scholarly journals Robust Registration of Multispectral Satellite Images Based on Structural and Geometrical Similarity

2021 ◽  
pp. 1-5
Author(s):  
Guohua Lv ◽  
Qiang Chi ◽  
Mohammad Awrangjeb ◽  
Jian Li
Keyword(s):  
Satellite Images ◽  
Geometrical Similarity ◽  
Multispectral Satellite Images

scholarly journals Forensic Geoarchaeology in the Search for Missing Persons

2021 ◽  
Vol 1 (1) ◽  
pp. 8-15
Author(s):  
Pier Matteo Barone ◽  
Rosa Maria Di Maggio ◽  
Silvia Mesturini
Keyword(s):  
Satellite Images ◽  
Social Phenomenon ◽  
Geographic Profiling ◽  
Missing Persons ◽  
The Social ◽  
Multispectral Satellite Images

Despite widespread concern over missing persons, there has always been little clarity on what the word “missing” means. Although the category of young runaways is, indeed, an important cluster, other popular concepts related to disappearances describe a portion of missing persons. Thus, the following question persists: What exactly does “missing” mean? In this brief communication, we would like to open a discussion about the social phenomenon of missing persons and the consequent deployment of people and techniques to find those persons. In particular, the benefits of some forensic geoarchaeological approaches that are not yet fully standardized will be highlighted, such as geographic profiling and the use of multispectral satellite images, in order to provide materials for future searching protocols.

Interactive processing of satellite images for structural and lithological mapping in northeast Africa

1986 ◽  
Vol 123 (4) ◽  
pp. 393-403 ◽  
Author(s):  
S. M. Berhe ◽  
D. A. Rothery
Keyword(s):  
Principal Components ◽  
Satellite Images ◽  
Spatial Filtering ◽  
Digital Processing ◽  
Horn Of Africa ◽  
Lithological Mapping ◽  
Late Precambrian ◽  
Late Proterozoic ◽  
Pan African ◽  
Multispectral Satellite Images

AbstractInteractive digital processing of multispectral satellite images (Landsat MSS) using principal components transformations and spatial filtering has clarified the position of continuous sutures linking apparently isolated Pan African (late Proterozoic) ophiolites. These have been field-checked and an arrangement of Pan African suture zones is proposed. Spatial filtering has also highlighted faults with various trends which can be related to the late Precambrian tectonics of the Horn of Africa region.

scholarly journals Improvement of Clay and Sand Quantification Based on a Novel Approach with a Focus on Multispectral Satellite Images

2018 ◽  
Vol 10 (10) ◽  
pp. 1555 ◽  
Author(s):  
Caio Fongaro ◽  
José Demattê ◽  
Rodnei Rizzo ◽  
José Lucas Safanelli ◽  
Wanderson Mendes ◽  
...  
Keyword(s):  
Model Calibration ◽  
Large Scale ◽  
Satellite Images ◽  
Satellite Image ◽  
Soil Mapping ◽  
Spectral Signatures ◽  
Novel Approach ◽  
Multispectral Satellite Images ◽  
Reduced Costs ◽  
Multispectral Satellite Image

Soil mapping demands large-scale surveys that are costly and time consuming. It is necessary to identify strategies with reduced costs to obtain detailed information for soil mapping. We aimed to compare multispectral satellite image and relief parameters for the quantification and mapping of clay and sand contents. The Temporal Synthetic Spectral (TESS) reflectance and Synthetic Soil Image (SYSI) approaches were used to identify and characterize texture spectral signatures at the image level. Soil samples were collected (0–20 cm depth, 919 points) from an area of 14,614 km2 in Brazil for reference and model calibration. We compared different prediction approaches: (a) TESS and SYSI; (b) Relief-Derived Covariates (RDC); and (c) SYSI plus RDC. The TESS method produced highly similar behavior to the laboratory convolved data. The sandy textural class showed a greater increase in average spectral reflectance from Band 1 to 7 compared with the clayey class. The prediction using SYSI produced a better result for clay (R2 = 0.83; RMSE = 65.0 g kg−1) and sand (R2 = 0.86; RMSE = 79.9 g kg−1). Multispectral satellite images were more stable for the identification of soil properties than relief parameters.

scholarly journals Ship-Iceberg Classification in SAR and Multispectral Satellite Images with Neural Networks

2020 ◽  
Vol 12 (15) ◽  
pp. 2353
Author(s):  
Henning Heiselberg
Keyword(s):  
Neural Networks ◽  
Satellite Images ◽  
Deep Neural Networks ◽  
Data Augmentation ◽  
Neural Nets ◽  
The Arctic ◽  
Support Vector ◽  
Sar Images ◽  
Multispectral Satellite Images ◽  
The Cost

Classification of ships and icebergs in the Arctic in satellite images is an important problem. We study how to train deep neural networks for improving the discrimination of ships and icebergs in multispectral satellite images. We also analyze synthetic-aperture radar (SAR) images for comparison. The annotated datasets of ships and icebergs are collected from multispectral Sentinel-2 data and taken from the C-CORE dataset of Sentinel-1 SAR images. Convolutional Neural Networks with a range of hyperparameters are tested and optimized. Classification accuracies are considerably better for deep neural networks than for support vector machines. Deeper neural nets improve the accuracy per epoch but at the cost of longer processing time. Extending the datasets with semi-supervised data from Greenland improves the accuracy considerably whereas data augmentation by rotating and flipping the images has little effect. The resulting classification accuracies for ships and icebergs are 86% for the SAR data and 96% for the MSI data due to the better resolution and more multispectral bands. The size and quality of the datasets are essential for training the deep neural networks, and methods to improve them are discussed. The reduced false alarm rates and exploitation of multisensory data are important for Arctic search and rescue services.

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