Reduction of Radiometric Miscalibration—Applications to Pushbroom Sensors

Abstract

The analysis of hyperspectral images is an important task in Remote Sensing. Foregoing radiometric calibration results in the assignment of incident electromagnetic radiation to digital numbers and reduces the striping caused by slightly different responses of the pixel detectors. However, due to uncertainties in the calibration some striping remains. This publication presents a new reduction framework that efficiently reduces linear and nonlinear miscalibrations by an image-driven, radiometric recalibration and rescaling. The proposed framework—Reduction Of Miscalibration Effects (ROME)—considering spectral and spatial probability distributions, is constrained by specific minimisation and maximisation principles and incorporates image processing techniques such as Minkowski metrics and convolution. To objectively evaluate the performance of the new approach, the technique was applied to a variety of commonly used image examples and to one simulated and miscalibrated EnMAP (Environmental Mapping and Analysis Program) scene. Other examples consist of miscalibrated AISA/Eagle VNIR (Visible and Near Infrared) and Hawk SWIR (Short Wave Infrared) scenes of rural areas of the region Fichtwald in Germany and Hyperion scenes of the Jalal-Abad district in Southern Kyrgyzstan. Recovery rates of approximately 97% for linear and approximately 94% for nonlinear miscalibrated data were achieved, clearly demonstrating the benefits of the new approach and its potential for broad applicability to miscalibrated pushbroom sensor data.

A Contribution to the Reduction of Radiometric Miscalibration of Pushbroom Sensors

“…Such high noise levels were chosen to also simulate low SNR scenarious that are noise dominated. More details on the noise matrices and the hyperspectral scenes are given in Rogass et al (2011).…”

“…1a) and the 'Mandrill' image ( Fig. 1b) are excluded from further considerations due to their unique spectral and spatial properties as detailed described in Rogass et al (2011). To simulate different types of miscalibrations and to evaluate their impact on the proposed work, the two grey valued images ( Fig.…”

“…In Rogass et al (2011) four grey valued images (Fig. 1) from the image database of the Signal and Image Processing Institute (SIPI) of the University of California (Weber, 1997), 512 × 512 pixels in size, and six hyperspectral scenes (3 AISA DUAL, 2 Hyperion and 1 EnMAP) were selected to test and to evaluate the performance of the proposed ROME framework.…”

“…Potential edge detection algorithms for single banded images must be then adapted to incorporate only along track gradients, because gradients of radiometric miscalibration might superimpose across track gradients. In Rogass et al (2011) the Canny algorithm (Canny, 1986) is used for single banded images and the Hyperspectral Edge Detection Algorithm (HEDA) is used for multi banded images (Rogass et al, 2010). After obtaining binary edge maps morphological dilations (Haralick et al, 1987;Rogass et al, 2009) are additionally applied to minimise edge adjacency effects caused by Point Spread Function (PSF) related blooming of edges into adjacent regions.…”

“…This framework, originally proposed by Rogass et al (2011) and named as Reduction of Miscalibration Effects (ROME), consists of a linear and a nonlinear slope reduction and an offset reduction that are consecutively performed and does not require a priori information or scene and sensor specific parameterisation.…”