Abstract
Improved sensor characteristics are generally assumed to increase the potential accuracy of image classification and information extraction from remote sensing imagery. However, the increase in data volume caused by these improvements raise challenges associated with the selection, storage, and processing of this data, and with the cost-effective and timely analysis of the remote sensing datasets. Previous research has extensively assessed the relevance and impact of spatial, spectral and temporal resolution of satellite data on classification accuracy, but little attention has been given to the impact of radiometric resolution. This study focuses on the role of radiometric resolution on classification accuracy of remote sensing data through different classification experiments over three different sites. The experiments were carried out using fine and low scale radiometric resolution images classified through a bagging classification tree. The classification experiments addressed different aspects of the classification road map, including among others, binary and multiclass classification schemes, spectrally and spatially enhanced images, as well as pixel and objects as units of the classification. In addition, the impact of image radiometric resolution on computational time and the information content in fine- and low-resolution images was also explored. While in certain cases, higher radiometric resolution has led to up to 8% higher classification accuracies compared to lower resolution radiometric data, other results indicate that higher radiometric resolution does not necessarily imply improved classification accuracy. Also, classification accuracy of spectral indices and texture bands is not related so much to the radiometric resolution of the original remote sensing images but rather to their own radiometric resolution. Overall, the results of this study suggest that data selection and classification need not always adhere to the highest possible radiometric resolution.
Highlights
In recent years, the improvements in spatial, spectral, radiometric, and temporal resolution of remote sensing imagery data has led to increased interest in the scientific community, as well as among end users in employing remote sensing data to new applications and operational needs.as resolution increases, the complexity of data increases and in order to fully exploit the potential of the new generation of remote sensing computers and sensors, a number of challenges need to be addressed
In the case of the 2010 image classification, the use of the lower radiometric resolution resulted in slightly higher overall accuracy (OA = 88% and khat = 0.75), compared to the finer 11-bit radiometric resolution image (OA = 86% and khat = 0.72), with the former classification demanding 10% less computational time
The role of radiometric resolution on classification accuracy, as well as on image information content and computational complexity was experimentally assessed in this study
Summary
The improvements in spatial, spectral, radiometric, and temporal resolution of remote sensing imagery data has led to increased interest in the scientific community, as well as among end users in employing remote sensing data to new applications and operational needs.as resolution increases, the complexity of data increases and in order to fully exploit the potential of the new generation of remote sensing computers and sensors, a number of challenges need to be addressed. The improvements in spatial, spectral, radiometric, and temporal resolution of remote sensing imagery data has led to increased interest in the scientific community, as well as among end users in employing remote sensing data to new applications and operational needs. With regard to storage requirements for example, NASA’s Earth Observing System Data and Information System (EOSDIS) has an extensive archive of remote sensing data currently exceeding 7.5 petabytes, with their data undergoing a growth of 4 TB daily [2]. These huge data volumes require the usage of image compression techniques, providing either ‘lossless’ compression or ‘lossy’ compression [3]. The majority of traditional image processing algorithms fail when the data resolution is greatly increased and it is often necessary to create new processing algorithms [4]
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