iki veri setinde de azaltıldığı görülmüştür.

Kaynakça

  • 1. Hu, Y.H., Lee, H.B., Scarpace, F.L., 1999. Optimal Linear Spectral Unmixing. IEEE, Transactions on Geoscience and Remote Sensing, 37(1), 639 – 644.
  • 2. Keshava, N., Mustard, J.F., 2002. Spectral unmixing. IEEE Signal Processing Magazine,19(1), 44-57.
  • 3. Somers, B., Delalieux, S., Verstraeten, W.W., van Aardt, J.A.N., Albrigo, G.L., Coppin, P., 2010. An Automated Waveband Selection Technique for 2010 Optimized Hyperspectral Mixture Analysis, International Journal of Remote Sensing, 31(20), 5549–5568.
  • 4. Healey, G., Slater, D., 1999. Models and Methods for Automated Material Identification in Hyperspectral Imagery Acquired Under Unknown Illumination and Atmospheric Conditions. IEEE Trans. Geosci. Remote Sensing, 37(6), 2706–2717.
  • 5. Adams, J., Sabol, D., Kapos, V., Filho, R., Roberts, D., Smith, M., Gillespie, A., 1995. Classification of Multispectral Images Based on Fractions of Endmembers: Application to Land-cover Change in the Brazilian Amazon. Remote Sensing Environ., 52(2), 137–154.
  • 6. Drumetz, L., Veganzones, M.A., Henrot, S., Phlypo, R., Chanussot, J., Jutten, C., 2016. Blind Hyperspectral Unmixing Using an Extended Linear Mixing Model to Address Spectral Variability. IEEE Trans. Image Process., 25(8), 3890–3905.
  • 7. Uezato, T., Fauvel, M., Dobigeon, N., 2019. Hyperspectral Unmixing with Spectral Variability Using Adaptive Bundles and Double Sparsity, IEEE Transactions on Geoscience and Remote Sensing 57(6), 3980-3992.
  • 8. Zhang, J., Rivard, B., Sanchez-Azofeifa, A., Castro-Esau, K., 2006. Intra- and Inter-class Spectral Variability of Tropical Tree Species at La Selva, Costa Rica: Implications for species identification using HYDICE imagery. Remote Sensing of Environment, 105, 129−141.
  • 9. Drumetz, L., Chanussot, J., Jutten, C., 2016. Variability of the Endmembers in Spectral Unmixing: Recent Advances. 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 1-5.
  • 10. Somers, B., Asner, G.P., Tits, L., Coppin, P., 2011. Endmember Variability in Spectral Mixture Analysis: A review. Remote Sensing of Environment, Remote Sensing of Environment, 115, 1603–1616.
  • 11. Zare, A., Ho, K.C., 2014. Endmember Variability in Hyperspectral Analysis: Addressing Spectral Variability During Spectral Unmixing. IEEE Signal Processing Magazine, 31(1), 95-104.
  • 12. Theiler, J., Ziemann, A., Matteoli, S., Diani, M., 2019. Spectral Variability of Remotely Sensed Target Materials: Causes, Models, and Strategies for Mitigation and Robust Exploitation. IEEE Geoscience and Remote Sensing Magazine, 7(2), 8-30.
  • 13. Veganzones, M.A., Drumetz, L., Tochon, G., Dalla Mura, M., Plaza, A., Bioucas-Dias, J., Chanussot, J., 2014. A New Extended Linear Mixing Model to Address Spectral Variability. 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), 1–4.
  • 14. Thouvenin, P., Dobigeon, N., Tourneret, J., 2016. Hyperspectral Unmixing with Spectral Variability Using a Perturbed Linear Mixing Model. IEEE Transactions on Signal Processing, 64(2), 525–538.
  • 15. Uezato, T., Murphy, R.J., Melkumyan, A., Chlingaryan, A., 2016. A Novel Spectral Unmixing Method Incorporating Spectral Variability Within Endmember Classes. IEEE Transactions on Geoscience and Remote Sensing, 54(5), 2812–2831.
  • 16. H ong, D., Yokoya, N., Chanussot, J., Xiang Zhu, X., 2019. An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing. IEEE Transactions on Image Processing, 28(4), 1923–1938.
  • 17. Uezato, T., Fauvel Ma., Dobigeon, N., 2019. Hyperspectral Unmixing with Spectral Variability Using Adaptive Bundles and Double Sparsity. IEEE Transactions on Geoscience and Remote Sensing, 1–13.
