Two-dimensional satellite image compression using compressive sensing

Compressive sensing is receiving a lot of attention from the image processing research community as a promising technique for image recovery from very few samples. The modality of compressive sensing technique is very useful in the applications where it is not feasible to acquire many samples. It is also prominently useful in satellite imaging applications since it drastically reduces the number of input samples thereby reducing the storage and communication bandwidth required to store and transmit the data into the ground station. In this paper, an interior point-based method is used to recover the entire satellite image from compressive sensing samples. The compression results obtained are compared with the compression results from conventional satellite image compression algorithms. The results demonstrate the increase in reconstruction accuracy as well as higher compression rate in case of compressive sensing-based compression technique.

MODELLING THE CHLOROPHYLL-A CONCENTRATION OF LAGUNA LAKE USING HIMAWARI-8 SATELLITE IMAGERY AND MACHINE LEARNING ALGORITHMS FOR NEAR REAL TIME MONITORING

Recent studies have investigated the use of satellite imaging combined with machine learning for modelling the Chlorophyll-a (Chl-a) concentration of bodies of water. However, most of these studies use satellite data that lack the temporal resolution needed to monitor dynamic changes in Chl-a in productive lakes like Laguna Lake. Thus, the aim of this paper is to present the methodology for modelling the Chl-a concentration of Laguna Lake in the Philippines using satellite imaging and machine learning algorithms. The methodology uses images from the Himawari-8 satellite, which have a spatial resolution of 0.5–2 km and are taken every 10 minutes. These are converted into a GeoTIFF format, where differences in spatial resolution are resolved. Additionally, radiometric correction, resampling, and filtering of the Himawari-8 bands to exclude cloud-contaminated pixels are performed. Subsequently, various regression and gradient boosting machine learning algorithms are applied onto the train dataset and evaluated, namely: Simple Linear Regression, Ridge Regression, Lasso Regression, and Light Gradient Boosting Model (LightGBM). The results of this study show that it is indeed possible to integrate algorithms in Machine Learning in modelling the near real-time variations in Chl-a content in a body of water, specifically in the case of Laguna Lake, to an acceptable margin of error. Specifically, the regression models performed similarly with a train RMSE of 1.44 and test RMSE of 2.51 for Simple Linear Regression and 2.48 for Ridge and Lasso Regression. The linear regression models exhibited a larger degree of overfitting than the LightGBM model, which had a 2.18 train RMSE.

The role of geophysics in the construction and stability assessment of tunnels

Geophysics play a vital role in the constructions of any major manmade structures in the world. One of those being the tunnels. In depth understanding of geophysical methods and a lot of information are needed in order to design a tunnel construction project. Comprehensive investigation on the ground condition has to be done before the field preparation study that will determine the stand-up time and the groundwater condition that may disrupt the tunnel construction. For tunnel stability assessment, an integration of geophysical methods is a must in order to obtain the most accurate results. Satellite imaging interpretation emphasizes on the structural tracing of negative lineament while field mapping emphasizes on location of underground seepage and major tectonic structures such as faults, joints and shear zones. Geoelectrical resistivity tomography survey is able to identify the differences in resistivity of Earth’s materials based on the water content inside of them. The best course of remediation could only be chosen once the output from all these studies are made available.

The Perspective from Space Unlocks the Amazon Water Cycle

Satellite imaging and remote sensing offer unique insights into the Amazon’s complex hydrology. A new review summarizes decades of findings and charts a path forward for new remote sensing missions.

Galaxian Contamination in Galactic Reddening Maps

Estimating the amount of foreground extinction due to the Milky Way dust along the line of sight is often a first step in determining the luminosity of an object. The amount of Galactic dust inferred by infrared emission maps can be contaminated by infrared light from nearby galaxies. By comparing extinction values at and around the location of nearby galaxies, we compile a list of 95 galaxies that likely contaminate the maps with an excess or improperly subtracted galaxian infrared emission, and tabulate our recommended values for the MW contribution. In addition to M82, which inspired this work, six more sources have an excess visual extinction A V of at least 0.05 mag greater than our annular values; including M83, NGC 1313, NGC 6822, NGC 918, UGC 11501, and UGC 11797. M33 is shown to be oversubtracted. NGC 88 and the outskirts of NGC 4258 are located in gaps in the Infrared Astronomical Satellite imaging. The recommended dust map values for the LMC, SMC, and M31 may also not be correctly returned by some software packages. Accurate reddening estimates are important for measuring stellar and supernova luminosities in these nearby galaxies.

Vector Fuzzy c-Spherical Shells (VFCSS) over Non-Crisp Numbers for Satellite Imaging

The conventional fuzzy c-spherical shells (FCSS) clustering model is extended to cluster shells involving non-crisp numbers, in this paper. This is achieved by a vectorized representation of distance, between two non-crisp numbers like the crisp numbers case. Using the proposed clustering method, named vector fuzzy c-spherical shells (VFCSS), all crisp and non-crisp numbers can be clustered by the FCSS algorithm in a unique structure. Therefore, we can implement FCSS clustering over various types of numbers in a unique structure with only a few alterations in the details used in implementing each case. The relations of VFCSS applied to crisp and non-crisp (containing symbolic-interval, LR-type, TFN-type and TAN-type fuzzy) numbers are presented in this paper. Finally, simulation results are reported for VFCSS applied to synthetic LR-type fuzzy numbers; where the application of the proposed method in real life and in geomorphology science is illustrated by extracting the radii of circular agricultural fields using remotely sensed images and the results show better performance and lower cost computational complexity of the proposed method in comparison to conventional FCSS.

Remote Sensing in Oil Spill Handling in Offshore North West Java

During the blowout event, it is critical to track the oil spill to minimize environmental damage and optimize restoration cost. In this paper, we deliver our success story in handling oil spill from recent experiences. We utilize remote sensing technologies to establish our analysis and plan the remediation strategies. We also comprehensively discuss the techniques to analyze big data from the satellites, to utilize the downloaded data for forecasting, and to align the satellite information with restoration strategies. PHE relies on its principle to maintain minimum damage and ensures safety by dividing the steps into several aspects of monitoring, response (offshore and onshore), shoreline management and waste management. PHE utilizes latest development in survey by using satellite imaging, survey boat, chopper and UAV drone. Spill containment is done using several layers of oil boom to recover oil spill, complemented with skimmers and storage tanks. PHE encourages shoreline remediation using nets and manual recovery for capturing oil sludge. Using this combination of technologies, PHE is able to model and anticipate oil spill movement from the source up until the farthest shoreline. This enables real time monitoring and handling, therefore minimum environmental damage is ensured. PHE also employs prudent engineering design based on real time field condition in order to ensure the equipment are highly suited for the condition, as well as ensuring good supply chain of the material availability. This publication addresses the first offshore blowout mitigation and handling in Indonesia that uses novel technologies such as static oil boom, satellite imaging and integrated effort in handling shoreline damage. It is hoped that the experience can be replicated for other offshore operating contractors in Indonesia in designing blowout remediation.