Automatic Selection of Relevant Attributes for Multi-Sensor Remote Sensing Analysis: A Case Study on Sea Ice Classification

It is of considerable benefit to combine information obtained from different satellite sensors to achieve advanced and improved characterization of sea ice conditions. However, it is also true that not all the information is relevant. It may be redundant, corrupted, or unnecessary for the given task, hence decreasing the performance of the algorithms. Therefore, it is crucial to select an optimal set of image attributes which provides the relevant information content to enhance the efficiency and accuracy of the image interpretation and retrieval of geophysical parameters. Comprehensive studies have been focused on the analysis of relevant features for sea ice analysis obtained from different sensors, especially synthetic aperture radar. However, the outcomes of these studies are mostly data and application-dependent and can, therefore, rarely be generalized. In this article, we employ a feature selection method based on graph Laplacians, which is fully automatic and easy to implement. The proposed approach assesses relevant information on a global and local level using two metrics and selects relevant features for different regions of an image according to their physical characteristics and observation conditions. In the recent study, we investigate the effectiveness of this approach for sea ice classification, using different multi-sensor data combinations. Experiments show the advantage of applying multi-sensor data sets and demonstrate that the attributes selected by our method result in high classification accuracies. We demonstrate that our approach automatically considers varying technical, sensor-specific, environmental, and sea ice conditions by employing flexible and adaptive feature selection method as a pre-processing step

Soil permittivity estimation over croplands using full and compact polarimetric SAR data

Soil permittivity estimation using Polarimetric Synthetic Aperture Radar (PolSAR) data has been an extensively researched area. Nonetheless, it provides ample scope for further improvements. The vegetation cover over the soil surface leads to a complex interaction of the incident polarized wave with the canopy and subsequently with the underlying soil surface. This paper introduces a novel methodology to estimate soil permittivity over croplands with vegetation cover using the full and compact polarimetric modes. The proposed method utilizes the full and compact polarimetric scattering-type parameters, θ FP and θ CP , respectively. These scattering type parameters are a function of the soil permittivity and the Barakat degree of polarization. The method considers the X-Bragg scattering model for the soil surface. In particular, these scattering-type parameters explicitly account for the depolarizing structure of the scattered wave while characterizing targets. Thus, the depolarization information in terms of surface roughness in the X-Bragg model gets inherent importance while using θ FP and θ CP , unlike existing scattering-type parameters. Therefore, the proposed technique enhances the expected value of the inversion accuracies. This study validated the major phenology stages of four crops using the UAVSAR full-pol and simulated compact pol SAR data and the ground truth data collected during the SMAPVEX12 campaign over Manitoba, Canada. The proposed method estimated permittivity with an RMSE of 2.2 to 4.69 for FP and 3.28 to 5.45 for CP SAR data along with a Pearson coefficient, r ≥ 0.62.Peer ReviewedPostprint (author's final draft

Advanced of Mathematics-Statistics Methods to Radar Calibration for Rainfall Estimation; A Review

Ground-based radar is known as one of the most important systems for precipitation measurement at high spatial and temporal resolutions. Radar data are recorded in digital manner and readily ingested to any statistical analyses. These measurements are subjected to specific calibration to eliminate systematic errors as well as minimizing the random errors, respectively. Since statistical methods are based on mathematics, they offer more precise results and easy interpretation with lower data detail. Although they have challenge to interpret due to their mathematical structure, but the accuracy of the conclusions and the interpretation of the output are appropriate. This article reviews the advanced methods in using the calibration of ground-based radar for forecasting meteorological events include two aspects: statistical techniques and data mining. Statistical techniques refer to empirical analyses such as regression, while data mining includes the Artificial Neural Network (ANN), data Kriging, Nearest Neighbour (NN), Decision Tree (DT) and fuzzy logic. The results show that Kriging is more applicable for interpolation. Regression methods are simple to use and data mining based on Artificial Intelligence is very precise. Thus, this review explores the characteristics of the statistical parameters in the field of radar applications and shows which parameters give the best results for undefined cases. DOI: 10.17762/ijritcc2321-8169.15012

