In the field of remote sensing image compression it is often argued that traditional MSE-based fidelity metrics might not effectively describe the quality of remote sensing lossy or near-lossless compressed images. In this paper we introduce a performance evaluation framework based on both reconstruction fidelity and impact on image exploitation. Besides MSE, the framework also considers hard classification and mixed pixel classification, as well as anomaly detection. We apply this framework to evaluate and compare the quality of state-of-the-art lossy and near-lossless compression techniques applied to hyperspectral AVIRIS scenes.

Quality assessment for hyperspectral imagery: comparison between lossy and near-lossless compression / Penna, Barbara; Tillo, Tammam; Magli, Enrico; Olmo, Gabriella. - (2006), pp. 1902-1906. ((Intervento presentato al convegno Asilomar Conference on Signals, Systems, and Computers tenutosi a Pacific Grove, USA nel October, 2006 [10.1109/ACSSC.2006.355093].

Quality assessment for hyperspectral imagery: comparison between lossy and near-lossless compression

PENNA, BARBARA;TILLO, TAMMAM;MAGLI, ENRICO;OLMO, Gabriella
2006

Abstract

In the field of remote sensing image compression it is often argued that traditional MSE-based fidelity metrics might not effectively describe the quality of remote sensing lossy or near-lossless compressed images. In this paper we introduce a performance evaluation framework based on both reconstruction fidelity and impact on image exploitation. Besides MSE, the framework also considers hard classification and mixed pixel classification, as well as anomaly detection. We apply this framework to evaluate and compare the quality of state-of-the-art lossy and near-lossless compression techniques applied to hyperspectral AVIRIS scenes.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/1531910
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