Cloud removal analysis of remote sensing satellite imagery

Abstract

Remote sensing satellite images are widely used for many applications, such as land use land cover mapping, agriculture, forestry, marine, disaster monitoring, etc. Unfortunately, there is an obstacle regarding cloud cover. The existence of clouds interferes remote sensing activities. Moreover, about two third of the earth's surface is covered by cloud at any one time. This issue increases the difficulty to support operational remote sensing monitoring applications.This thesis developed, tested and applied methods for cloud masking and removal that work in tropical regions. Four specific objectives were addressed: (1) developing a cloud and cloud shadow masking method for Landsat 8 images in Indonesia, ensuring accuracy is assessed over heterogeneous land cover; (2) improving the ability of the proposed method for detecting cloud and cloud shadow for Landsat 8 images with a variety of cloud types in Indonesia; (3) developing a method for filling masked pixels in Landsat 8 images in Indonesia; and (4) extension a cloud and cloud shadow masking method for Sentinel-2 images.After introductory, review and research approach chapters one -three, chapter four developed a method for cloud and cloud shadow masking called Multi-temporal Cloud Masking (MCM) for Landsat 8 images in tropical environments. This method used two kinds of data: (1) target image and(2) reference image. A target image is an image may have cloud and cloud shadow contaminated pixels and a reference image is a cloud-free image. This method utilizes the difference of reflectance values between those images. The MCM algorithm was applied and evaluated in a part of Indonesia which has a heterogeneous land cover. This chapter concluded that the MCM algorithm can be used to accurately detect cloud and cloud shadow for Landsat 8 images from areas with a heterogeneous land cover in tropical environments.Chapter five developed and assessed an approach for cloud and cloud shadow removal called Multitemporal Cloud Removal (MCR) for Landsat 8 images. This approach has three main steps: (1) radiometric correction; (2) cloud and cloud shadow detection; and (3) image reconstruction. In this chapter, the previous MCM was improved for detecting a variety of cloud types. By using images from a sequence acquisition date, the issue of varying land cover changes was minimised. In the image reconstruction step, a method for filling masked pixels was applied. At the end of the process, cloud and cloud shadow free image was generated. To evaluate the results, selected Landsat 8 images over a limited area in Indonesia with heterogeneous land cover and a variety of cloud types were ii tested. As a result, cloud and cloud shadow contaminated pixels on Landsat 8 images were able to be removed in several test cases. The resultant images had high visual and statistical similarities with the reference image. This evaluation provided a preliminary indication of the utility of MCR in removing cloud and cloud shadow for Landsat 8 images.Chapter six expanded the MCM approach designed ...