Abstract
Reliable image classification results are crucial for the application of remote sensing images, but the reliability of image classification has received less attention. In particular, the inherent uncertainty of remote sensing images has been disregarded. The uncertainty of a remote sensing image accumulates and propagates continuously in the classification process and ultimately affects the reliability of the classification results. Therefore, quantitative description and investigation of the inherent uncertainty of remote sensing images are crucial in achieving reliable remote sensing image classification. In this study, we analyze the sources of uncertainty of remote sensing images in detail and propose a quantitative descriptor for measuring image uncertainty comprehensively and effectively. In addition, we also design two verification schemes to verify the validity of the proposed uncertainty descriptor. Finally, the validity of the proposed uncertainty descriptor is confirmed by experimental results on three real remote sensing images. Our study on the uncertainty of remote sensing images may help the development of uncertainty control methods and reliable classification schemes of remote sensing images.
Highlights
With the continuous development of space-to-earth observation technology, more and more remote sensing images with different spatial, spectral, and temporal resolutions have appeared in recent years
This study aims to analyze the sources of uncertainty of remote sensing imagery in detail and propose a quantitative description model for measuring image uncertainty comprehensively and effectively
To prove the validity and robustness of the proposed uncertainty descriptor, we perform validation experiments on three real remote sensing image datasets according to the two designed verification schemes
Summary
With the continuous development of space-to-earth observation technology, more and more remote sensing images with different spatial, spectral, and temporal resolutions have appeared in recent years. Different types of remote sensing imagery can record surface information in detail from different aspects. High spatial resolution remote sensing images can effectively record the detailed spatial information of various ground objects. Hyperspectral imagery with hundreds of continuous narrow spectral bands can record the spectral information of different ground objects in detail [1]. Remote sensing images have been widely and effectively utilized in many fields, such as urban monitoring, environment assessment, and decision making [2,3]. Remote sensing image classification is crucial; it has been receiving much attention in recent decades and has always been a research hotspot [4]
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