The Data Big Bang and the Expanding Digital Universe: High-Dimensional, Complex and Massive Data Sets in an Inflationary Epoch

Recent and forthcoming advances in instrumentation, and giant new surveys, are creating astronomical data sets that are not amenable to the methods of analysis familiar to astronomers. Traditional methods are often inadequate not merely because of the size in bytes of the data sets, but also because of the complexity of modern data sets. Mathematical limitations of familiar algorithms and techniques in dealing with such data sets create a critical need for new paradigms for the representation, analysis and scientific visualization (as opposed to illustrative visualization) of heterogeneous, multiresolution data across application domains. Some of the problems presented by the new data sets have been addressed by other disciplines such as applied mathematics, statistics and machine learning and have been utilized by other sciences such as space-based geosciences. Unfortunately, valuable results pertaining to these problems are mostly to be found only in publications outside of astronomy. Here we offer brief overviews of a number of concepts, techniques and developments, some "old" and some new. These are generally unknown to most of the astronomical community, but are vital to the analysis and visualization of complex datasets and images. In order for astronomers to take advantage of the richness and complexity of the new era of data, and to be able to identify, adopt, and apply new solutions, the astronomical community needs a certain degree of awareness and understanding of the new concepts. One of the goals of this paper is to help bridge the gap between applied mathematics, artificial intelligence and computer science on the one side and astronomy on the other.Comment: 24 pages, 8 Figures, 1 Table. Accepted for publication: "Advances in Astronomy, special issue "Robotic Astronomy

Automated Quantitative Description of Spiral Galaxy Arm-Segment Structure

We describe a system for the automatic quantification of structure in spiral galaxies. This enables translation of sky survey images into data needed to help address fundamental astrophysical questions such as the origin of spiral structure---a phenomenon that has eluded theoretical description despite 150 years of study (Sellwood 2010). The difficulty of automated measurement is underscored by the fact that, to date, only manual efforts (such as the citizen science project Galaxy Zoo) have been able to extract information about large samples of spiral galaxies. An automated approach will be needed to eliminate measurement subjectivity and handle the otherwise-overwhelming image quantities (up to billions of images) from near-future surveys. Our approach automatically describes spiral galaxy structure as a set of arcs, precisely describing spiral arm segment arrangement while retaining the flexibility needed to accommodate the observed wide variety of spiral galaxy structure. The largest existing quantitative measurements were manually-guided and encompassed fewer than 100 galaxies, while we have already applied our method to more than 29,000 galaxies. Our output matches previous information, both quantitatively over small existing samples, and qualitatively against human classifications from Galaxy Zoo.Comment: 9 pages;4 figures; 2 tables; accepted to CVPR (Computer Vision and Pattern Recognition), June 2012, Providence, Rhode Island, June 16-21, 201

Report of the Terrestrial Bodies Science Working Group. Volume 9: Complementary research and development

Topics discussed include the need for: the conception and development of a wide spectrum of experiments, instruments, and vehicles in order to derive the proper return from an exploration program; the effective use of alternative methods of data acquisition involving ground-based, airborne and near Earth orbital techniques to supplement spacraft mission; and continued reduction and analysis of existing data including laboratory and theoretical studies in order to benefit fully from experiments and to build on the past programs toward a logical and efficient exploration of the solar system

Wide{Field Sky Monitoring -Optical and X-rays

Abstract. We report on selected projects in wide-field sky imaging. This includes the recent efforts to digitize the astronomical sky plate archives and to apply these data for various scientific projects. We also address and discuss the status of the development of related algorithms and software programs. These data may easily provide very long term monitoring over very extended time intervals (up to more than 100 years) with limiting magnitudes between 12 and 23. The further experiments include CCD sky monitors, OMC camera onboard the ESA Integral satellite, robotic telescopes, and innovative wide-field X-ray telescopes

Multivariate Approaches to Classification in Extragalactic Astronomy

Clustering objects into synthetic groups is a natural activity of any science. Astrophysics is not an exception and is now facing a deluge of data. For galaxies, the one-century old Hubble classification and the Hubble tuning fork are still largely in use, together with numerous mono-or bivariate classifications most often made by eye. However, a classification must be driven by the data, and sophisticated multivariate statistical tools are used more and more often. In this paper we review these different approaches in order to situate them in the general context of unsupervised and supervised learning. We insist on the astrophysical outcomes of these studies to show that multivariate analyses provide an obvious path toward a renewal of our classification of galaxies and are invaluable tools to investigate the physics and evolution of galaxies.Comment: Open Access paper. http://www.frontiersin.org/milky\_way\_and\_galaxies/10.3389/fspas.2015.00003/abstract\>. \<10.3389/fspas.2015.00003 \&g

