Abstract
Legacy grayscale aerial photographs represent one of the main available sources for studying the past state of the environment and its relationship to the present. However, these photographs lack spectral information thereby hindering their use in current remote sensing approaches that rely on spectral data for characterizing surfaces. This article proposes a conditional generative adversarial network, a deep learning model, to enrich legacy photographs by predicting color channels for an input grayscale image. The technique was used to colorize two orthophotographs (taken in 1956 and 1978) covering the entire Eurométropole de Strasbourg. To assess the model's performances, two strategies were proposed: first, colorized photographs were evaluated with metrics such as peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM); second, random forest classifications were performed to extract land cover classes from grayscale and colorized photographs, respectively. The results revealed strong performances, with PSNR = 25.56 ± 2.20 and SSIM = 0.93 ± 0.06 indicating that the model successfully learned the mapping between grayscale and color photographs over a large territory. Moreover, land cover classifications performed on colorized data showed significant improvements over grayscale photographs, respectively, +6% and +17% for 1956 and 1978. Finally, the plausibility of outputs images was evaluated visually. We conclude that deep learning models are powerful tools for improving radiometric properties of old aerial grayscale photographs and land cover mapping. We also argue that the proposed approach could serve as a basis for further developments aiming to promote the use of aerial photographs archives for landscapes reconstruction.
Highlights
H ISTORICAL aerial photographs provide crucial data for efficient long-term environmental monitoring and change detection [1]–[4]
Given the lack of literature addressing the colorization of legacy aerial photographs, we propose a novel methodology to provide new spatial products, suitable for better integration within current remote sensing workflows
The proposed DRAGAN is fully conditioned by grayscale samples that are derived from current color stills
Summary
H ISTORICAL aerial photographs provide crucial data for efficient long-term environmental monitoring and change detection [1]–[4]. They offer unique background information at a very high spatial resolution [2], [5]. Thereafter, color photography rapidly superseded the use of grayscale shots [6]. Despite the current prominence of color aerial photographs, there remain many aerial photograph libraries that consist mainly of grayscale products. The French National Mapping Agency (IGN) distributes aerial photographs collected since 1919. The first IGN color photographs were only made available in 1959 [8]
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More From: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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