Calculating surface reflection characteristics by optical remote sensing

The growing number of available optical remote sensing data providing large spatial and temporal coverage enables the coherent and gapless observation of the earth’s surface on the scale of whole countries or continents. To produce datasets of that size, individual satellite scenes have to be stitched together forming so-called mosaics. Here the problem arises that the different images feature varying radiometric properties depending on the momentary acquisition conditions. The interpretation of optical remote sensing data is to a great extent based on the analysis of the spectral composition of an observed surface reflection. Therefore the normalization of all images included in a large image mosaic is necessary to ensure consistent results concerning the application of procedures to the whole dataset. In this work an algorithm is described which enables the automated spectral harmonization of satellite images to a reference scene. As the stable and satisfying functionality of the proposed algorithm was already put to operational use to process a high number of SPOT-4/-5, IRS LISS-III and Landsat-5 scenes in the frame of the European Environment Agency's Copernicus/GMES Initial Operations (GIO) High-Resolution Layer (HRL) mapping of the HRL Forest for 20 Western, Central and (South)Eastern European countries, it is further evaluated on its reliability concerning the application to newer Sentinel-2 multispectral imaging products. The results show that the algorithm is comparably efficient for the processing of satellite image data from sources other than the sensor configurations it was originally designed for.

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356. Development of an Semi-OMER Beam Configuration for Identification of Gas Leak Time and Location With Use of an Optical Remote Sensing and Computed Tomographic Technique

10.3320/1.2762758 ◽

1999 ◽

Author(s):

D.Y. Park ◽

M.G. Yost ◽

R. Hashimonay ◽

C.F. Wu

Keyword(s):

Remote Sensing ◽

Optical Remote Sensing ◽

Computed Tomographic ◽

Gas Leak ◽

Tomographic Technique

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Arbitrary Oriented Ship Detection in Optical Remote Sensing Images via Partially Supervised Learning

2020 39th Chinese Control Conference (CCC) ◽

10.23919/ccc50068.2020.9188661 ◽

2020 ◽

Author(s):

Linhao Li ◽

Zhiqiang Zhou ◽

Lingjuan Miao ◽

Junfu Liu ◽

Xiaowu Xiao

Keyword(s):

Remote Sensing ◽

Supervised Learning ◽

Optical Remote Sensing ◽

Remote Sensing Images ◽

Ship Detection ◽

Partially Supervised Learning

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Characterization of the auroral ionosphere using optical remote-sensing techniques

10.2514/6.1980-1383 ◽

1980 ◽

Author(s):

R. SEARS

Keyword(s):

Remote Sensing ◽

Optical Remote Sensing ◽

Auroral Ionosphere ◽

Remote Sensing Techniques

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Optical remote sensing of the ocean

10.2514/6.1984-380 ◽

1984 ◽

Author(s):

W. HOVIS

Keyword(s):

Remote Sensing ◽

Optical Remote Sensing

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Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

Engineering and Technology Journal ◽

10.30684/etj.v38i4a.399 ◽

2020 ◽

Vol 38 (4A) ◽

pp. 510-514

Author(s):

Tay H. Shihab ◽

Amjed N. Al-Hameedawi ◽

Ammar M. Hamza

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Artificial Neural Network ◽

Random Forest ◽

Satellite Image ◽

Landsat 8 ◽

Optical Remote Sensing ◽

Data Set ◽

Artificial Neural ◽

Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019. They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

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