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

By Xin Huang, Liangpei Zhang, Le Wang. 2009

127 citations recorded by the source corpus.

Subfields: Atmospheric Science, Ecology, Media Technology, Remote Sensing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. E. Aptoula
2. Xin Huang

In Ecology

1. E. Aptoula
2. Desheng Liu
3. Xin Huang
4. Yoshio Inoue
5. Brian Alan Johnson
6. Zhixiao Xie
7. Eiji Sakaiya

In Media Technology

1. E. Aptoula
2. Xin Huang
3. Lionel Gueguen
4. Liangpei Zhang
5. Brian Alan Johnson
6. Zhixiao Xie
7. Yoshio Inoue
8. Eiji Sakaiya

In Remote Sensing

1. E. Aptoula
2. Xin Huang

Citations in the corpus

• Remote Sensing Image Retrieval With Global Morphological Texture Descriptors (2014, methodology): “cessful and widely used methods for remote sensing image classification, while the morphological remote sensing application extent also includes simplification [35], texture analysis [36], and segmentation [37].”
• Strathprints Institutional Repository Shuhui and Ren, Jinchang (2015) Effective and Efficient Midlevel Visual Elements-oriented Land-use Classification Using Vhr Remote Sensing Images. Ieee Transactions on Geoscience and Remote Sensing, 53 (8). Pp. 4238-4249. Issn 0196-2892 Effective and Efficient M (unknown): “Most previous works [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] mainly focus on classifying pixels or objects (or rather, grouping of local homogeneous pixels) into their thematic class by extracting low-level image features (e.g. texture feature [9] [10], color feature [8], spatial and…”
• Assessing object-based classification: advantages and limitations (2009, background): “…(SVM), in all classifications for its generally good performance in dealing with a large number of features without heavily relying on feature selection (Melgani and Bruzzone 2004, Huang et al. 2009), although some studies have shown that SVM can benefit from feature selection (Navot et al .”
• Wavelength selection and spectral discrimination for paddy rice, with laboratory measurements of hyperspectral leaf reflectance (2011, background): “1989; Yoder and Pettigrew-Crosby, 1995; Curran et al., 1997, 2001; Blackburn, 1999; Sims and Gamon, 2002; Coops et al., 2003), and their ability to discriminate different species (Kleynen et al., 2003; Huang and Zhang, 2009).”