False-Color Satellite Imagery

2021 ◽  
pp. 466-466
Keyword(s):  
Satellite Imagery ◽  
False Color

scholarly journals PEMANFAATAN DATA SATELIT HIMAWARI 8 UNTUK MENDETEKSI SEBARAN ASAP: STUDI KASUS DI KALIMANTAN DAN SUMATERA TANGGAL 8 DAN 9 SEPTEMBER 2015

2017 ◽  
Vol 2 (2) ◽  
pp. 157
Author(s):  
Ayu Vista Wulandari ◽  
Ni Kadek Trisna Dewi ◽  
Wishnu Agum Swastiko
Keyword(s):  
Satellite Imagery ◽  
Forest Fire ◽  
Forest Fires ◽  
Near Infrared ◽  
Negative Impact ◽  
Distribution Data ◽  
Observation Data ◽  
False Color ◽  
Negative Impacts ◽  
Infrared Channel

The forest fires that occurred in the entire month of September 2015 was quite considerably disturbing many public activities in Borneo and Sumatera. The smoke which is caused by forest fire has negative impact for the surrounding environments, one of them is reducing horizontal visibility. Meteorological stations in Borneo and Sumatra recorded the lowest visibility occurred on September, 8th and 9th 2015 at average range was 100 m. Based on information of BMKG (Indonesian Agency of Meteorological, Climatological and Geophysics) noted that during the month of September 2015 there was a distribution of hotspots which indicates the occurrence of forest fire cases. This research is aimed to determine the potential of distribution of smoke by satellite imagery of Himawari 8 to reduce its negative impacts. By using this method that is by comparing the hotspot distribution data from BMKG with false color RGB image product (1 visible channel and 2 near infrared channel) along with trajectory of smoke’s distribution by utilizing application of GMSLPD SATAID. The distribution of smoke can be seen as an image with the brownish pattern which partially covered the area of Borneo and Sumatera. The result showed that the smoke’s distribution by the result of RGB imagery well-matched enough with the hotspot’s distribution data from BMKG, which the smoke almost covered most area of the western of Sumatera and center of Borneo. In this case also supported by the trajectory of smoke’s distribution which is derived from southeast-south and spread to the northwest-north in the researches area. By using the observation data from chosen meteorological stations showed a similar result with the above method. Thus, it can be assumed that by using satellite imagery of Himawari 8 is quite capable to discover smoke’s distribution caused by forest fires case. Keywords: Smoke, Satellite, Himawari 8, SATAID.

scholarly journals SOME ASPECTS OF SATELLITE IMAGERY INTEGRATION FROM EROS B AND LANDSAT 8

2016 ◽  
Vol XLI-B7 ◽  
pp. 647-652
Author(s):  
A. Fryskowska ◽  
M. Wojtkowska ◽  
P. Delis ◽  
A. Grochala
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Thermal Infrared ◽  
Visual Assessment ◽  
Landsat 8 ◽  
Image Integration ◽  
Panchromatic Image ◽  
False Color ◽  
New Sensors ◽  
Band Combinations

The Landsat 8 satellite which was launched in 2013 is a next generation of the Landsat remote sensing satellites series. It is equipped with two new sensors: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). What distinguishes this satellite from the previous is four new bands (coastal aerosol, cirrus and two thermal infrared TIRS bands). Similar to its antecedent, Landsat 8 records electromagnetic radiation in a panchromatic band at a range of 0.5‐0.9 μm with a spatial resolution equal to 15 m. In the paper, multispectral imagery integration capabilities of Landsat 8 with data from the new high resolution panchromatic EROS B satellite are analyzed. The range of panchromatic band for EROS B is 0.4‐0.9 μm and spatial resolution is 0.7 m. Research relied on improving the spatial resolution of natural color band combinations (bands: 4,3,2) and of desired false color band composition of Landsat 8 satellite imagery. For this purpose, six algorithms have been tested: Brovey’s, Mulitplicative, PCA, IHS, Ehler's, HPF. On the basis of the visual assessment, it was concluded that the best results of multispectral and panchromatic image integration, regardless land cover, are obtained for the multiplicative method. These conclusions were confirmed by statistical analysis using correlation coefficient, ERGAS and R-RMSE indicators.

scholarly journals SOME ASPECTS OF SATELLITE IMAGERY INTEGRATION FROM EROS B AND LANDSAT 8