  • 18. D rumetz, L., Meyer, T.R., Chanussot, J., Bertozzi, A.L., Christian, J., 2019. Hyperspectral Image Unmixing with Endmember Bundles and Group Sparsity Inducing Mixed Norms. IEEE Transactions on Image Processing, 28(7), 3435 – 3450.
  • 19. Ibarrola-Ulzurrun, E., Drumetz, L., Marcello, J., Gonzalo-Martín, C., Chanussot, J., 2019. Hyperspectral Classification Through Unmixing Abundance Maps Addressing Spectral Variability. IEEE Transactions on Geoscience and Remote Sensing, 57(7), 4775 – 4788.
  • 20. Borsoi, R.A., Imbiriba, T., Bermudez, J.C.M., 2020. A Data Dependent Multiscale Model for Hyperspectral Unmixing with Spectral Variability, IEEE Transactions on Image Processing, 29, 3638 – 3651.
  • 21. Benhalouche, F.Z., Karoui, M.S., Deville, Y., 2019. An NMF-Based Approach for Hyperspectral Unmixing Using a New Multiplicative-tuning Linear Mixing Model to Address Spectral Variability. 2019 27th European Signal Processing Conference (EUSIPCO), 24-27.
  • 22. Jung, J., Pasolli, E., Prasad, S., Tilton, J., Crawford, M., 2014. A Framework for Land Cover Classification Using Discrete Return LiDAR data: Adopting Pseudo-waveform and Hierarchical Segmentation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(2), 491–502.
  • 23. Uezato, T., Yokoya, N., He, W., 2020. Illumination Invariant Hyperspectral Image Unmixing Based on a Digital Surface Model, IEEE Transactions on Image Processing, 29, 3652 – 3664.
  • 24. Kahraman, S., Bacher, R., Uezato, T., Chanussot, J., Tangel, A., 2019. LiDAR- Guided Reduction of Spectral Variability in Hyperspectral Imagery. 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2019.
  • 25. Somers, B., Delalieux, S., Verstraeten, W.W., van Aardt, J.A.N., Albrigo, G.L., Coppin, P., 2010. An Automated Waveband Selection Technique for Optimized Hyperspectral Mixture Analysis, International Journal of Remote Sensing, 31(20), 5549–5568.
  • 26. Bioucas-Dias, J., Plaza, A., Dobigeon, N., Parente, M., Du, Q., Gader, P., Chanussot, J., 2012. Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-based Approaches. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(2), 354-379.
  • 27. Lee, D., Seung, H., 1999. Learning the Parts of Objects by Non-negative Matrix Factorization. Nature, 401, 788–791.
  • 28. Salembier, P., Garrido, L., 2000. Binary Partition Tree as an Efficient Representation for Image Processing, Segmentation, and Information Retrieval, IEEE Trans. Image Process., Apr., 9(4), 561–576.
  • 29. Tu, T.N., Chen, C.H., Wu, J.L., Chang, C.I., 1998. A Fast Two-stage Classification Method for High-dimensional Remote Sensing Data, IEEE Transactions on Geoscience and Remote Sensing, 36, 182–191.
  • 30. As ner, G.P., Lobell, D.B., 2000. A Biogeophysical Approach for Automated SWIR Unmixing of Soils and Vegetation, Remote Sensing of Environment, 74, 99–112.
  • 31. Uezato, T., Murphy, R.J., Melkumyan, A., Chlingaryan, A., 2016. A Novel Endmember Bundle Extraction and Clustering Approach for Capturing Spectral Variability Within Endmember Classes, IEEE Trans. Geosci. Remote Sens., 54(11), 6712 – 6731.
  • 32. http://www.lx.it.pt/~bioucas/code.htm.
  • 33. Drumetz, L., Veganzones, M.A., Henrot, S., Phlypo, R., Chanussot, J., Jutten, C., 2016. Blind Hyperspectral Unmixing Using an Extended Linear Mixing Model to Address Spectral Variability, IEEE Transactions on Image Processing, 25(8), 3890 – 3905.
Toplam 33 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Sevcan Kahraman Bu kişi benim 0000-0003-2173-7821

