TESTING A COMBINED MULTISPECTRAL-MULTITEMPORAL APPROACH FOR GETTING CLOUDLESS IMAGERY FOR SENTINEL-2

Abstract. Earth observation and land cover monitoring are among major applications for satellite data. However, the use of primary satellite information is often limited by clouds, cloud shadows, and haze, which generally contaminate optical imagery. For purposes of hazard assessment, for instance, such as flooding, drought, or seismic events, the availability of uncontaminated optical data is required. Different approaches exist for masking and replacing cloud/haze related contamination. However, most common algorithms take advantage by employing thermal data. Hence, we tested an algorithm suitable for optical imagery only. The approach combines a multispectral-multitemporal strategy to retrieve daytime cloudless and shadow-free imagery. While the approach has been explored for Landsat information, namely Landsat 5 TM and Landsat 8 OLI, here we aim at testing the suitability of the method for Sentinel-2 Multi-Spectral Instrument. A multitemporal stack, for the same image scene, is employed to retrieve a composite uncontaminated image over a temporal period of few months. Besides, in order to emphasize the effectiveness of optical imagery for monitoring post-disaster events, two temporal stages have been processed, before and after a critical seismic event occurred in Lombok Island, Indonesia, in summer 2018. The approach relies on a clouds and cloud shadows masking algorithm, based on spectral features, and a data reconstruction phase based on automatic selection of the most suitable pixels from a multitemporal stack. Results have been tested with uncontaminated image samples for the same scene. High accuracy is achieved

Validation Examples of ATCOR Haze Removal of Rapid-Eye Images

Atmospheric correction of satellite images is necessary for many applications of remote sensing. Among them are applications for agriculture, forestry, land cover and land cover change, urban mapping, emergency and inland water. ATCOR is a widely used atmospheric correction tool which can process data of many optical satellite sensors, for instance Landsat, Sentinel-2, SPOT and RapidEye. ATCOR includes a terrain and adjacency correction of satellite images and several Special algorithms like haze detection, haze correction, cirrus correction, de-shadowing and empirical methods for BRDF correction. The largest uncertainty in atmospheric correction arises out of spatial and temporal variation of Aerosol amount and type. Therefore validation of aerosol estimation is one important step in validation of atmospheric correction algorithms. Last year we presented validation results of aerosol retrieval by the widely used atmospheric correction tool ATCOR. We compared vertical column aerosol-optical thickness (AOT) spectra derived from Rapid-Eye data with in-situ sun-photometer measurements on the ground. Mean uncertainty was ΔAOT550 ≈ 0.04. The presentation will update these results including reference measurements on the ground in year 2015. Haze removal gives the chance to add more observation points in time series analysis. We started to investigate the accuracy of ATCOR haze removal by comparing haze-removed Rapid-Eye Images with atmospherically corrected images from nearby cloudless data takes. First results are shown in the proposed presentation

HAZE REMOVAL IN THE VISIBLE BANDS OF LANDSAT 8 OLI OVER SHALLOW WATER AREA

Haze is one of radiometric quality parameters in remote sensing imagery. With certain atmospheric correction, haze is possible to be removed. Nevertheless, an efficient method for haze removal is still a challenge. Many methods have been developed to remove or to minimize the haze disruption. While most of the developed methods deal with removing haze over land areas, this paper tried to focus to remove haze from shallow water areas. The method presented in this paper is a simple subtraction algorithm between a band that reflected by water and a band that absorbed by water. This paper used data from Landsat 8 with visible bands as a band that reflected by water while the band that absorbed by water represented by NIR, SWIR-1, and SWIR-2 bands. To validate the method, a reference data which relatively clear of cloud and haze contamination is selected. The pixel numbers from certain points are selected and collected from data scene, results scene and reference scene. Those pixel numbers, then being compared each other to get a correlation number between data scene to reference scene and between result scene and reference scene. The comparison shows that the method using NIR, SWIR-1, and SWIR-2 all significantly improved correlations numbers between result scene with reference scene to higher than 0.9. The comparison also indicates that haze removal result using NIR band had the highest correlation with reference data.

Development of cloud removal and land cover Change extraction algorithms for remotely-sensed Landsat imagery

Land cover change monitoring requires the analysis of remotely-sensed data. In the tropics this is difficult because of persistent cloud cover, and data availability. This research focuses on the elimination of cloud cover as an important step towards addressing the issue of change detection. The result produced clearer images, whereas some persistent cloud remains. This persistent cloud and the cloud adjacency effects diminish the quality of image product and affect the change detection quality

Framework to Create Cloud-Free Remote Sensing Data Using Passenger Aircraft as the Platform

