Exploitation of infrared polarimetric imagery for passive remote sensing applications

Polarimetric infrared imagery has emerged over the past few decades as a candidate technology to detect manmade objects by taking advantage of the fact that smooth materials emit strong polarized electromagnetic waves, which can be remotely sensed by a specialized camera using a rotating polarizer in front of the focal plate array in order to generate the so-called Stokes parameters: S0, S1, S2, and DoLP. Current research in this area has shown the ability of using such variations of these parameters to detect smooth manmade structures in low contrast contrast scenarios. This dissertation proposes and evaluates novel anomaly detection methods for long-wave infrared polarimetric imagery exploitation suited for surveillance applications requiring automatic target detection capability. The targets considered are manmade structures in natural clutter backgrounds under unknown illumination and atmospheric effects. A method based on mathematical morphology is proposed with the intent to enhance the polarimetric Stokes features of manmade structures found in the scene while minimizing its effects on natural clutter. The method suggests that morphology-based algorithms are capable of enhancing the contrast between manmade objects and natural clutter backgrounds, thus, improving the probability of correct detection of manmade objects in the scene. The second method departs from common practices in the polarimetric research community (i.e., using the Stokes vector parameters as input to algorithms) by using instead the raw polarization component imagery (e.g., 0°, 45°, 90°, and 135°) and employing multivariate mathematical statistics to distinguish the two classes of objects. This dissertation unequivocally shows that algorithms based on this new direction significantly outperform the prior art (algorithms based on Stokes parameters and their variants). To support this claim, this dissertation offers an exhaustive data analysis and quantitative comparative study, among the various competing algorithms, using long-wave infrared polarimetric imagery collected outdoor, over several days, under varying weather conditions, geometry of illumination, and diurnal cycles

Advanced Geoscience Remote Sensing

Nowadays, advanced remote sensing technology plays tremendous roles to build a quantitative and comprehensive understanding of how the Earth system operates. The advanced remote sensing technology is also used widely to monitor and survey the natural disasters and man-made pollution. Besides, telecommunication is considered as precise advanced remote sensing technology tool. Indeed precise usages of remote sensing and telecommunication without a comprehensive understanding of mathematics and physics. This book has three parts (i) microwave remote sensing applications, (ii) nuclear, geophysics and telecommunication; and (iii) environment remote sensing investigations

Oil Spills and Slicks imaged by Synthetic Aperture Radar

Oil spills and slicks occur in the ocean around the world due to natural seeps, oil extraction, transportation, and consumption. Satellite synthetic aperture radar (SAR) has proven to be an efficient tool for identifying and classifying oil on the sea surface. This information can be used to monitor areas for potential illegal marine discharge or to respond to an oil spill incident. When used to monitor shipping lanes or drilling platforms, timely analysis can identify offending parties and lead to prosecution. Following an oil spill such as that from the Deepwater Horizon rig in the Gulf of Mexico in 2010, SAR can be used to direct response activities and optimize available resources

Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

An Analysis of multimodal sensor fusion for target detection in an urban environment

This work makes a compelling case for simulation as an attractive tool in designing cutting-edge remote sensing systems to generate the sheer volume of data required for a reasonable trade study. The generalized approach presented here allows multimodal system designers to tailor target and sensor parameters for their particular scenarios of interest via synthetic image generation tools, ensuring that resources are best allocated while sensors are still in the design phase. Additionally, sensor operators can use the customizable process showcased here to optimize image collection parameters for existing sensors. In the remote sensing community, polarimetric capabilities are often seen as a tool without a widely accepted mission. This study proposes incorporating a polarimetric and spectral sensor in a multimodal architecture to improve target detection performance in an urban environment. Two novel multimodal fusion algorithms are proposed--one for the pixel level, and another for the decision level. A synthetic urban scene is rendered for 355 unique combinations of illumination condition and sensor viewing geometry with the Digital Imaging and Remote Sensing Image Generation (DIRSIG) model, and then validated to ensure the presence of enough background clutter. The utility of polarimetric information is shown to vary with the sun-target-sensor geometry, and the decision fusion algorithm is shown to generally outperform the pixel fusion algorithm. The results essentially suggest that polarimetric information may be leveraged to restore the capabilities of a spectral sensor if forced to image under less than ideal circumstances

Surface soil moisture estimate from Sentinel-1 and Sentinel-2 data in agricultural fields in areas of high vulnerability to climate variations: the Marche region (Italy) case study

Surface soil moisture is a key hydrologic state variable that greatly influences the global environment and human society. Its significant decrease in the Mediterranean region, registered since the 1950s, and expected to continue in the next century, threatens soil health and crops. Microwave remote sensing techniques are becoming a key tool for the implementation of climate-smart agriculture, as a means for surface soil moisture retrieval that exploits the correlation between liquid water and the dielectric properties of soil. In this study, a workflow in Google Earth Engine was developed to estimate surface soil moisture in the agricultural fields of the Marche region (Italy) through Synthetic Aperture Radar data. Firstly, agricultural areas were extracted with both Sentinel-2 optical and Sentinel-1 radar satellites, investigating the use of Dual-Polarimetric Entropy-Alpha decomposition's bands to improve the accuracy of radar data classification. The results show that Entropy and Alpha bands improve the kappa index obtained from the radar data only by 4% (K = 0.818), exceeding optical accuracy in urban and water areas. However, they still did not allow to reach the overall optical accuracy (K = 0.927). The best classification results are reached with the total dataset (K = 0.949). Subsequently, Water Cloud and Tu Wien models were implemented on the crop areas using calibration parameters derived from literature, to test if an acceptable accuracy is reached without in situ observation. While the first model’s accuracy was inadequate (RMSD = 12.3), the extraction of surface soil moisture using Tu Wien change detection method was found to have acceptable accuracy (RMSD = 9.4)

Dark Spot Detection from SAR Intensity Imagery with Spatial Density Thresholding for Oil Spill Monitoring

Since the 1980s, satellite-borne synthetic aperture radar (SAR) has been investigated for early warning and monitoring of marine oil spills to permit effective satellite surveillance in the marine environment. Automated detection of oil spills from satellite SAR intensity imagery consists of three steps: 1) Detection of dark spots; 2) Extraction of features from the detected dark spots; and 3) Classification of the dark spots into oil spills and look-alikes. However, marine oil spill detection is a very difficult and challenging task. Open questions exist in each of the three stages. In this thesis, the focus is on the first stage—dark spot detection. An efficient and effective dark spot detection method is critical and fundamental for developing an automated oil spill detection system. A novel method for this task is presented. The key to the method is utilizing the spatial density feature to enhance the separability of dark spots and the background. After an adaptive intensity thresholding, a spatial density thresholding is further used to differentiate dark spots from the background. The proposed method was applied to a evaluation dataset with 60 RADARSAT-1 ScanSAR Narrow Beam intensity images containing oil spill anomalies. The experimental results obtained from the test dataset demonstrate that the proposed method for dark spot detection is fast, robust and effective. Recommendations are given for future research to be conducted to ensure that this procedure goes beyond the prototype stage and becomes a practical application

Remote Sensing of the Oceans

This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements