Natural Terrain Features Research Articles

Obstacle Detection using Lidar 360° for Military Use

This paper presents a novel approach to obstacle detection using LiDAR360° for military use. The proposed system utilizes a 360° LiDAR sensor to detect obstacles in the environment and employs a real-time algorithm to identify and track potential threats. The system is designed to operate in challenging environments, including low-light and low-visibility conditions, and is capable of detecting a wide range of obstacles, including vehicles, buildings, and natural terrain features. The system's performance is evaluated using both simulated and real-world data, and the results demonstrate its effectiveness in detecting obstacles in a variety of scenarios. The proposed system has the potential to significantly improve situational awareness and enhance the safety and effectiveness of military operations in complex environments.

Autonomous Navigation Performance Using Natural Feature Tracking during the OSIRIS-REx Touch-and-Go Sample Collection Event

When the Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) spacecraft collected a sample of surface material from asteroid Bennu in 2020 October, it was the first time that an autonomous optical navigation system relying on natural terrain features had been used to guide a spacecraft to a planetary surface. This system, called Natural Feature Tracking (NFT), works by rendering features from digital terrain models and then correlating them with the terrain in real-time navigation images to estimate the spacecraft's position and velocity with respect to the asteroid. Here we describe how the OSIRIS-REx mission built the catalog of features for NFT and how those features performed during rehearsals for and execution of the Touch-and-Go (TAG) sample collection event. Feature performance (quality and accuracy of match) in the rendering and correlation process is the basis of the NFT measurement. All features scored well above the minimum correlation threshold thanks to the effort invested in selecting and modeling them. Residuals across the TAG trajectory were small, indicating that features in the catalog were defined consistently relative to each other. NFT delivered the spacecraft to within 1 m of the targeted location, with a difference of only 3.5 cm and 1.4 s from the predicted location and time of touch. This exceptional performance was crucial for spacecraft safety given Bennu's rough and hazardous terrain.

The Impact of Climate Changes on Slope Stability and Landslide Conditioning Factors: An Example from Kravarsko, Croatia

The Gajevo landslide in the Kravarsko area (Vukomeričke Gorice hilly area, northern Croatia) was chosen for investigation due to the existing landslide risk for the households at the landslide crown. Available data are limited, but a new landslide map and cross-section was developed within the presented research, mostly based on detailed light detection and ranging (LiDAR) data and field mapping. By comparing available orthophotos of the landslide, resident testimonies, precipitation data, and media releases, it was concluded that the landslide was activated in February 2014. The landslide was primarily triggered by increased precipitation (its measured variations could be in direct connection with ongoing global climate changes), but natural terrain features and anthropogenic factors also affected slope stability. New findings have led to the conclusion that the existing landslide area is large and complex and the crown and head scarp area should be stabilized by urgent remediation measures.

New framework for natural-artificial transport paths and hydrological connectivity analysis in an agriculture-intensive catchment

The impacts of human activities on hydrological connectivity disturb the network topology of transport paths, which has gradually evolved from natural terrain features to dual natural-artificial features. In this study, a new framework is proposed to extract information from natural-artificial transport paths and related hydrological connectivity dominated by agricultural practices and ditch networks. Graph theory and connectivity indexes are integrated for the comprehensive classification and the parallel processing of potential flow transport networks and their upstream drainage areas. Based on high-resolution remote sensing data and detailed field investigations, this new framework, which combines graph theory and connectivity indexes, is applied to a typical agriculture-intensive catchment in China. The results show that artificial factors greatly influence the transport paths and the related drainage areas. With the development of ditch construction, the hydrological transport paths become shorter and more fragmented. In addition, key ditch segments are identified by connectivity indexes, and recommendations are given for future planning. This new framework offers an approach for the hydrological connectivity analysis of complex networks and provides effective strategies for agricultural development.

A prologue to natural computing in remote sensing

Nature was always one of the sources of motivation for artist, painters, musician’s, scientist and many more. New growth in science and technology has given several optimization approaches that inspire the creation of greater efficient algorithms for Digital Satellite Image Classification. Different conventional classification techniques were used for satellite image classification however the outcomes got from those calculations were very little proficient to use for any genuine application. Different forms of remote sensing images and varying classification techniques are examined in this paper. Natural Computing Intelligence explores the area based on Physics, Chemistry, Geo-Sciences, Human mind and some other intelligent techniques. From these intelligent computational techniques, this paper includes some of the nature-inspired algorithms for satellite image classification of the Alwar region, Rajasthan, India. The fundamental objective of the paper is to retrieve various natural terrain features with a group of classifiers or a hybrid model for the accomplishment of better accuracy. Various parameters like Kappa Coefficient, User Accuracy, Producer Accuracy, and Overall Accuracy are used to decide the effectiveness of classification.

