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A decision-level fusion approach to tree species classification from multi-source remotely sensed data

In this study, an object-oriented, decision-level fusion method is proposed for tree species classification based on spectral, textural, and structural features derived from multi-spectral and panchromatic imagery and Light Detection And Ranging (LiDAR) data. Murphy's average method based on the Dempster Shafer theory (DST) was used to calculate the combined mass function for decision making purposes. For individual feature groups, the mass functions were calculated using the support vector machine (SVM) classification method. The species examined included Norway maple, honey locust, Austrian pine, blue spruce, and white spruce. In addition to these species, a two- or three-species compound class was included in the decision process based on the normalized entropy in the presence of conflict that was itself determined according to whether individual groups of features were consistent. The developed method provided a mechanism to identify tree crowns, which could not be classified to one single species with a high confidence due to the conflict among feature groups. Data used in this study were obtained for the Keele Campus of York University, Toronto, Ontario. Among the 223 test crowns, 204 crowns were assigned to one single species, and the overall classification accuracy was 0.89. A decision could not be made for 19 crowns with confidence, and as a result, a two- or three-species compound class was assigned. The classification accuracy was higher than that obtained using SVM classification based on individual and combined spectral, structural, and textural features.

Ketersediaan
01621.3678Perpustakaan BIG (eksternal harddisk)Tersedia
Informasi Detail
Judul Seri
ISPRS Open Journal of Photogrammetry and Remote Sensing
No. Panggil
-
Penerbit
Amsterdam : Elsevier.,
Deskripsi Fisik
16 hlm PDF, 4.155 KB
Bahasa
Inggris
ISBN/ISSN
1872-8235
Klasifikasi
Remote sensing
Tipe Isi
text
Tipe Media
other
Tipe Pembawa
-
Edisi
Vol.1, October 2021
Subjek
Information fusion
Dempster Shafer Theory (DST)
Tree species classification
Normalized entropy
Multi-source remotely sensed data
Object-oriented
Info Detail Spesifik
-
Pernyataan Tanggungjawab
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • A decision-level fusion approach to tree species classification from multi-source remotely sensed data
    In this study, an object-oriented, decision-level fusion method is proposed for tree species classification based on spectral, textural, and structural features derived from multi-spectral and panchromatic imagery and Light Detection And Ranging (LiDAR) data. Murphy's average method based on the Dempster Shafer theory (DST) was used to calculate the combined mass function for decision making purposes. For individual feature groups, the mass functions were calculated using the support vector machine (SVM) classification method. The species examined included Norway maple, honey locust, Austrian pine, blue spruce, and white spruce. In addition to these species, a two- or three-species compound class was included in the decision process based on the normalized entropy in the presence of conflict that was itself determined according to whether individual groups of features were consistent. The developed method provided a mechanism to identify tree crowns, which could not be classified to one single species with a high confidence due to the conflict among feature groups. Data used in this study were obtained for the Keele Campus of York University, Toronto, Ontario. Among the 223 test crowns, 204 crowns were assigned to one single species, and the overall classification accuracy was 0.89. A decision could not be made for 19 crowns with confidence, and as a result, a two- or three-species compound class was assigned. The classification accuracy was higher than that obtained using SVM classification based on individual and combined spectral, structural, and textural features.
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