Liquid crystal hyperspectral imager

Hyperspectral imaging is the collection, processing and analysis of spectral data in numerous contiguous wavelength bands while also providing spatial context. Some of the commonly used instruments for hyperspectral imaging are pushbroom scanning imaging systems, grating based imaging spectrometers and more recently electronically tunable filters. Electronically tunable filters offer the advantages of compactness and absence of mechanically movable parts. Electronically tunable filters have the ability to rapidly switch between wavelengths and provide spatial and spectral information over a large wavelength range. They involve the use of materials whose response to light can be altered in the presence of an external stimulus. While these filters offer some unique advantages, they also present some equally unique challenges. This research work involves the design and development of a multichannel imaging system using electronically tunable Liquid Crystal Fabry-Perot etalons. This instrument is called the Liquid Crystal Hyperspectral Imager (LiCHI). LiCHI images four spectral regions simultaneously and presents a trade-off between spatial and spectral domains. This simultaneity of measurements in multiple wavelengths can be exploited for dynamic and ephemeral events. LiCHI was initially designed for multispectral imaging of space plasmas but its versatility was demonstrated by testing in the field for multiple applications including landscape analysis and anomaly detection. The results obtained after testing of this instrument and analysis of the images are promising and demonstrate LiCHI as a good candidate for hyperspectral imaging. The challenges posed by LiCHI for each of these applications have also been explored

Three-Dimensional Mapping of Habitats Using Remote-Sensing Data and Machine-Learning Algorithms

Progress toward habitat protection goals can effectively be performed using satellite imagery and machine-learning (ML) models at various spatial and temporal scales. In this regard, habitat types and landscape structures can be discriminated against using remote-sensing (RS) datasets. However, most existing research in three-dimensional (3D) habitat mapping primarily relies on same/cross-sensor features like features derived from multibeam Light Detection And Ranging (LiDAR), hydrographic LiDAR, and aerial images, often overlooking the potential benefits of considering multi-sensor data integration. To address this gap, this study introduced a novel approach to creating 3D habitat maps by using high-resolution multispectral images and a LiDAR-derived Digital Surface Model (DSM) coupled with an object-based Random Forest (RF) algorithm. LiDAR-derived products were also used to improve the accuracy of the habitat classification, especially for the habitat classes with similar spectral characteristics but different heights. Two study areas in the United Kingdom (UK) were chosen to explore the accuracy of the developed models. The overall accuracies for the two mentioned study areas were high (91% and 82%), which is indicative of the high potential of the developed RS method for 3D habitat mapping. Overall, it was observed that a combination of high-resolution multispectral imagery and LiDAR data could help the separation of different habitat types and provide reliable 3D information