Segmentation of RADARSAT-2 Dual-Polarization Sea Ice Imagery

The mapping of sea ice is an important task for understanding global climate and for safe shipping. Currently, sea ice maps are created by human analysts with the help of remote sensing imagery, including synthetic aperture radar (SAR) imagery. While the maps are generally correct, they can be somewhat subjective and do not have pixel-level resolution due to the time consuming nature of manual segmentation. Therefore, automated sea ice mapping algorithms such as the multivariate iterative region growing with semantics (MIRGS) sea ice image segmentation algorithm are needed. MIRGS was designed to work with one-channel single-polarization SAR imagery from the RADARSAT-1 satellite. The launch of RADARSAT-2 has made available two-channel dual-polarization SAR imagery for the purposes of sea ice mapping. Dual-polarization imagery provides more information for distinguishing ice types, and one of the channels is less sensitive to changes in the backscatter caused by the SAR incidence angle parameter. In the past, this change in backscatter due to the incidence angle was a key limitation that prevented automatic segmentation of full SAR scenes. This thesis investigates techniques to make use of the dual-polarization data in MIRGS. An evaluation of MIRGS with RADARSAT-2 data was performed and showed that some detail was lost and that the incidence angle caused errors in segmentation. Several data fusion schemes were investigated to determine if they can improve performance. Gradient generation methods designed to take advantage of dual-polarization data, feature space fusion using linear and non-linear transforms as well as image fusion methods based on wavelet combination rules were implemented and tested. Tuning of the MIRGS parameters was performed to find the best set of parameters for segmentation of dual-polarization data. Results show that the standard MIRGS algorithm with default parameters provides the highest accuracy, so no changes are necessary for dual-polarization data. A hierarchical segmentation scheme that segments the dual-polarization channels separately was implemented to overcome the incidence angle errors. The technique is effective but requires more user input than the standard MIRGS algorithm

Method for landslides detection with semi-automatic procedures: The case in the zone center-east of Cauca department, Colombia

Landslides are a common natural hazard that causes human casualties, but also infrastructure damage and land-use degradation. Therefore, a quantitative assessment of their presence is required by means of detecting and recognizing the potentially unstable areas. This research aims to develop a method supported on semiautomatic methods to detect potential mass movements at a regional scale. Five techniques were studied: Morphometry, SAR interferometry (InSAR), Persistent Scatterer InSAR (PS-InSAR), SAR polarimetry (PolSAR) and NDVI composites of Landsat 5, Landsat 7, and Landsat 8. The case study was chosen within the mid-eastern area of the Cauca state, which is characterised by its mountainous terrain and the presence of slope instabilities, officially registered in the CGS-SIMMA landslide inventory. This inventory revealed that the type `slide' occurred with 77.4% from the entire registries, `fall' with 16.5%, followed by `creeps' with 3%, flows with 2.6%, and `lateral spread' with 0.43%. As a result, we obtained the morphometric variables: slope, CONVI, TWI, landform, which were highly associated with landslides. The effect of a DEM in the processing flow of the InSAR method was similar for the InSAR coherence variable using the DEMs ASTER, PALSAR RTC, Topo-map, and SRTM. Then, a multiInSAR analysis gave displacement velocities in the LOS direction between -10 and 10 mm/year. With the dual-PolSAR analysis (Sentinel-1), VH and VV C-band polarised radar energy emitted median values of backscatters, for landslides, about of -14.5 dB for VH polarisation and -8.5 dB for VV polarisation. Also, L-band fully polarimetric NASA-UAVSAR data allowed to nd the mechanism of dispersion of CGS landslide inventory: 39% for surface scattering, 46.4% for volume dispersion, and 14.6% for double-bounce scattering. The optical remote sensing provided NDVI composites derived from Landsat series between 2012 and 2016, showing that NDVI values between 0.40 and 0.70 had a high correlation to landslides. In summary, we found the highest categories related to landslides by Weight of Evidence method (WofE) for each spaceborne technique applied. Finally, these results were merged to generate the landslide detection model by using the supervised machine learning method of Random Forest. By taking training and test samples, the precision of the detection model was of about 70% for the rotational and translational types.Los deslizamientos son una amenaza natural que causa pérdidas humanas, daños a la infraestructura y degradación del suelo. Una evaluación cuantitativa de su presencia se requiere mediante la detección y el reconocimiento de potenciales áreas inestables. Esta investigación tuvo como alcance desarrollar un método soportado en métodos semi-automáticos para detectar potenciales movimientos en masa a escala regional. Cinco técnicas fueron estudiadas: Morfometría, Interferometría radar, Interferometría con Persistent Scatterers, Polarimetría radar y composiciones del NDVI con los satélites Landsat 5, Landsat 7 y Landsat 8. El caso de estudio se seleccionó dentro de la región intermedia al este del departamento del Cauca, la cual se caracteriza por terreno montañoso y la presencia de inestabilidades de la pendiente oficialmente registrados en el servicio SIMMA del Servicio Geológico Colombiano. Este inventario reveló que el tipo de movimiento deslizamiento ocurrió con una frecuencia relativa de 77.4%, caidos con el 16.5% de los casos y reptaciones con 3%, flujos con 2.6% y propagación lateral con 0.43%. Como resultado, se obtuvo las variables morfométricas: pendiente, convergencia, índice topográfico de humedad y forma del terreno altamente asociados con los deslizamientos. El efecto de un DEM en el procesamiento del método InSAR fue similar para la variable coherencia usando los DEMs: ASTER, PAlSAR RTC, Topo-map y SRTM. Un análisis Multi-InSAR estimó velocidades de desplazamiento en dirección de vista del radar entre -10 y 10 mm/año. El análisis de polarimetría dual del Sentinel-1 arrojó valores de retrodispersión promedio de -14.5 dB en la banda VH y -8.5dB en la banda VV. Las cuatro polarimetrías del sensor aéreo UAVSAR permitió caracterizar el mecanismo de dispersión del Inventario de Deslizamiento así: 39% en el mecanismo de superficie, 46.4% en el mecanismo de volumen y 14.6% en el mecanismo de doble rebote. La información generada en el rango óptico permitió obtener composiciones de NDVI derivados de la plataforma Landsat entre los años 2012 y 2016, mostrando que el rango entre 0.4 y 0.7 tuvieron una alta asociación con los deslizamientos. En esta investigación se determinaron las categorías de las variables de Teledetección más altamente relacionadas con los movimientos en masa mediante el método de Pesos de Evidencias (WofE). Finalmente, estos resultados se fusionaron para generar el modelo de detección de deslizamientos usando el método supervisado de aprendizaje de máquina Random Forest. Tomando muestras aleatorias para entrenar y validar el modelo en una proporción 70:30, el modelo de detección, especialmente los movimientos de tipo rotacional y traslacional fueron clasificados con una tasa general de éxito del 70%.Ministerio de CienciasConvocatoria 647 de 2014Research line: Geotechnics and Geoenvironmental HazardDoctorad

