A Minimum Cloud Cover Mosaic Image Model of the Operational Land Imager Landsat-8 Multitemporal Data using Tile based

The need for remote sensing minimum cloud cover or cloud free mosaic images is now increasing in line with the increased of national development activities based on one map policy. However, the continuity and availability of cloud and haze free remote sensing data for the purpose of monitoring the natural resources are still low. This paper presents a model of medium resolution remote sensing data processing of Landsat-8 uses a new approach called mosaic tile based model (MTB), which is developed from the mosaic pixel based model (MPB) algorithm, to obtain an annual multitemporal mosaic image with minimum cloud cover mosaic imageries. The MTB model is an approach constructed from a set of pixels (called tiles) considering the image quality that is extracted from cloud and haze free areas, vegetation coverage, and open land coverage of multitemporal imageries. The data used in the model are from Landsat-8 Operational Land Imager (OLI) covering 10 scenes area, with 2.5 years recording period from June 2015 to June 2017; covered Riau, West Sumatra and North Sumatra Provinces. The MTB model is examined with tile size of 0.1 degrees (11x11 km2), 0.05 degrees (5.5x5.5 km2), and 0.02 degrees (2.2x2.2 km2). The result of the analysis shows that the smallest tile size 0.02 gives the best result in terms of minimum cloud cover and haze (or named clear area). The comparison of clear area values to cloud cover and haze for three years (2015, 2016 and 2017) for the three mosaic images of MTB are 68.2%, 78.8%, and 86.4%, respectively

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

Deep internal learning for inpainting of cloud-affected regions in satellite imagery

Cloud cover remains a significant limitation to a broad range of applications relying on optical remote sensing imagery, including crop identification/yield prediction, climate monitoring, and land cover classification. A common approach to cloud removal treats the problem as an inpainting task and imputes optical data in the cloud-affected regions employing either mosaicing historical data or making use of sensing modalities not impacted by cloud obstructions, such as SAR. Recently, deep learning approaches have been explored in these applications; however, the majority of reported solutions rely on external learning practices, i.e., models trained on fixed datasets. Although these models perform well within the context of a particular dataset, a significant risk of spatial and temporal overfitting exists when applied in different locations or at different times. Here, cloud removal was implemented within an internal learning regime through an inpainting technique based on the deep image prior. The approach was evaluated on both a synthetic dataset with an exact ground truth, as well as real samples. The ability to inpaint the cloud-affected regions for varying weather conditions across a whole year with no prior training was demonstrated, and the performance of the approach was characterised

Global Forest Monitoring from Earth Observation

Covering recent developments in satellite observation data undertaken for monitoring forest areas from global to national levels, this book highlights operational tools and systems for monitoring forest ecosystems. It also tackles the technical issues surrounding the ability to produce accurate and consistent estimates of forest area changes, which are needed to report greenhouse gas emissions and removals from land use changes. Written by leading global experts in the field, this book offers a launch point for future advances in satellite-based monitoring of global forest resources. It gives readers a deeper understanding of monitoring methods and shows how state-of-art technologies may soon provide key data for creating more balanced policies

Generating "cloud free" and "cloud-shadow free" mosaic for spot panchromatic images

International Geoscience and Remote Sensing Symposium (IGARSS)42480-2482IGRS