Yayımlanma Tarihi 31 Aralık 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 35 Sayı: 4

Kaynak Göster

APA Kahraman, S. (2020). LiDAR Verisi Yardımıyla Otomatik Dalga Boyu Bandı Yaklaşımı Kullanılarak Hiperspektral Görüntülerde Spektral Değişkenliğin Azaltılması. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 35(4), 983-992. https://doi.org/10.21605/cukurovaummfd.869160
AMA Kahraman S. LiDAR Verisi Yardımıyla Otomatik Dalga Boyu Bandı Yaklaşımı Kullanılarak Hiperspektral Görüntülerde Spektral Değişkenliğin Azaltılması. cukurovaummfd. Aralık 2020;35(4):983-992. doi:10.21605/cukurovaummfd.869160
Chicago Kahraman, Sevcan. “LiDAR Verisi Yardımıyla Otomatik Dalga Boyu Bandı Yaklaşımı Kullanılarak Hiperspektral Görüntülerde Spektral Değişkenliğin Azaltılması”. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi 35, sy. 4 (Aralık 2020): 983-92. https://doi.org/10.21605/cukurovaummfd.869160.
EndNote Kahraman S (01 Aralık 2020) LiDAR Verisi Yardımıyla Otomatik Dalga Boyu Bandı Yaklaşımı Kullanılarak Hiperspektral Görüntülerde Spektral Değişkenliğin Azaltılması. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi 35 4 983–992.
IEEE S. Kahraman, “LiDAR Verisi Yardımıyla Otomatik Dalga Boyu Bandı Yaklaşımı Kullanılarak Hiperspektral Görüntülerde Spektral Değişkenliğin Azaltılması”, cukurovaummfd, c. 35, sy. 4, ss. 983–992, 2020, doi: 10.21605/cukurovaummfd.869160.
ISNAD Kahraman, Sevcan. “LiDAR Verisi Yardımıyla Otomatik Dalga Boyu Bandı Yaklaşımı Kullanılarak Hiperspektral Görüntülerde Spektral Değişkenliğin Azaltılması”. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi 35/4 (Aralık 2020), 983-992. https://doi.org/10.21605/cukurovaummfd.869160.
JAMA Kahraman S. LiDAR Verisi Yardımıyla Otomatik Dalga Boyu Bandı Yaklaşımı Kullanılarak Hiperspektral Görüntülerde Spektral Değişkenliğin Azaltılması. cukurovaummfd. 2020;35:983–992.
MLA Kahraman, Sevcan. “LiDAR Verisi Yardımıyla Otomatik Dalga Boyu Bandı Yaklaşımı Kullanılarak Hiperspektral Görüntülerde Spektral Değişkenliğin Azaltılması”. Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, c. 35, sy. 4, 2020, ss. 983-92, doi:10.21605/cukurovaummfd.869160.
Vancouver Kahraman S. LiDAR Verisi Yardımıyla Otomatik Dalga Boyu Bandı Yaklaşımı Kullanılarak Hiperspektral Görüntülerde Spektral Değişkenliğin Azaltılması. cukurovaummfd. 2020;35(4):983-92.