The Hybridization of Neural Network and Particle Swarm Optimization for Natural Terrain Feature Extraction

The optimization of various soft computing and metaheuristic techniques can be ameliorated in a global area network, Swarm intelligence. In this research, a hybrid algorithm of neural network and particle swarm optimization has been presented for remote sensing applications. The terrain features of the land in a remote sensing image have been classified using these algorithms. Remote sensing basically deals with the processing and interpretation of satellite images without any physical contact to that particular region. In addition, the geo-spatial characteristics of the data also recorded during image classification. The hybrid concept used in this research, the implementation of algorithm in this paper based on the neurons network to find the best solution, which is further resolved using the Particle Swarm Optimization approach, an optimization technique. The proposed algorithm easily classifies the terrain features with higher efficiency and kappa coefficient value. The results show that 94.36% accuracy attained from the proposed technique. The overall accuracy improved by 5.24 % and 14.93% and kappa coefficient enhancement of 6.97 % and 18.99 % in comparison to existing studies.

Ancient lowland Maya complexity as revealed by airborne laser scanning of northern Guatemala.

Lowland Maya civilization flourished in the tropical region of the Yucatan peninsula and environs for more than 2500 years (~1000 BCE to 1500 CE). Known for its sophistication in writing, art, architecture, astronomy, and mathematics, Maya civilization still poses questions about the nature of its cities and surrounding populations because of its location in an inaccessible forest. In 2016, an aerial lidar survey across 2144 square kilometers of northern Guatemala mapped natural terrain and archaeological features over several distinct areas. We present results from these data, revealing interconnected urban settlement and landscapes with extensive infrastructural development. Studied through a joint international effort of interdisciplinary teams sharing protocols, this lidar survey compels a reevaluation of Maya demography, agriculture, and political economy and suggests future avenues of field research.

EARTH OBSERVATION AND SATELLITE IMAGERY USING SPIDER MONKEY OPTIMIZATION (SMO)

Remote Sensing Image classification is one of the major research areas due to its wide spectrum of applications including natural terrain feature classification, land use monitoring, ground water exploration, environmental disaster assessment and urban planning etc.Various Swarm Intelligence Techniques, nature inspired and some other intelligent techniques have been used in the field of remote sensing image classification. The various Artificial Intelligent techniques used in the area are Genetic Algorithm, Particle Swarm Optimization (PSO), Artificial Bee Colony optimization (ABC), Ant Colony Optimization (ACO), Cuckoo Search (CS) and also hybrid techniques of these for image classification& they also provide accuracy. An extensive research has been done in this field to classify various features of the image. Throughout feature extractions efforts can be made in direction of identifying all these features and classify the remote image areas consisting of, to get more efficiency than existing methods by means of spider monkey optimization a recent artificial intelligence technique. We used Artificial Intelligence Techniques in image classification of Natural Terrain features & using Spider Monkey Optimization (SMO) for Image Classification

Uncertainty quantification, turbulent scattering, and random sampling in outdoor sound propagation calculations

Although the fundamental physics of sound propagation outdoors (e.g., absorption, ground interactions, refraction, and scattering) are well understood, modeling capabilities are typically inadequate to capture the many intricate interactions with the atmosphere, ground, and natural and man-made terrain features. Furthermore, environmental data are rarely available at the sub-wavelength resolution needed to accurately predict the sound field in the audible range, in a deterministic sense. Fortunately, advances in computing capabilities and statistical sampling methods provide new opportunities for quantifying the inherent uncertainties of sound propagation predictions. Among the sampling techniques considered in this presentation are ordinary Monte Carlo sampling, Latin hypercube sampling, stratified sampling based on meteorological classes, importance sampling, and adaptive importance sampling. When uncertainties in the atmospheric and ground properties dominate, importance sampling is found to converge t...

A BBO based framework for natural terrain identification in remote sensing

Nature inspired intelligence is increasingly being used to solve complex problems. Identifying different types of terrains present in the satellite imagery of a given region is one such problem in the field of remote sensing. Prospects of its numerous applications in real life have been a motivating factor for scientists to develop newer terrain analyzers to perform this task with more precision. This paper presents a two phase biogeography based optimization (BBO) based generic frame work for identifying natural terrain features in a given region. BBO is a population-based algorithm and is based on the theory of island biogeography that explains the geographical distribution of biological organisms. Validation is performed on two remote sensing datasets for Alwar and Delhi regions in India. Better performance of proposed analyzers has been observed as compared to state of the art techniques.

