Final Probability Map Research Articles

Application of a temporal decorrelation model using Sentinel-1 SAR data to Detect volcanic ash deposits related to the 2020 Taal volcano eruption

Volcanic ash deposits affect buildings, vegetation, and population. After a volcanic eruption, it is critical to detect the areas affected by ash deposits to achieve an advisable management of the emergency. Optical sensors have been widely used to carry out this task, but they are limited by solar illumination and weather conditions. As an alternative, Synthetic Aperture Radar (SAR) data are not affected by those limitations. Recently, a Temporal Decorrelation Model (TDM) that uses SAR data was proposed for detecting and mapping ash deposits, but it has only been applied to L-band data. Today there is available a huge quantity of C-band data acquired by the Sentinel-1 constellation. In this study we applied the TDM to Sentinel-1 data in order to assess its performance for detecting volcanic ash deposits after an eruption. We selected the eruption of Taal volcano in The Philippines on January 12, 2020, as our case study. We computed more than 4000 interferometric pairs from a dataset of 93 images acquired before, during, and after the eruption. Our results show that TDM can be applied to C-band data, despite the higher temporal decorrelation suffered by them. Our final probability map is consistent with the field evidence reported by the Philippines Institute of Volcanology and Seismology (PHILVOLCS) and the isopachs map reported in the literature. This new application provides a novel framework for the coherence exploitation of C-Band data. Also, this approach could be applied to detection and monitoring of other natural disasters.

The influence of geological and technological factors on the probability of determining zones of residual oil reserves

This article is devoted to the probability maps have been constructed for predicting the zones of residual oil reserves using the example of deposits in Shaim region. The refinement of the previously presented algorithm [4] has been made, which helps with a fairly high degree of probability to quickly localize the residual oil reserves based on 2D modeling. In the process of work, the influence of many geological and technological parameters on the final probability map was established, the influence of the observation zone on the value of the correlation coefficient of the map of residual mobile oil reserves with the map of current mobile oil reserves based on geological and hydrodynamic modeling was established.

A Co-Point Mapping-Based Approach to Drivable Area Detection for Self-Driving Cars

The randomness and complexity of urban traffic scenes make it a difficult task for self-driving cars to detect drivable areas. Inspired by human driving behaviors, we propose a novel method of drivable area detection for self-driving cars based on fusing pixel information from a monocular camera with spatial information from a light detection and ranging (LIDAR) scanner. Similar to the bijection of collineation, a new concept called co-point mapping, which is a bijection that maps points from the LIDAR scanner to points on the edge of the image segmentation, is introduced in the proposed method. Our method positions candidate drivable areas through self-learning models based on the initial drivable areas that are obtained by fusing obstacle information with superpixels. In addition, a fusion of four features is applied in order to achieve a more robust performance. In particular, a feature called drivable degree (DD) is proposed to characterize the drivable degree of the LIDAR points. After the initial drivable area is characterized by the features obtained through self-learning, a Bayesian framework is utilized to calculate the final probability map of the drivable area. Our approach introduces no common hypothesis and requires no training steps; yet it yields a state-of-art performance when tested on the ROAD-KITTI benchmark. Experimental results demonstrate that the proposed method is a general and efficient approach for detecting drivable area.

Evaluating susceptibility of karst dolines (sinkholes) for collapse in Sango, Tennessee, USA.

Dolines or sinkholes are earth depressions that develop in soluble rocks complexes such as limestone, dolomite, gypsum, anhydrite, and halite; dolines appear in a variety of shapes from nearly circular to complex structures with highly curved perimeters. The occurrence of dolines in the studied karst area is not random; they are the results of geomorphic, hydrologic, and chemical processes that have caused partial subsidence, even the total collapse of the land surface when voids and caves are present in the bedrock and the regolith arch overbridging these voids is unstable. In the study area, the majority of collapses occur in the regolith (bedrock cover) that bridges voids in the bedrock. Because these collapsing dolines may result in property damage and even cause the loss of lives, there is a need to develop methods for evaluating karst hazards. These methods can then be used by planners and practitioners for urban and economic development, especially in regions with a growing population. The purpose of this project is threefold: 1) to develop a karst feature database, 2) to investigate critical indicators associated with doline collapse, and 3) to develop a doline susceptibility model for potential doline collapse based on external morphometric data. The study has revealed the presence of short range spatial dependence in the distribution of the dolines' morphometric parameters such as circularity, the geographic orientation of the main doline axes, and the length-to-width doline ratios; therefore, geostatistics can be used to spatially evaluate the susceptibility of the karst area for doline collapse. The partial susceptibility estimates were combined into a final probability map enabling the identification of areas where, until now, undetected dolines may cause significant hazards.

Video object pursuit by tri-tracker with on-line learning from positive and negative candidates

Based on chain code, an improved Hough detection method for head detection is proposed, with which moving regions of objects are determined. During tracking process, we present a tri-tracking method (tri-tracker), on-line trained by positive and negative candidates, for tracking objects. The tracker trains three support vector machines (SVMs) initialised with a small number of labelled frames and updates the classifiers in a collaborative fashion, in which, an object is represented using a local binary pattern (LBP) histogram, RGB colour histogram and pixel-pattern-based texture feature (PPBTF) histogram, respectively. Based on the probability map created by each classifier, the final probability map forms by combing three individual probability maps. And then the peak of final probability map, which we consider as the object's position, is found by mean shift. Experiments on several video sequences show the robustness and accuracy of our proposed method.