Multi-Scale Approach using Remote Sensing Techniques for Lithium Pegmatite Exploration: First Results

2020 ◽  
Author(s):  
Joana Cardoso-Fernandes ◽  
Ana Claudia Teodoro ◽  
Alexandre Lima ◽  
Christian Mielke ◽  
Friederike Korting ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Multi Scale ◽  
First Results ◽  
Remote Sensing Techniques

scholarly journals Image Spectrometry and Copernicus data for Heritage Mapping in the Jarama Valley, Spain

2019 ◽  
Vol 2 ◽  
pp. 1-5
Author(s):  
Juan Gregorio Rejas Ayuga ◽  
Francisco Javier González Matesanz ◽  
Pilar Sánchez-Ortiz
Keyword(s):  
Remote Sensing ◽  
Spanish Civil War ◽  
Spectral Characteristics ◽  
Biophysical Parameters ◽  
Military Installations ◽  
Thermal Anomalies ◽  
Digital Heritage ◽  
First Results ◽  
Remote Sensing Techniques ◽  
Spectral Ranges

<p><strong>Abstract.</strong> For millions of years, the Jarama Valley, located in the middle of Spain, has fluctuated physically and geomorphological, supposing over the centuries a crucial settlement for wildlife and different human cultures as an efficient route through South Europa. Currently, this scenario, which consist of a vast and intricate network of military installations due to the Battle of Jarama in the Spanish Civil War, mixed with incredible paleontological sites, Celtiberian cities and roman, are part of a great-protected natural and cultural classified as the Southeast Regional Park close to Madrid City.</p><p>The aim of this work is developing a geospatial methodology for the digital heritage mapping in the Jarama Valley using geomatics’ technologies in situ, drones' data, image spectrometry and the Copernicus European program, both their active and passive sensors. Data from Sentinel 1, Sentinel 2 and airborne sensors have analysed according to remote sensing techniques to recognize the potential remains and to reconstruct the heritage landscapes of three test areas in the Jarama Valley. We have investigated the spectral characteristics of main biophysical parameters in the pattern recognition of man-made materials in several remote sensing scenes of the Jarama Valley. Spectral ranges from drones' data are used to validate data cubes from multisperspectral sensor ATM. Sentinel 1 polarimetric products and thermal anomalies have calculated in order to contrast evidences of buried remains and confirm land change detection over the last decades. First results and their consequences on the digital heritage mapping of the Jarama Valley are discussed, representing a first point of protection and an advance of a full cultural and natural heritage research project in this region.</p>

scholarly journals Análisis del efecto de variables ambientales en la estimación de la erosionabilidad (Factor K) = Analysis of the Effect of Environmental Variables on the Soil Erodibility Estimation (K Factor)

2018 ◽  
pp. 195
Author(s):  
Pedro Perez Cutillas ◽  
Gonzalo G. Barberá ◽  
Carmelo Conesa García
Keyword(s):  
Remote Sensing ◽  
Environmental Variables ◽  
Input Data ◽  
Vegetation Index ◽  
Mineralogical Composition ◽  
Soil Erodibility ◽  
Estimation Algorithms ◽  
K Factor ◽  
Remote Sensing Techniques

El objetivo principal de este trabajo se centra en la determinación y análisis de las variables ambientales que influyen en las divergencias de las estimaciones de erosionabilidad a partir de dos métodos, aplicando tres algoritmos de estimación del Factor K. La exploración de esta información permite conocer el peso que ejerce el origen de los datos de entrada a los modelos en el cómputo de erosionabilidad y qué importancia tiene en función del algoritmo elegido para la estimación del Factor K. Los resultados muestran que las pendientes, así como los índices de vegetación (NDVI) y de composición mineralógico (IOI) obtenidos mediantes técnicas de teledetección han   mostrado los valores de asociación más elevados entre ambos métodos.The main goal of this work is to determine and analyze the influence of environmental variables on the changes of two erodibility methods, through the application of three estimation algorithms of K Factor. The analysis of this information allows knowing the significance of the input data to the models in the erodibility estimation, and likewise the consequence of the algorithm selected for the estimation of K Factor. The results show that the slopes, as well as the vegetation index (NDVI) and the mineralogical composition index (IOI), generated both by remote sensing techniques, have shown the highest values of association between methods.

Semantic Relation Model and Dataset for Remote Sensing Scene Understanding

2021 ◽  
Vol 10 (7) ◽  
pp. 488
Author(s):  
Peng Li ◽  
Dezheng Zhang ◽  
Aziguli Wulamu ◽  
Xin Liu ◽  
Peng Chen
Keyword(s):  
Remote Sensing ◽  
Scene Understanding ◽  
Deep Understanding ◽  
Remote Sensing Images ◽  
Convolutional Network ◽  
Scene Graph ◽  
Multi Scale ◽  
Relationship Extraction ◽  
High Level ◽  
Graph Generation

A deep understanding of our visual world is more than an isolated perception on a series of objects, and the relationships between them also contain rich semantic information. Especially for those satellite remote sensing images, the span is so large that the various objects are always of different sizes and complex spatial compositions. Therefore, the recognition of semantic relations is conducive to strengthen the understanding of remote sensing scenes. In this paper, we propose a novel multi-scale semantic fusion network (MSFN). In this framework, dilated convolution is introduced into a graph convolutional network (GCN) based on an attentional mechanism to fuse and refine multi-scale semantic context, which is crucial to strengthen the cognitive ability of our model Besides, based on the mapping between visual features and semantic embeddings, we design a sparse relationship extraction module to remove meaningless connections among entities and improve the efficiency of scene graph generation. Meanwhile, to further promote the research of scene understanding in remote sensing field, this paper also proposes a remote sensing scene graph dataset (RSSGD). We carry out extensive experiments and the results show that our model significantly outperforms previous methods on scene graph generation. In addition, RSSGD effectively bridges the huge semantic gap between low-level perception and high-level cognition of remote sensing images.

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