무인비행체 탑재 열화상 및 실화상 이미지를 활용한 야생동물 탐지 가능성 연구

학위논문(석사) -- 서울대학교대학원 : 환경대학원 환경조경학과, 2022.2. 송영근.야생동물의 탐지와 모니터링을 위해, 현장 직접 관찰, 포획-재포획과 같은 전통적 조사 방법이 다양한 목적으로 수행되어왔다. 하지만, 이러한 방법들은 많은 시간과 상대적으로 비싼 비용이 필요하며, 신뢰 가능한 탐지 결과를 얻기 위해선 숙련된 현장 전문가가 필요하다. 게다가, 전통적인 현장 조사 방법은 현장에서 야생동물을 마주치는 등 위험한 상황에 처할 수 있다. 이에 따라, 카메라 트래핑, GPS 추적, eDNA 샘플링과 같은 원격 조사 방법이 기존의 전통적 조사방법을 대체하며 더욱 빈번히 사용되기 시작했다. 하지만, 이러한 방법들은 여전히 목표로 하는 대상의 전체 면적과, 개별 개체를 탐지할 수 없다는 한계를 가지고 있다. 이러한 한계를 극복하기 위해, 무인비행체 (UAV, Unmanned Aerial Vehicle)가 야생동물 탐지의 대중적인 도구로 자리매김하고 있다. UAV의 가장 큰 장점은, 선명하고 촘촘한 공간 및 시간해상도와 함께 전체 연구 지역에 대한 동물 탐지가 가능하다는 것이다. 이에 더해, UAV를 사용함으로써, 접근하기 어려운 지역이나 위험한 곳에 대한 조사가 가능해진다. 하지만, 이러한 이점 외에, UAV의 단점도 명확히 존재한다. 대상지, 비행 속도 및 높이 등과 같이 UAV를 사용하는 환경에 따라, 작은 동물, 울창한 숲속에 있는 개체, 빠르게 움직이는 동물을 탐지하는 것이 제한된다. 또한, 기상환경에 따라서도 비행이 불가할 수 있고, 배터리 용량으로 인한 비행시간의 제한도 존재한다. 하지만, 정밀한 탐지가 불가능하더라도, 이와 관련 연구가 꾸준히 수행되고 있으며, 선행연구들은 육상 및 해상 포유류, 조류, 그리고 파충류 등을 탐지하는 데에 성공하였다. UAV를 통해 얻어지는 가장 대표적인 데이터는 실화상 이미지이다. 이를 사용해 머신러닝 및 딥러닝 (ML-DL, Machine Learning and Deep Learning) 방법이 주로 사용되고 있다. 이러한 방법은 상대적으로 정확한 탐지 결과를 보여주지만, 특정 종을 탐지할 수 있는 모델의 개발을 위해선 최소한 천 장의 이미지가 필요하다. 실화상 이미지 외에도, 열화상 이미지 또한 UAV를 통해 획득 될 수 있다. 열화상 센서 기술의 개발과 센서 가격의 하락은 많은 야생동물 연구자들의 관심을 사로잡았다. 열화상 카메라를 사용하면 동물의 체온과 주변환경과의 온도 차이를 통해 정온동물을 탐지하는 것이 가능하다. 하지만, 새로운 데이터가 사용되더라도, 여전히 ML-DL 방법이 동물 탐지에 주로 사용되고 있으며, 이러한 방법은 UAV를 활용한 야생동물의 실시간 탐지를 제한한다. 따라서, 본 연구는 열화상과 실화상 이미지를 활용한 동물 자동 탐지 방법의 개발과, 개발된 방법이 이전 방법들의 평균 이상의 정확도와 함께 현장에서 실시간으로 사용될 수 있도록 하는 것을 목표로 한다.For wildlife detection and monitoring, traditional methods such as direct observation and capture-recapture have been carried out for diverse purposes. However, these methods require a large amount of time, considerable expense, and field-skilled experts to obtain reliable results. Furthermore, performing a traditional field survey can result in dangerous situations, such as an encounter with wild animals. Remote monitoring methods, such as those based on camera trapping, GPS collars, and environmental DNA sampling, have been used more frequently, mostly replacing traditional survey methods, as the technologies have developed. But these methods still have limitations, such as the lack of ability to cover an entire region or detect individual targets. To overcome those limitations, the unmanned aerial vehicle (UAV) is becoming a popular tool for conducting a wildlife census. The main benefits of UAVs are able to detect animals remotely covering a wider region with clear and fine spatial and temporal resolutions. In addition, by operating UAVs investigate hard to access or dangerous areas become possible. However, besides these advantages, the limitations of UAVs clearly exist. By UAV operating environments such as study site, flying height or speed, the ability to detect small animals, targets in the dense forest, tracking fast-moving animals can be limited. And by the weather, operating UAV is unable, and the flight time is limited by the battery matters. Although detailed detection is unavailable, related researches are developing and previous studies used UAV to detect terrestrial and marine mammals, avian and reptile species. The most common type of data acquired by UAVs is RGB images. Using these images, machine-learning and deep-learning (ML–DL) methods were mainly used for wildlife detection. ML–DL methods provide relatively accurate results, but at least 1,000 images are required to develop a proper detection model for specific species. Instead of RGB images, thermal images can be acquired by a UAV. The development of thermal sensor technology and sensor price reduction has attracted the interest of wildlife researchers. Using a thermal camera, homeothermic animals can be detected based on the temperature difference between their bodies and the surrounding environment. Although the technology and data are new, the same ML–DL methods were typically used for animal detection. These ML-DL methods limit the use of UAVs for real-time wildlife detection in the field. Therefore, this paper aims to develop an automated animal detection method with thermal and RGB image datasets and to utilize it under in situ conditions in real-time while ensuring the average-above detection ability of previous methods.Abstract I Contents IV List of Tables VII List of Figures VIII Chapter 1. Introduction 1 1.1 Research background 1 1.2 Research goals and objectives 10 1.2.1 Research goals 10 1.2.2 Research objectives 11 1.3 Theoretical background 13 1.3.1 Concept of the UAV 13 1.3.2 Concept of the thermal camera 13 Chapter 2. Methods 15 2.1 Study site 15 2.2 Data acquisition and preprocessing 16 2.2.1 Data acquisition 16 2.2.2 RGB lens distortion correction and clipping 19 2.2.3 Thermal image correction by fur color 21 2.2.4 Unnatural object removal 22 2.3 Animal detection 24 2.3.1 Sobel edge creation and contour generation 24 2.3.2 Object detection and sorting 26 Chapter 3. Results 30 3.1 Number of counted objects 31 3.2 Time costs of image types 33 Chapter 4. Discussion 36 4.1 Reference comparison 36 4.2 Instant detection 40 4.3 Supplemental usage 41 4.4 Utility of thermal sensors 42 4.5 Applications in other fields 43 Chapter 5. Conclusions 47 References 49 Appendix: Glossary 61 초록 62석