Pan-Sharpening Performance Comparison for Land Use Classification Application, and Its Effect on LISA LAPAN-A3 in Accuracy Improvement

2022 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES) Pub Date : 2022-11-24 DOI:10.1109/ICARES56907.2022.9993548

Agung Wahyudiono, Ega Asti Anggari, A. Herawan, Patria Rachman Hakim, A. Hadi Syafrudin, Elvira Rachim

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Abstract

Pan-sharpening is one data fusion application that aims to increase the spatial resolution of the multi-spectral image by merging a low-resolution multispectral image with a high-resolution panchromatic image. This process is commonly used to increase the quality of images in the application of land use classification. This research aims to see and learn about the performance of the pan-sharpening method in terms of Land Use Classifications. 5 different methods are compared to see each performance in classification. Moreover, not only using a single-platform data, which is multi-spectral (MS) and panchromatic (Pan) image from Landsat 8, this research also tries to fuse 2 data from a different platform, which are MS from LISA LAPAN-A3 and Pan from Landsat 8. It found that each pan-sharpening method has a different result in terms of accuracy when applied to single-platform data and cross-platform data, nevertheless, some improvements in accuracy were slightly found in pan-sharpened LISA's product to a 9.31% increase.

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土地利用分类应用的泛锐化性能比较及其对LISA LAPAN-A3精度提高的影响

泛锐化是一种数据融合应用，旨在通过将低分辨率多光谱图像与高分辨率全色图像合并来提高多光谱图像的空间分辨率。在土地利用分类的应用中，该过程通常用于提高图像质量。本研究旨在观察和了解泛锐化方法在土地利用分类方面的表现，并对5种不同的方法进行了比较，以了解每种分类方法的表现。此外，本研究不仅使用单一平台的数据，即Landsat 8的多光谱(MS)和全色(Pan)图像，还尝试融合不同平台的数据，即LISA LAPAN-A3的MS和Landsat 8的Pan。研究发现，每种泛锐化方法在单平台数据和跨平台数据上的精度结果不同，但在LISA的产品中，泛锐化后的产品精度略有提高，提高了9.31%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2022 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES)

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