An object-based approach for vegetation and non-vegetation discrimination using WorldView-2 image

Ridha, Syafiq Muhammad and Kamal, Muhammad (2021) An object-based approach for vegetation and non-vegetation discrimination using WorldView-2 image. In: Seventh Geoinformation Science Symposium 2021; 120820U (2021), 22 December 2021, Yogyakarta.

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Abstract

The emergence of remote sensing images with high spatial resolution has increased the advancement of image-based information extraction methods. One of the rapidly developing approaches for mapping and analyzing high spatial resolution images is the object-based approach, also known as geographic object-based image analysis (GEOBIA). This development makes it possible to quickly and accurately distinguish between vegetated and non-vegetated objects in vegetation study. This study aims to (1) create a ruleset to discriminate vegetated and non-vegetated objects from a high spatial resolution image, (2) apply the GEOBIA approach to map vegetated and non-vegetated objects, and (3) calculate the accuracy of the mapping results. The GEOBIA approach was applied to a WorldView-2 image (2 m pixel size and eight multispectral bands) of the Clungup Mangrove Conservation area, Malang, East Java, Indonesia. We assessed the ability of all of the WorldView-2 image bands for discriminating the targeted objects. The segmentation process in GEOBIA used a multi-resolution segmentation algorithm using the normalized difference vegetation index (NDVI), and the image classification used a rule-based classification technique. The green, red, and near-infrared bands can effectively distinguish the targeted objects based on the developed ruleset. The classification result shows that the vegetated and non-vegetated classes fall within their corresponding objects on the image. We implemented an area-based accuracy assessment that assesses both positional and thematic accuracy of the mapping result, based on the visual interpretation of the pansharpened WV-2 image (0.5 m pixel size) as a reference for the accuracy assessment. This process results in a 74,06 accuracy, meaning that the combination of GEOBIA and WorldView-2 image produces high accuracy of vegetated and non-vegetated objects map. © 2021 SPIE.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Cited by: 0
Uncontrolled Keywords: Image resolution; Image segmentation; Infrared devices; Mapping; Pixels; Remote sensing; Analysis approach; Geographic object-based image analysis; High spatial resolution images; Object based; Objects-based; Pixel size; Ruleset; Segmentation; Targeted objects; Worldview-2; Vegetation
Subjects: G Geography. Anthropology. Recreation > GF Human ecology. Anthropogeography
Divisions: Faculty of Geography > Departemen Geografi Pembangunan
Depositing User: Sri JUNANDI
Date Deposited: 09 Oct 2024 08:52
Last Modified: 09 Oct 2024 08:52
URI: https://ir.lib.ugm.ac.id/id/eprint/8672

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