Abstract
Clouds, cloud shadows (CCS), and numerous other factors will cause a missing data problem in passive remote sensing images. A well-known reconstruction method is the selection of a similar pixel (with an additional clear reference image) from the remaining clear part of an image to replace the missing pixel. Due to the merit of filling the missing value using a pixel acquired on the same image with the same sensor and the same date, this method is suitable for time-series applications when a time-series profile-based similar measure is utilized for selecting the similar pixel. Since the similar pixel is independently selected, the improper reference pixel or various accuracies obtained by different land covers causes the problem of salt-and-pepper noise in the reconstructed part of an image. To overcome these problems, this paper presents a spectral–temporal patch (STP)-based missing area reconstruction method for time-series images. First, the STP, the pixels of which have similar spectral and temporal evolution characteristics, is extracted using multi-temporal image segmentation. However, some STP have Missing Observations (STPMO) in the time series, which should be reconstructed. Next, for an STPMO, the most similar STP is selected as the reference STP; then, the mean and standard deviation of the STPMO is predicted using a linear regression method with the reference STP. Finally, the textural information, which is denoted by the spatial configuration of color or intensities of neighboring pixels, is extracted from the clear temporal-adjacent STP and “injected” into the missing area to obtain synthetic cloud-free images. We performed an STP-based missing area reconstruction experiment in Jiangzhou, Chongzuo, Guangxi with time-series images acquired by wide field view (WFV) onboard Chinese Gao Fen 1 on 12 different dates. The results indicate that the proposed method can effectively recover the missing information without salt-and-pepper noise in the reconstructed area; also, the reconstructed part of the image is consistent with the clear part without a false edge. The results confirm that the spectral information from the remaining clear part of the same image and textural information from the temporal-adjacent image can create seamless time-series images.
Highlights
In passive remote sensing, clouds, cloud shadows (CCS) and numerous other factors (such as the Scan Line Corrector Off (SLC-OFF) for Enhanced Thematic Mapper Plus(ETM+) onboard Landsat 7) may obscure or contaminate the land surface information recorded by a remote sensor, which causes information distortion or missing data in the corresponding area of the obtained images [1,2]
Since the segment obtained by our method has similar spectral and temporal evolution characteristics, we refer to the segmentation result as an spectral–temporal patch (STP). (2) Reference STP selection for the STP have Missing Observations (STPMO): we adopt a similar STP-based measurement to select the reference STP for the STPMO. (3) Missing STP reconstruction: we reconstruct the missing STP in the image according to the reference STP, and obtain cloud-free time-series images
By magnifying the reconstruction area, we observe that (1) the reconstructed region of our method (Figure 5e) can restore the textural features and provide sufficient information, whereas the contrasting method (Figure 5f) is substantially influenced by salt-and-pepper noise, which conceals the local spectral contract of the land surface; and (2) the reconstructed part is consistent with the clear part of the image without a distinct false edge, which indicates that the reconstructed part and clear part blend well
Summary
Clouds, cloud shadows (CCS) and numerous other factors (such as the Scan Line Corrector Off (SLC-OFF) for Enhanced Thematic Mapper Plus(ETM+) onboard Landsat 7) may obscure or contaminate the land surface information recorded by a remote sensor, which causes information distortion or missing data in the corresponding area of the obtained images [1,2]. In cloudy and rainy regions, the acquisition of cloud-free images becomes difficult. The lack of cloud-free images become one of the most restrictive factors for land-related applications. Filling the missing part of the partially contaminated image will increase the data availability. The reconstruction of missing areas in time-series images will benefit a time-series image based application
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