Image processing and satellite imagery analysis in environments

A special issue of Geosciences (ISSN 2076-3263).

Deadline for manuscript submissions: closed (15 November 2019) | Viewed by 15843

Special Issue Editor

Department of Applied Science and Technology, Polytechnic University of Turin, 10129 Turin, Italy
Interests: general physics and mathematics; optics; software; image processing applied to microscopy and satellite imagery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Environmental studies are spanning from the small scale of local environments to the global scale of our planet, and even beyond. To understand the evolution of the Earth’s life and, in particular, to recognize how the human activities affected and are affecting it, we need to collect and analyze data concerning its huge complex system. Environments of smaller scales are, at the same time, subjected to any global change and to the local direct human actions. The extent of the local problems, which can appear due to the climate change for instance, needs to be carefully considered, in order to prevent any possible risk. Concurrently, human activities need to be planned, to reduce both local and global effects. All these studies and consequent human actions require data and models.

One of the tools that we have at our disposal to receive information for environmental studies is satellite technology. Earth remote sensing satellites are those specifically designed for the observation of our planet from orbit and are basically intended for environmental monitoring and meteorology, but also for map making. In environmental monitoring, the satellites give us information by means of a remote detection of any change of Earth's surface, that is, of its temperature, rainfall, vegetation, state of lakes, seas and oceans, state of the surface of ice fields, and so on. By recording vegetation changes, for instance, the health of vegetation can be measured and suffering caused by droughts monitored.

The Special Issue of Geosciences titled: “Image Processing and Satellite Imagery Analysis in Environments" is here proposed to stress the role of satellites in the analysis and monitoring of environments. However, satellite imagery needs to be processed to extract data from it. Many methods and algorithms exist and many can be developed which can properly enhance and extract information from satellite maps, in different ranges of frequencies, recorded at different scales, from local to global environments.

Let me suggest a few subjects which can be suitable for the Special Issue, keeping in mind that many more could be added to the list:

- Satellite imagery and the monitoring of polar ice and glaciers;

- Coastline erosion and deposition;

- Internal waves in oceans and atmospheres;

- Sand dunes and their motion seen from space;

- The vegetation indices and their monitoring from space;

- The study of life seen from pace; for instance, how animals interact with vegetation;

- The impact of urbanization on rural area; agriculture and deforestation;

- The impact of human activities on cultural heritage and natural sites.

In addition to the problems concerning the global scale, the Special Issue aims to collect works which are analyzing problems on a local scale. Applications involving reliable quantitative results, based on image processing algorithms, such as image segmentation, edge detection, filtering to enhance details, directional analyses, and attribute quantification, are welcome.

Of course, active research in image processing is not limited to the abovementioned methods, much like environmental studies are not limited to the list given above. Awaiting to receive cases and methods, I solicit your contribution to this Special Issue of Geosciences, devoted to the use of image processing in extracting information from satellite imagery, with the aim of helping any decision-making process, which can be devised in environmental monitoring.

Dr. Amelia Carolina Sparavigna
Guest Editor

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Keywords

  • Environmental monitoring
  • Image segmentation
  • Edge detection
  • Vegetation indices
  • Deforestation
  • Urbanization
  • Hazard and risk management

Published Papers (4 papers)