Cloud removal in optical remote sensing imagery is essential for many Earth observation applications.Due to the inherent imaging geometry features in satellite remote sensing, it is impossible to observe the ground under the clouds directly; therefore, cloud removal algorithms are always not perfect owing to the loss of ground truth. Passenger aircraft have the advantages of short visitation frequency and low cost. Additionally, because passenger aircraft fly at lower altitudes compared to satellites, they can observe the ground under the clouds at an oblique viewing angle. In this study, we examine the possibility of creating cloud-free remote sensing data by stacking multi-angle images captured by passenger aircraft. To accomplish this, a processing framework is proposed, which includes four main steps: 1) multi-angle image acquisition from passenger aircraft, 2) cloud detection based on deep learning semantic segmentation models, 3) cloud removal by image stacking, and 4) image quality enhancement via haze removal. This method is intended to remove cloud contamination without the requirements of reference images and pre-determination of cloud types. The proposed method was tested in multiple case studies, wherein the resultant cloud- and haze-free orthophotos were visualized and quantitatively analyzed in various land cover type scenes. The results of the case studies demonstrated that the proposed method could generate high quality, cloud-free orthophotos. Therefore, we conclude that this framework has great potential for creating cloud-free remote sensing images when the cloud removal of satellite imagery is difficult or inaccurate

Haze Reduction from Remotely Sensed Data

Haze consists of atmospheric aerosols and molecules that scatter and absorb solar radiation, thus affecting the downward and upward solar radiance to be recorded by remote sensing sensors. Haze modifies the spectral signature of land classes and reduces classification accuracy, so causing problems to users of remote sensing data. Hence, there is a need to reduce the haze effects to improve the usefulness of the data. A way to do this is by integrating spectral and statistical approaches. The result shows that the haze reduction method is able to increase the accuracy of the data statistically and visually

Historical forest biomass dynamics modelled with Landsat spectral trajectories

Acknowledgements National Forest Inventory data are available online, provided by Ministerio de Agricultura, Alimentación y Medio Ambiente (España). Landsat images are available online, provided by the USGS.Peer reviewedPostprin

Haze compensation and atmospheric correction for Sentinel-2 data

Sentinel-2 data bring the opportunity to analyze landcover at a high spatial accuracy together with a wide swath. Nevertheless, the high data volume requires a per granule analysis. This may lead to border effect (difference in the radiance/reflectance value) between the neighboring granules during atmospheric correction. If there is a high variation of the aerosol optical thickness (AOT) across the granules, especially in case of haze, the atmospherically corrected mosaicked products often show granule border effects. To overcome this artifact a dehazing prior the atmospheric correction is performed. The dehazing compensates only for the haze thickness keeping the AOT fraction for further estimation and compensation in the atmospheric correction chain. This approach results in a smoother AOT map estimate and a corresponding bottom of atmosphere (BOA) reflectance with no border artifact. Digital elevation model (DEM) is employed allowing a better labeling of haze and a higher accuracy of the dehazing. The DEM analysis rejects high elevation areas where bright surfaces might erroneously be classified as haze, thus reducing the probability of misclassification. An example of a numeric evaluation of the atmospheric correction products (AOT and BOA reflectance) is given. It demonstrates a smooth transition between the granules in the AOT map leading to the proper estimate of the BOA reflectance data. The dehazing and atmospheric correction are implemented in the DLR's ATCOR software

Investigation of techniques for inventorying forested regions. Volume 2: Forestry information system requirements and joint use of remotely sensed and ancillary data

The author has identified the following significant results. Effects of terrain topography in mountainous forested regions on LANDSAT signals and classifier training were found to be significant. The aspect of sloping terrain relative to the sun's azimuth was the major cause of variability. A relative insolation factor could be defined which, in a single variable, represents the joint effects of slope and aspect and solar geometry on irradiance. Forest canopy reflectances were bound, both through simulation, and empirically, to have nondiffuse reflectance characteristics. Training procedures could be improved by stratifying in the space of ancillary variables and training in each stratum. Application of the Tasselled-Cap transformation for LANDSAT data acquired over forested terrain could provide a viable technique for data compression and convenient physical interpretations

DESHADOWING OF HIGH SPATIAL RESOLUTION IMAGERY APPLIED TO URBAN AREA DETECTION

Different built-up structures usually lead to large regions covered by shadows, causing partial or total loss of information present in urban environments. In order to mitigate the presence of shadows while improving the urban target discrimination in multispectral images, this paper proposes an automated methodology for both detection and recovery of shadows. First, the image bands are preprocessed in order to highlight their most relevant parts. Secondly, a shadow detection procedure is performed by using morphological filtering so that a shadow mask is obtained. Finally, the reconstruction of shadow-occluded areas is accomplished by an image inpainting strategy. The experimental evaluation of our methodology was carried out in four study areas acquired from a WorldView-2 (WV-2) satellite scene over the urban area of São Paulo city. The experiments have demonstrated a high performance of the proposed shadow detection scheme, with an average overall accuracy up to 92%. Considering the results obtained by our shadow removal strategy, the pre-selected shadows were substantially recovered, as verified by visual inspections. Comparisons involving both VrNIR-BI and VgNIR-BI spectral indices computed from original and shadow-free images also attest the substantial gain in recovering anthropic targets such as streets, roofs and buildings initially damaged by shadows