2016 ◽  
Vol XLI-B7 ◽  
pp. 647-652 ◽  
Author(s):  
A. Fryskowska ◽  
M. Wojtkowska ◽  
P. Delis ◽  
A. Grochala
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Thermal Infrared ◽  
Visual Assessment ◽  
Landsat 8 ◽  
Image Integration ◽  
Panchromatic Image ◽  
False Color ◽  
New Sensors ◽  
Band Combinations

The Landsat 8 satellite which was launched in 2013 is a next generation of the Landsat remote sensing satellites series. It is equipped with two new sensors: the Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS). What distinguishes this satellite from the previous is four new bands (coastal aerosol, cirrus and two thermal infrared TIRS bands). Similar to its antecedent, Landsat 8 records electromagnetic radiation in a panchromatic band at a range of 0.5‐0.9 μm with a spatial resolution equal to 15 m. In the paper, multispectral imagery integration capabilities of Landsat 8 with data from the new high resolution panchromatic EROS B satellite are analyzed. The range of panchromatic band for EROS B is 0.4‐0.9 μm and spatial resolution is 0.7 m. Research relied on improving the spatial resolution of natural color band combinations (bands: 4,3,2) and of desired false color band composition of Landsat 8 satellite imagery. For this purpose, six algorithms have been tested: Brovey’s, Mulitplicative, PCA, IHS, Ehler's, HPF. On the basis of the visual assessment, it was concluded that the best results of multispectral and panchromatic image integration, regardless land cover, are obtained for the multiplicative method. These conclusions were confirmed by statistical analysis using correlation coefficient, ERGAS and R-RMSE indicators.

Two structural states of the Z-band in cardiac and skeletal muscle

1989 ◽  
Vol 47 ◽  
pp. 1022-1023
Author(s):  
J.P. Schroeter ◽  
M.A. Goldstein ◽  
J.P. Bretaudiere ◽  
L.H. Michael ◽  
R.L. Sass
Keyword(s):  
Skeletal Muscle ◽  
Cross Section ◽  
Real Space ◽  
Contractile State ◽  
Contour Maps ◽  
False Color ◽  
Grey Scale ◽  
Fourier Filtering ◽  
Cardiac Muscles ◽  
Enhancement Algorithm

We have recently established the existence of two structural states of the Z band lattice in cross section in cardiac as well as in skeletal muscle. The two structural states are related to the contractile state of the muscle. In skeletal muscle at rest, the Z band is in the small square (ss) lattice form, but tetanized muscle exhibits the basket weave (bw) form. In contrast, unstimu- lated cardiac muscle exhibits the bw form, but cardiac muscles exposed to EGTA show the ss form.We have used two-dimensional computer enhancement techniques on digitized electron micrographs to compare each lattice form as it appears in both cardiac and skeletal muscle. Both real space averaging and fourier filtering methods were used. Enhanced images were displayed as grey-scale projections, as contour maps, and in false color.There is only a slight difference between the lattices produced by the two different enhancement techniques. Thus the information presented in these images is not likely to be an artifact of the enhancement algorithm.

LambdaNet: A Fully Convolutional Architecture for Directional Change Detection

2020 ◽  
Vol 2020 (8) ◽  
pp. 114-1-114-7
Author(s):  
Bryan Blakeslee ◽  
Andreas Savakis
Keyword(s):  
Change Detection ◽  
Satellite Imagery ◽  
Classification Scheme ◽  
Deep Architecture ◽  
Binary Process ◽  
Directional Change ◽  
Class Labels ◽  
Image Pairs

Change detection in image pairs has traditionally been a binary process, reporting either “Change” or “No Change.” In this paper, we present LambdaNet, a novel deep architecture for performing pixel-level directional change detection based on a four class classification scheme. LambdaNet successfully incorporates the notion of “directional change” and identifies differences between two images as “Additive Change” when a new object appears, “Subtractive Change” when an object is removed, “Exchange” when different objects are present in the same location, and “No Change.” To obtain pixel annotated change maps for training, we generated directional change class labels for the Change Detection 2014 dataset. Our tests illustrate that LambdaNet would be suitable for situations where the type of change is unstructured, such as change detection scenarios in satellite imagery.

Mask R-CNN based segmentation method for satellite imagery of photovoltaics generation systems

2020 ◽  
Author(s):  
SiMing Liang ◽  
FengYang Qi ◽  
YiFan Ding ◽  
Rui Cao ◽  
Qiang Yang ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Segmentation Method
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