Hauser, L.T., Giang, N.V., Bình, N.A., Trang, N.T.Q., Hieu, N.M., Toan, L.Q. and Hòa, P.V. (2015) Spectral Discrimination of Common Mangrove Species in Ca Mau Peninsula, Vietnam: Using a Large Sample Set of Hyperspectral In-Situ Canopy Data. Proceeding of National GIS Conference, Hanoi, 20 October 2015, 35-37.

has been cited by the following article:

• TITLE: Object-Based vs. Pixel-Based Classification of Mangrove Forest Mapping in Vien An Dong Commune, Ngoc Hien District, Ca Mau Province Using VNREDSat-1 Images

  AUTHORS: Nguyen Thi Quynh Trang, Le Quang Toan, Tong Thi Huyen Ai, Nguyen Vu Giang, Pham Viet Hoa

  KEYWORDS: Object-Based Classification, Pixel-Based Classification, VNREDSat-1, Mangrove Forest, Ca Mau

  JOURNAL NAME: Advances in Remote Sensing, Vol.5 No.4, December 2, 2016

  ABSTRACT: Many researches have been performed comparing object-based classification (OBC) and pixel-based classification (PBC), particularly in classifying high-resolution satellite images. VNREDSat-1 is the first optical remote sensing satellite of Vietnam with resolution of 2.5 m (Panchromatic) and 10 m (Multispectral). The objective of this research is to compare two classification approaches using VNREDSat-1 image for mapping mangrove forest in Vien An Dong commune, Ngoc Hien district, Ca Mau province. ISODATA algorithm (in PBC method) and membership function classifier (in OBC method) were chosen to classify the same image. The results show that the overall accuracies of OBC and PBC are 73% and 62.16% respectively, and OBC solved the “salt and pepper” which is the main issue of PBC as well. Therefore, OBC is supposed to be the better approach to classify VNREDSat-1 for mapping mangrove forest in Ngoc Hien commune.