A Spectral–Temporal Patch-Based Missing Area Reconstruction for Time-Series Images

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.

Reconstruction of Cloud-free Sentinel-2 Image Time-series Using an Extended Spatiotemporal Image Fusion Approach

“…To homogenize the differences, a normalization processing for calibrating the synthetic pixel values to the observational values was required. There are two main types of normalization methods: mapping and regression [16]. The mapping method directly establishes a pixel value equation between the processed images and uses the output of the mapping equation to replace the pixel values under consideration.…”

“…The first is the image composite method that uses observed image time series to reconstruct cloud-free images. For example, several methods have been developed for reconstruction of cloud-free time-series of Landsat images [9][10][11][12][13][14][15][16][17]. The second is the spatiotemporal image fusion approach that generates image time series by synthesizing fine resolution images (such as Landsat and Sentinel-2 images) from a limited number of clear-sky fine resolution images and coarse resolution images, such as observations from a moderate resolution image spectroradiometer (MODIS) when cloud-free fine images are unavailable.…”

Reconstruction of Sentinel-2 Image Time Series Using Google Earth Engine

“…Many methods have been developed to reconstruct the surface reflectance or NDVI time series. Based on several studies [7,14,15], methods to reconstruct surface reflectance time series can be divided into four types: (1) temporal-based methods [16,17]; (2) spectralbased methods [18,19]; (3) hybrid methods [20,21]; (4) multi-source fusion [7,[22][23][24]. In comparison, methods to reconstruct NDVI time series can be divided into four categories:…”

Otokodlayıcılar Kullanarak Uzaktan Algılama Görüntülerindeki Eksik Verilerin Yeniden Yapılandırılması

“…Ancak, pasif algılayıcılara sahip uydu sistemlerinin çalışma koşulları ve atmosferik koşullar nedeniyle, uzaktan algılama verileri genellikle eksik bilgiler içerirler [11]. Bu durum başta zaman serisi verileri kullanılarak yapılan çalışmalar olmak üzere birçok uzaktan algılama çalışmasının yapılmasını zorlaştırmakta ya da engellemektedir [12]. Bu nedenle uydu verileri içerisindeki eksik bilgilerin (veri olmayan piksellerin) tamamlanması, uzaktan algılama verilerinin kullanılabilirliğini attırarak bu alanda yapılan çoklu zaman analizi çalışmalarına önemli katkılar sağlayacaktır.…”