Automatic cloud removal from multi-temporal SPOT images

An approach to remove clouds in Landsat-8 operational land imager (OLI) data was developed with independent component analysis (ICA). Within cloud-covered areas, histograms were derived to quantify changes of the reflectance values before and after the use of the algorithm. Referred to a cloud-free image, changes of histogram curves validated the algorithm. Scatterplots were generated and linear regression performed for the reflectance values of each band before and after the algorithm, and compared to those of the reference image. Band-by-band, results in cloud removal were acceptable. The algorithm had little effect on pixels in cloud-free areas after the analyses of histograms, scatterplots, and linear regression equations. Finally, the algorithm was applied to various land use and land cover types and cloud conditions, and to a full Landsat-8 scene yielding satisfactory results efficiently.

Section: Introductionmentioning

confidence: 99%

Removal of Thin Clouds in Landsat-8 OLI Data with Independent Component Analysis

Shen

Wang

et al. 2015

“…However, VHSR sensors do not come with such, and, when available in medium resolution it is of a coarser resolution than the other channels (e.g., the spatial resolution of Landsat 7 ETM+ is 60 m while that of Landsat 8 TIRS is 100 m [48]). Some alternatives proposed to address this limitation have been the use of Markov random fields [49], linear spectral unmixing [50], pixel-based seed identification and object-based region growing [51], and a multi-temporal approach at constant viewing angles [35]. Some cloud-specific masking algorithms are the AFAR algorithm (ACOLITE/FMASK Aquatic Refined) developed by the Royal Belgian Institute of Natural Sciences (RBINS) , the Automatic Cloud Cover Assessment modified (ACCAm) algorithm (ACCA modified) by VITO, and Idepix developed by Brockmann Consult GmbH.…”

Section: Cloud Maskingmentioning

confidence: 99%

A Workflow for Automated Satellite Image Processing: from Raw VHSR Data to Object-Based Spectral Information for Smallholder Agriculture

Stratoulias

Tolpekin

et al. 2017

Earth Observation has become a progressively important source of information for land use and land cover services over the past decades. At the same time, an increasing number of reconnaissance satellites have been set in orbit with ever increasing spatial, temporal, spectral, and radiometric resolutions. The available bulk of data, fostered by open access policies adopted by several agencies, is setting a new landscape in remote sensing in which timeliness and efficiency are important aspects of data processing. This study presents a fully automated workflow able to process a large collection of very high spatial resolution satellite images to produce actionable information in the application framework of smallholder farming. The workflow applies sequential image processing, extracts meaningful statistical information from agricultural parcels, and stores them in a crop spectrotemporal signature library. An important objective is to follow crop development through the season by analyzing multi-temporal and multi-sensor images. The workflow is based on free and open-source software, namely R, Python, Linux shell scripts, the Geospatial Data Abstraction Library, custom FORTRAN, C++, and the GNU Make utilities. We tested and applied this workflow on a multi-sensor image archive of over 270 VHSR WorldView-2, -3, QuickBird, GeoEye, and RapidEye images acquired over five different study areas where smallholder agriculture prevails.

“…Automated cloud classification methods based on a single Landsat image [41][42][43][44][45][46][47][48] achieved high accuracies in detecting clouds and their shadows. Recent cloud classification efforts based on multi-temporal images [49][50][51][52][53][54][55][56] have been proposed to better detect clouds and cloud shadows. The method proposed in [57] only deals with clouds and ignored cloud shadows, while the method proposed by Zhu et al [48] is designed to detect clouds and associated shadows simultaneously in Landsat images.…”

Section: Etm+ Ndvi Composingmentioning

confidence: 99%

Building a Better Urban Picture: Combining Day and Night Remote Sensing Imagery

Zhang

Thau

et al. 2015

Urban areas play a very important role in global climate change. There is increasing need to understand global urban areas with sufficient spatial details for global climate change mitigation. Remote sensing imagery, such as medium resolution Landsat daytime multispectral imagery and coarse resolution Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light imagery, has provided a powerful tool for characterizing and mapping cities, with advantages and disadvantages. Here we propose a framework to merge cloud and cloud shadow-free Landsat Normalized Difference Vegetation Index (NDVI) composite and DMSP/OLS Night Time Light (NTL) to characterize global urban areas at a 30 m resolution, through a Normalized Difference Urban Index (NDUI) to make full use of them while minimizing their limitations. We modify the maximum NDVI value multi-date image compositing method to generate the cloud and cloud shadow-free Landsat NDVI composite, which is critical for generating a global NDUI. Evaluation results show the NDUI can effectively increase the separability between urban areas and bare lands as well as farmland, capturing large scale urban extents and, at the same time, providing sufficient spatial details inside urban areas. With advanced