Hybrid Algorithm of Cuckoo Search and Particle Swarm Optimization for Natural Terrain Feature Extraction

Hybrid Algorithm of Cuckoo Search and Particle Swarm Optimization for Natural Terrain Feature Extraction

Selecting locations for landing of various formations of helicopters using spatial modelling

During crisis situations such as floods, landslides, humanitarian crisis and even military clashes there are situations when it is necessary to send helicopters to the crisis areas. To facilitate the process of searching for the sites suitable for landing, it is possible to use the tools of spatial modelling. The paper describes a procedure of selecting areas potentially suitable for landing of particular formations of helicopters. It lists natural and man-made terrain features that represent the obstacles that can prevent helicopters from landing. It also states specific requirements of the NATO documents that have to be respected when selecting the areas for landing. These requirements relate to a slope of ground and an obstruction angle on approach and exit paths. Creating the knowledge base and graphical models in ERDAS IMAGINE is then described. In the first step of the procedure the areas generally suitable for landing are selected. Then the different configurations of landing points that form the landing sites are created and corresponding outputs are generated. Finally, several tactical requirements are incorporated.

A Global "Natural" Grid Modeling Method Based on Terrain Topological Structure

Abstract. In this paper, a Global "Natural" Grid (GNG) modeling method based on the terrain topological structure is proposed. GNG partitions the earth surface into "natural" cells by using the natural terrain feature elements, which may be more suitable for exploring and understanding some natural phenomena in the environment monitoring, hydrological analysis, species distribution, etc. This modeling method mainly consists of three steps: firstly, a variable-resolution geometric representation of global DEMs based on the restricted quadtree is constructed; secondly, the feature elements are extracted and an original GNG model is built; finally, a multi-resolution representation of GNG is established by recursively applying topological simplification. In the end, an experiment is done to validate the correctness and feasibility of this modeling method.

Performance Analysis of Biogeography Based Land Cover Feature Extractor for Building Hybrid Intelligent Models

This paper is an analytical study of the performance governing factors of the biogeography based land cover feature extraction technique which is characterized by its ability to perform differently on different natural terrain features contained in a satellite image. From the discussion, we establish the fact that the classification efficiency of BBO for a given land cover feature is inversely proportional to the degree of disorder and directly proportional to the similarity index of the digital number (DN) values of the pixels comprising that land cover feature when viewed in any of the bands of the multi-spectral satellite image. In order to verify our proposed hypotheses, we calculated the entropies and similarity indices for each of the land cover feature in two bands on two different datasets and found the same results in both the bands for each of the datasets we took, thus validating the theory. This finding is of prime importance since a prior analysis of the performance of biogeography based feature extraction technique on different types of terrain under consideration will improve the analyst's decisive capabilities for the selection of the most appropriate technique for the feature extraction task in hand. This in turn can be applied for building efficient artificially intelligent hybrid classifiers by applying the BBO technique on the extraction of those features on which it shows maximum classification efficiency as demonstrated in the paper.

Fusion of Biogeography based optimization and Artificial bee colony for identification of Natural Terrain Features

Swarm Intelligence techniques expedite the configuration and collimation of the remarkable ability of group members to reason and learn in an environment of contingency and corrigendum from their peers by sharing information. This paper introduces a novel approach of fusion of two intelligent techniques generally to augment the performance of a single intelligent technique by means of information sharing. Biogeography-based optimization (BBO) is a recently developed heuristic algorithm, which proves to be a strong entrant in swarm intelligence with the encouraging and consistent performance. But, as BBO lacks inbuilt property of clustering, its behavior can be replaced with the honey bees of artificial bee colony (ABC), a new swarm intelligent technique. These two methods can be combined to create a new method which is easy to implement and gives more optimized results than the results when BBO is used. We have successfully applied this fusion of techniques for classifying diversified land cover areas in a multispectral remote sensing satellite image. The results illustrate that the proposed approach is very efficient than BBO and highly accurate land cover features can be extracted by using this approach.