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Research

12 pages, 3804 KiB  
Article
Bat Algorithm Based Non-linear Contrast Stretching for Satellite Image Enhancement
by Anju Asokan, Daniela E. Popescu, J. Anitha and D. Jude Hemanth
Geosciences 2020, 10(2), 78; https://doi.org/10.3390/geosciences10020078 - 21 Feb 2020
Cited by 18 | Viewed by 3857
Abstract
The remote sensing images acquired from the satellites are low contrast images. The availability of low contrast images and failure of the traditional methods such as Histogram Equalization and Gamma correction in preserving the brightness levels in the image are the main issues [...] Read more.
The remote sensing images acquired from the satellites are low contrast images. The availability of low contrast images and failure of the traditional methods such as Histogram Equalization and Gamma correction in preserving the brightness levels in the image are the main issues in satellite image processing. This paper proposes an optimized contrast stretching using non-linear transformation for image enhancement. The non-linear transformation is influenced by the appropriate choice of control parameters for the sample images since manual tuning for individual images is tedious. A Bat algorithm based tuning is employed for the automated selection of control parameters in the transformation. The performance of the optimization algorithm is compared against other metaheuristic algorithms such as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). It is noted that the bat algorithm based contrast enhancement outperforms the other optimization techniques in terms of metrics such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Absolute Mean Brightness Error (AMBE), Entropy and CPU time (Central Processing Unit). Full article
(This article belongs to the Special Issue Image processing and satellite imagery analysis in environments)
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10 pages, 2502 KiB  
Article
Monitoring Coastline Dynamics of Alakol Lake in Kazakhstan Using Remote Sensing Data
by Adilet Valeyev, Marat Karatayev, Ainagul Abitbayeva, Saule Uxukbayeva, Aruzhan Bektursynova and Zhanerke Sharapkhanova
Geosciences 2019, 9(9), 404; https://doi.org/10.3390/geosciences9090404 - 19 Sep 2019
Cited by 17 | Viewed by 3777
Abstract
Alakol Lake is one of the largest hydrologically closed lake located in Balkash-Alakol River Basin in southeast Kazakhstan. Having a coastline approximately at 490 km, Alakol Lake has faced multiple threats due to both natural and anthropogenic factors as a result of tectonic [...] Read more.
Alakol Lake is one of the largest hydrologically closed lake located in Balkash-Alakol River Basin in southeast Kazakhstan. Having a coastline approximately at 490 km, Alakol Lake has faced multiple threats due to both natural and anthropogenic factors as a result of tectonic movements, geology, wind-wave conditions, growing tourism activities, fishing, and transport, etc. The present study aims to investigate the historical trends in coastline changes along Alakol Lake in Kazakhstan and estimate its change rate by using remote sensing data in particular scale-space images Landsat-5 TM, 7 ETM+, 8 OLI, and Sentinel-2A. Based on Landsat and Sentinel data, the modified normalized difference water index was calculated to demonstrate the coastline changes along Alakol Lake between 1990 and 2018. Moreover, the monitoring and analysis of coastline dynamics is based on the main morphometric characteristics of Alakol Lake including water surface area, coastline length, geomorphology of the coast, etc. Our results reveal that there is a continuous coastline retreat, depending on the coast types. For example, in the case of the denudation coasts, a land inundation was from 120 to 270 m between 1990 and 2018. In the case of the accumulative coast (mainly northeast, north, and northwest coasts) a land inundation was from 200 to 900 m. A vast area of agricultural land around Alakol Lake become flooded and lost. This study demonstrates the importance of monitoring coastline dynamics because it provides essential information for understanding the coastal response to contemporary nature and anthropogenic impacts. Full article
(This article belongs to the Special Issue Image processing and satellite imagery analysis in environments)
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22 pages, 4374 KiB  
Article
NN-Based Prediction of Sentinel-1 SAR Image Filtering Efficiency
by Oleksii Rubel, Vladimir Lukin, Andrii Rubel and Karen Egiazarian
Geosciences 2019, 9(7), 290; https://doi.org/10.3390/geosciences9070290 - 29 Jun 2019
Cited by 11 | Viewed by 3139
Abstract
Images acquired by synthetic aperture radars are degraded by speckle that prevents efficient extraction of useful information from radar remote sensing data. Filtering or despeckling is a tool often used to improve image quality. However, depending upon image and noise properties, the quality [...] Read more.
Images acquired by synthetic aperture radars are degraded by speckle that prevents efficient extraction of useful information from radar remote sensing data. Filtering or despeckling is a tool often used to improve image quality. However, depending upon image and noise properties, the quality of improvement can vary. Besides, a quality can be characterized by different criteria or metrics, where visual quality metrics can be of value. For the case study of discrete cosine transform (DCT)based filtering, we show that improvement of radar image quality due to denoising can be predicted in a simple and fast way, especially if one deals with particular type of radar data such as images acquired by Sentinel-1. Our approach is based on application of a trained neural network that, in general, might have a different number of inputs (features). We propose a set of features describing image and noise statistics from different viewpoints. From this set, that contains 28 features, we analyze different subsets and show that a subset of the 13 most important and informative features leads to a very accurate prediction. Test image generation and network training peculiarities are discussed. The trained neural network is then tested using different verification strategies. The results of the network application to test and real-life radar images are presented, demonstrating good performance for a wide set of quality metrics. Full article
(This article belongs to the Special Issue Image processing and satellite imagery analysis in environments)
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18 pages, 16722 KiB  
Article
Identification of Alteration Minerals from Unstable Reflectance Spectra Using a Deep Learning Method
by Soichiro Tanaka, Hideo Tsuru, Kazuaki Someno and Yasushi Yamaguchi
Geosciences 2019, 9(5), 195; https://doi.org/10.3390/geosciences9050195 - 28 Apr 2019
Cited by 4 | Viewed by 3902
Abstract
Hydrothermal alteration minerals, which are important as indicators in the exploration of ore deposits, exhibit diagnostic absorption peaks in the short-wavelength infrared region. We propose an approach for the identification of alteration minerals that uses a deep learning method and compare it with [...] Read more.
Hydrothermal alteration minerals, which are important as indicators in the exploration of ore deposits, exhibit diagnostic absorption peaks in the short-wavelength infrared region. We propose an approach for the identification of alteration minerals that uses a deep learning method and compare it with conventional identification methods which use numerical calculation. Inexpensive spectrometers often tend to show errors in the wavelength direction, even after wavelength calibration, which causes erroneous mineral identification. In this study, deep learning is applied to extract features from reflectance spectra to remove such errors. Two typical deep learning methods—a convolutional neural network and a multi-layer perceptron—were applied to spectral reflectance data, with and without hull quotient processing, and their accuracy rates and f-values were evaluated. There was an improvement in mineral identification accuracy when hull quotient processing was applied to the learning data. Full article
(This article belongs to the Special Issue Image processing and satellite imagery analysis in environments)
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