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

Polarimetric Synthetic Aperture Radar

This open access book focuses on the practical application of electromagnetic polarimetry principles in Earth remote sensing with an educational purpose. In the last decade, the operations from fully polarimetric synthetic aperture radar such as the Japanese ALOS/PalSAR, the Canadian Radarsat-2 and the German TerraSAR-X and their easy data access for scientific use have developed further the research and data applications at L,C and X band. As a consequence, the wider distribution of polarimetric data sets across the remote sensing community boosted activity and development in polarimetric SAR applications, also in view of future missions. Numerous experiments with real data from spaceborne platforms are shown, with the aim of giving an up-to-date and complete treatment of the unique benefits of fully polarimetric synthetic aperture radar data in five different domains: forest, agriculture, cryosphere, urban and oceans

Spectral-spatial classification of hyperspectral images: three tricks and a new supervised learning setting

Spectral-spatial classification of hyperspectral images has been the subject of many studies in recent years. In the presence of only very few labeled pixels, this task becomes challenging. In this paper we address the following two research questions: 1) Can a simple neural network with just a single hidden layer achieve state of the art performance in the presence of few labeled pixels? 2) How is the performance of hyperspectral image classification methods affected when using disjoint train and test sets? We give a positive answer to the first question by using three tricks within a very basic shallow Convolutional Neural Network (CNN) architecture: a tailored loss function, and smooth- and label-based data augmentation. The tailored loss function enforces that neighborhood wavelengths have similar contributions to the features generated during training. A new label-based technique here proposed favors selection of pixels in smaller classes, which is beneficial in the presence of very few labeled pixels and skewed class distributions. To address the second question, we introduce a new sampling procedure to generate disjoint train and test set. Then the train set is used to obtain the CNN model, which is then applied to pixels in the test set to estimate their labels. We assess the efficacy of the simple neural network method on five publicly available hyperspectral images. On these images our method significantly outperforms considered baselines. Notably, with just 1% of labeled pixels per class, on these datasets our method achieves an accuracy that goes from 86.42% (challenging dataset) to 99.52% (easy dataset). Furthermore we show that the simple neural network method improves over other baselines in the new challenging supervised setting. Our analysis substantiates the highly beneficial effect of using the entire image (so train and test data) for constructing a model.Comment: Remote Sensing 201

A Range of Earth Observation Techniques for Assessing Plant Diversity

AbstractVegetation diversity and health is multidimensional and only partially understood due to its complexity. So far there is no single monitoring approach that can sufficiently assess and predict vegetation health and resilience. To gain a better understanding of the different remote sensing (RS) approaches that are available, this chapter reviews the range of Earth observation (EO) platforms, sensors, and techniques for assessing vegetation diversity. Platforms include close-range EO platforms, spectral laboratories, plant phenomics facilities, ecotrons, wireless sensor networks (WSNs), towers, air- and spaceborne EO platforms, and unmanned aerial systems (UAS). Sensors include spectrometers, optical imaging systems, Light Detection and Ranging (LiDAR), and radar. Applications and approaches to vegetation diversity modeling and mapping with air- and spaceborne EO data are also presented. The chapter concludes with recommendations for the future direction of monitoring vegetation diversity using RS