Use of geometry for spatial reorientation in children applies only to symmetric spaces

Proponents of the geometric module hypothesis argue that following disorientation, many species reorient by use of macro-environment geometry. It is suggested that attention to the surface layout geometry of natural terrain features may have been selected for over evolutionary time due to the enduring and unambiguous location information it provides. Paradoxically, however, tests of the hypothesis have been exclusively conducted in symmetric (hence 'unnatural' and geometrically ambiguous) environments. The present series of studies examines reorientation by 18-month-3-year-old children in a rectangular versus irregular quadrilateral enclosure (Study 1), a rectangular versus irregular quadrilateral array (Study 2) and an isosceles versus irregular triangular array (Study 3). Children were successful in symmetric but not asymmetric environments, casting doubt on the functional argument for an empirical basis of the geometric module hypothesis.

A Hybrid Conditional Random Field for Estimating the Underlying Ground Surface From Airborne LiDAR Data

Recent advances in airborne light detection and ranging (LiDAR) technology allow rapid and inexpensive generation of digital surface models (DSMs), 3-D point clouds of buildings, vegetations, cars, and natural terrain features over large regions. However, in many applications, such as flood modeling and landslide prediction, digital terrain models (DTMs), the topography of the bare-Earth surface, are needed. This paper introduces a novel machine learning approach to automatically extract DTMs from their corresponding DSMs. We first classify each point as being either ground or nonground, using supervised learning techniques applied to a variety of features. For the points which are classified as ground, we use the LiDAR measurements as an estimate of the surface height, but, for the nonground points, we have to interpolate between nearby values, which we do using a Gaussian random field. Since our model contains both discrete and continuous latent variables, and is a discriminative (rather than generative) probabilistic model, we call it a hybrid conditional random field. We show that a Maximum a Posteriori estimate of the surface height can be efficiently estimated by using a variant of the Expectation Maximization algorithm. Experiments demonstrate that the accuracy of this learning-based approach outperforms the previous best systems, based on manually tuned heuristics.

Possible uses of submarine canyons in acoustic thermometry

High-power sources and high-gain receivers are used in long-range ocean acoustic thermometry. Because of power supply and data telemetry the sources and receivers are often placed near coastal facilities. There may be some significant benefits associated with taking advantage of natural terrain features to protect the sources and receivers from fisheries, and to optimize certain aspects of their performance. Sources and receivers are generally moored in deep water, in the axis of the sound channel, on the order of 100 to 1000 m. The shortest distance from shore to deep water is frequently associated with submarine canyons. For example it is less than 15 km from Point Pinos to water depths in excess of 2000 m in Monterey Submarine Canyon, less than half the distance along continental borders without submarine canyons. Fishermen do not fish inside the canyon walls for fear of rapid depth changes. Therefore, the canyons provide a natural barrier to the threat of fishing activity to moored sources and receivers. It is obvious that the canyon restricts the ray paths between a source or receiver in the canyon. However, if the source and receiver geometry is carefully selected this may not be a significant limitation. Oceanic noise arrivals in a submarine canyon can be restricted to those in the direction of deep water. If for example the canyon opens at an angle of 30 deg from the receiver and if the noise is omnidirectional at the axis of the array, the terrain barrier restricts noise to that 30-deg window. This would provide an optimum noise reduction of 7.8 dB. On the source side of the problem, placing the source in the canyon will limit the high-intensity blast to the energy going out the canyon mouth into deep water. In this way, marine mammals in the area of the source would be shielded from the direct blast unless they happen to be in a restricted direction from the source, or very near the source. If the source could be made so that it was directional, the volume of the direct blast would be even more restricted. Certainly, there are some possible downsides to placing sources and receivers in submarine canyons. These will be explored in the presentation.

The Design of an Automated, High-Accuracy Antenna Test Facility

This paper presents the step-by-step application of proven far-field range and instrumentation design techniques to a specific antenna measurement problem, describes the resulting facility design, and presents the predicted measurement uncertainty. Fundamental electromagnetic design criteria for an outdoor, far-field facility establish minimum dimensional requirements for the range design and limiting values of source-antenna directivity. Electromagnetic compatibility of the facility is assured by frequency coordination with existing and planned services in the area surrounding the available site. Additional design constraints for this facility included restricted measurement time, reduction of spurious test enclosure effects, limited available terrain, and required data quality. In this case, the required range length is in excess of 6500 ft, and paraboloidal source antenna diameters up to 23 ft are required. The frequency coordination problem was solved by exploiting the natural terrain features and configuring the measurement system as "test-on-transmit." Signal and reference paths share the same range cable. The quantity of data that must be handled in the available measurement interval required the use of a computer-based measurement system.