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Evaluation of Morphological Texture Features for Mangrove Forest Mapping and Species Discrimination Using Multispectral IKONOS Imagery

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3 Author(s)
Xin Huang ; State Key Lab. of Inf. Eng. in Surveying, Mapping, & Remote Sensing, Wuhan Univ., Wuhan ; Liangpei Zhang ; Le Wang

This letter aims to exploit morphological textures in discriminating three mangrove species and surrounding environment with multispectral IKONOS imagery in a study area on the Caribbean coast of Panama. Morphological texture features are utilized to distinguish red (Rhizophora mangle), white (Laguncularia racemosa), and black (Avicennia germinans) mangroves and rainforest regions. Meanwhile, two fusion methods are presented, i.e., vector stacking and support vector machine (SVM) output fusion, for integrating the hybrid spectral-textural features. For comparison purposes, the object-based analysis and the gray-level co-occurrence matrix (GLCM) textures are adopted. Results revealed that the morphological feature opening by reconstruction (OBR) followed by closing by reconstruction (CBR) and its dual operator CBR followed by OBR gave very promising accuracies for both mangrove discrimination (89.1% and 91.1%, respectively) and forest mapping (91.4% and 93.7%, respectively), compared with the object-based analysis (80.5% for mangrove discrimination and 82.9% for forest mapping) and the GLCM method (81.9% and 87.2%, respectively). With respect to the spectral-textural information fusion algorithms, experiments showed that the SVM output fusion could obtain an additional 2.0% accuracy improvement than the vector-stacking approach.

Published in:

Geoscience and Remote Sensing Letters, IEEE  (Volume:6 ,  Issue: 3 )