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Application of Image Processing and Data in Remote Sensing
Published in Ankur Dumka, Alaknanda Ashok, Parag Verma, Poonam Verma, Advanced Digital Image Processing and Its Applications in Big Data, 2020
Ankur Dumka, Alaknanda Ashok, Parag Verma, Poonam Verma
Satellite images obtained may be in multispectral and panchromatic mode. Remote sensing satellites capture the information in the form of digital data. These data are stored in formats of Band Sequential, Band Interleaved by Lines, and Band Interleaved by Pixels. Each of the formats of the satellite images has a header and trailer apart from the information such as date of acquisition, altitude of satellite, and sun angle. All the stated details help to correctly find the data geometrically. In the Band Sequential format, all the data for a single band covering the complete image of a scenario are stored in one single file. Each band is saved as a separate image sequentially for a multiband image. In order to extract information from five band images, five files have to be read, whereas in the Band Interleaved by Lines format, the images of the different bands are stored in the computer memory line by line, where each line is represented in all the bands before recording the next line. This makes the lines inseparable, and if the format is required to be analyzed, then all the lines are required to be analyzed. In the Band Interleaved by Pixels, the image information is stored pixel by pixel where the brightness of the image is stored in the pixels. GeoTIFF is the format that stores geographical and cartographic data. It is a metadata format that also provides coordinates given by the satellites.
Geographic data I/O
Published in Robin Lovelace, Jakub Nowosad, Jannes Muenchow, Geocomputation with R, 2019
Robin Lovelace, Jakub Nowosad, Jannes Muenchow
The raster file format (native to the raster package) is used when a file extension is invalid or missing. Some raster file formats come with additional options. You can use them with the options parameter24. GeoTIFF files, for example, can be compressed using COMPRESS: writeRaster(x = single_layer, filename = ”my_raster.tif”, datatype = ”INT2U”, options = c(”COMPRESS=DEFLATE”), overwrite = TRUE)
Acquiring Data: EarthExplorer, GloVis, LandsatLook Viewer, and NRCS Geospatial Data Gateway
Published in Stacy A. C. Nelson, Siamak Khorram, Image Processing and Data Analysis with ERDAS IMAGINE®, 2018
Stacy A. C. Nelson, Siamak Khorram
NOTE: For L8 OLI/TIRS products, the LandsatLook options provide high-resolution JPEG images (such as LandsatLook Natural Color Image) for further examining the scene area or use as ancillary data. The “Level 1 GeoTIFF Data Product” provides the image data in the form of GeoTIFF images (i.e., LandsatLook Images with Geographic Reference). In a GeoTIFF image, the georeferencing information is embedded within the individual TIFF image files making it possible to find coordinate locations on the image.
Remote sensing and GIS techniques to monitor morphological changes along the middle-lower Vistula river, Poland
Published in International Journal of River Basin Management, 2021
The present analysis was performed using Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 OLI data, all having a spatial resolution of 30 m (Gilvear & Bryant, 2016) and covering the period 1985–2017 (Table 2). The satellite images (Figure 4) were downloaded as a Level-1 Data Product in GeoTIFF format from the United States Geological Survey (USGS) Earth Explorer service (earthexplorer.usgs.gov) and handled with the freeware GIS software QGis. The data were pre-processed using common Landsat image preparation approaches, including radiometric processing, dark area subtraction and geometric correction (Jensen, 2005). As cloud cover is a major limitation when using satellite data (Julien & Sobrino, 2019), a threshold was set to 10% to reject images having a higher cloud cover, following similar studies available in the literature. Given that the radiation from the Earth’s surface undergoes significant interaction with the atmosphere before it reaches the satellite sensor, potentially altering the monitored signal (Hadjimitsis et al., 2010), the images were pre-processed to account for the atmospheric conditions by means of the Semi-Automatic Classification Plugin of QGis. Provided that each image has information on the acquisition date, it was possible to correlate them with the hydrology measured at the gauging station of Modlin. No images referring to very dry periods or flooding events were considered, resulting in the extraction of consistent riverbank information (Gurnell et al., 1994).
Principal polar spectral indices for mapping mangroves forest in South East Asia: study case Indonesia
Published in International Journal of Digital Earth, 2019
Fatwa Ramdani, Sabaruddin Rahman, Chandra Giri
The methodology used in this study is summarized in Figure 3. Grass GIS and QGIS open source software was used to generate the PPS indices. The Grass GIS was employed for pre-processing the Landsat images and for the Principal Component Analysis (PCA). The result then exported to GeoTIFF format and processed in QGIS environment for PPS indices transformation, NDVI calculation, and RGB band composite. For the accuracy and precision measurement, the result of fieldwork then imported to QGIS for post-processing analysis. The specification for computing platform used in this study is as following: Processor: Intel ®Pentium Core i7-3612QM, CPU @ 2.10GHzRAM: 8.00 GBSSD: 500GBOperating System: Windows 10, 64-bit Operating System, x64-based processorVGA Card: NVIDIA GEFORCE® with CUDA™
Uni-temporal Sentinel-2 imagery for wildfire detection using deep learning semantic segmentation models
Published in Geomatics, Natural Hazards and Risk, 2023
Ali Mahdi Al-Dabbagh, Muhammad Ilyas
In our wildfire dataset, each image has a spatial resolution of 10 m and consists of thirteen bands. The image is saved using the Universal Transverse Mercator (UTM) as the coordinate system and GeoTiff as a format file. The dataset has 21,690 images containing the burned area’s pixels. The mask is a binary image of the burned area that consists of two categories: the burned area in the foreground and the non-burned area in the background. The values of each pixel are saved in an 8-bit unsigned integer with a value of 1 for the burned area and 0 for the non-burned area. Tables 3 and 4 show the distribution numbers of images and masks depending on wildfire areas, and Figure 3 depicts the dataset’s image and mask samples.