Comparing per-pixel and object-based classification results using two different land-cover/land-use classification schemes: a case study using Landsat-8 OLI imagery
Abstract
Development of satellite sensor systems capable of producing high spatial resolution digital images has led to the emergence of various alternative methods beside the more established per-pixel multispectral classifications. One alternative method is object-based image analysis (OBIA). At the beginning of its development, OBIA was primarily used for high spatial resolution images. However, the OBIA is now widely applied to images with medium- and even low-spatial resolutions. This study aimed to compare the effects of the OBIA and per-pixel classifications using using Landsat-8 OLI medium-spatial resolution image. Since the per-pixel classification relies solely on spectral aspects on various spectral bands, while the OBIA classification made use of spatial aspects as the main criteria, this study also made use of two land-cover/land-use classification schemes as references, i.e. spectral-oriented and spatial-oriented classification systems. The spectral-oriented classification scheme specifies categories from spectral perspective, i.e. pixel values in n-dimensional feature space; while the spatial-oriented one specifies categories with respect to their spatial characteristics. By using Kulon Progo region as a test area, the results showed that the OBIA classification was able to provide higher accuracy than that of per-pixel classification, both by referring to the spectral and spatial dimension classification schemes. The increase in accuracy provided by the OBIA classification proved to be greater when applied with a spatial dimension classification scheme, which was more than 10%, as compared to the improvement obtained by the spectral dimension classification scheme, i.e. 7%. This study also recommends the need for comparison studies using higher-spatial resolution imagery.
- Publication:
-
Sixth International Symposium on LAPAN-IPB Satellite
- Pub Date:
- December 2019
- DOI:
- 10.1117/12.2541876
- Bibcode:
- 2019SPIE11372E..06P