TY - JOUR

T1 - De-noised and contrast enhanced KH-9 HEXAGON mapping and panoramic camera images for urban research

AU - Shahtahmassebi, Amir Reza

AU - Liu, Minshi

AU - Li, Longwei

AU - Wu, JieXia

AU - Zhao, Mingwei

AU - Chen, Xi

AU - Jiang, Ling

AU - Huang, Danni

AU - Hu, Feng

AU - Huang, Minmin

AU - Deng, Kai

AU - Huang, Xiaoli

AU - Shahtahmassebi, Golnaz

AU - Biswas, Asim

AU - Moore, Nathan

AU - Atkinson, Peter M.

PY - 2023/6/30

Y1 - 2023/6/30

N2 - In 2002 and 2020–2022, KH-9 HEXAGON mapping camera system (MCS) and panoramic camera system (PCS) images were made available to the public, respectively. Although great efforts have been made by the scientific community to develop applications that utilize KH-9 HEXAGON images, little attention has been paid to de-noising and contrast enhancement of these images particularly over urban landscapes. This paper focuses on developing a de-noising and contrast enhancement pipeline for KH-9 HEXAGON MCS and PCS over urban regions. The proposed approach employs first a wavelet transform trained using a suite of ‘degree of over-smoothing’ metrics (DOSM) for image de-noising. These metrics are sensitive to structure, texture, edges and local homogeneity of image objects. Then the de-noised image is subjected to the multi-resolution Top-hat to optimize the contrast. This method incorporates a range of shapes and neighborhoods at multiple scales. The method was applied to a KH-9 HEXAGON MCS image (acquired in 1975) and PCS image (acquired in 1974) representing a complex urban landscape, to support comprehensive evaluation under a range of settings. Performance was assessed against three state-of-the-art benchmark approaches: residual learning (deep learning), blind deconvolution and spatial filtering. To evaluate the performance of the proposed pipeline against the benchmarks, we employed the saturation image edge difference standard-deviation, co-occurrence metrics and the semivariogram. Additionally, the potential applications of pre-processed results were demonstrated using change detection, identification reference points and stereo images. The proposed method not only improved the quality of the KH-9 image across the different urban landscape types, but also preserved the original spatial characteristics of the image in comparison with the benchmark methods. At a time when understanding the nature of our changing planet is paramount, the proposed pipeline should be of great benefit to investigators wishing to use KH program images to extend their historical or time-series analyses further back in time.

AB - In 2002 and 2020–2022, KH-9 HEXAGON mapping camera system (MCS) and panoramic camera system (PCS) images were made available to the public, respectively. Although great efforts have been made by the scientific community to develop applications that utilize KH-9 HEXAGON images, little attention has been paid to de-noising and contrast enhancement of these images particularly over urban landscapes. This paper focuses on developing a de-noising and contrast enhancement pipeline for KH-9 HEXAGON MCS and PCS over urban regions. The proposed approach employs first a wavelet transform trained using a suite of ‘degree of over-smoothing’ metrics (DOSM) for image de-noising. These metrics are sensitive to structure, texture, edges and local homogeneity of image objects. Then the de-noised image is subjected to the multi-resolution Top-hat to optimize the contrast. This method incorporates a range of shapes and neighborhoods at multiple scales. The method was applied to a KH-9 HEXAGON MCS image (acquired in 1975) and PCS image (acquired in 1974) representing a complex urban landscape, to support comprehensive evaluation under a range of settings. Performance was assessed against three state-of-the-art benchmark approaches: residual learning (deep learning), blind deconvolution and spatial filtering. To evaluate the performance of the proposed pipeline against the benchmarks, we employed the saturation image edge difference standard-deviation, co-occurrence metrics and the semivariogram. Additionally, the potential applications of pre-processed results were demonstrated using change detection, identification reference points and stereo images. The proposed method not only improved the quality of the KH-9 image across the different urban landscape types, but also preserved the original spatial characteristics of the image in comparison with the benchmark methods. At a time when understanding the nature of our changing planet is paramount, the proposed pipeline should be of great benefit to investigators wishing to use KH program images to extend their historical or time-series analyses further back in time.

KW - Keyhole program

KW - KH-9 HEXAGON

KW - PCS

KW - MCS

KW - Wavelet

KW - Top-hat

KW - Residual learning

KW - Blind deconvolution

KW - Stereo

U2 - 10.1016/j.srs.2023.100082

DO - 10.1016/j.srs.2023.100082

M3 - Journal article

VL - 7

JO - Science of Remote Sensing

JF - Science of Remote Sensing

SN - 2666-0172

M1 - 100082

ER -