Image-Based Bidirectional Reflectance Distribution Function of Human Skin in the Visible and Near Infrared

Human detection is an important first step in locating and tracking people in many missions including SAR and ISR operations. Recent detection systems utilize hyperspectral and multispectral technology to increase the acquired spectral content in imagery and subsequently better identify targets. This research demonstrates human detection through a multispectral skin detection system to exploit the unique optical properties of human skin. At wavelengths in the VIS and NIR regions of the electromagnetic spectrum, an individual can be identified by their unique skin parameters. Current detection methods base the skin pixel selection criteria on a diffuse skin reflectance model; however, it can be observed that human skin exhibits a combination of specular and diffuse reflectance. The objective of this effort is to better characterize human skin reflectance by collecting image-based BRDF skin measurements for future model incorporation in the existing multispectral skin detection system. Integrating multispectral BRDF data should reduce misdetections and better describe skin reflectance as a function of illumination source, target, and detector orientation

A Similarity Measure for Material Appearance

We present a model to measure the similarity in appearance between different materials, which correlates with human similarity judgments. We first create a database of 9,000 rendered images depicting objects with varying materials, shape and illumination. We then gather data on perceived similarity from crowdsourced experiments; our analysis of over 114,840 answers suggests that indeed a shared perception of appearance similarity exists. We feed this data to a deep learning architecture with a novel loss function, which learns a feature space for materials that correlates with such perceived appearance similarity. Our evaluation shows that our model outperforms existing metrics. Last, we demonstrate several applications enabled by our metric, including appearance-based search for material suggestions, database visualization, clustering and summarization, and gamut mapping.Comment: 12 pages, 17 figure

a Berlin case study

Durch den Prozess der Urbanisierung verändert die Menschheit die Erdoberfläche in großem Ausmaß und auf unwiederbringliche Weise. Die optische Fernerkundung ist eine Art der Erdbeobachtung, die das Verständnis dieses dynamischen Prozesses und seiner Auswirkungen erweitern kann. Die vorliegende Arbeit untersucht, inwiefern hyperspektrale Daten Informationen über Versiegelung liefern können, die der integrierten Analyse urbaner Mensch-Umwelt-Beziehungen dienen. Hierzu wird die Verarbeitungskette von Vorverarbeitung der Rohdaten bis zur Erstellung referenzierter Karten zu Landbedeckung und Versiegelung am Beispiel von Hyperspectral Mapper Daten von Berlin ganzheitlich untersucht. Die traditionelle Verarbeitungskette wird mehrmals erweitert bzw. abgewandelt. So wird die radiometrische Vorverarbeitung um die Normalisierung von Helligkeitsgradienten erweitert, welche durch die direktionellen Reflexionseigenschaften urbaner Oberflächen entstehen. Die Klassifikation in fünf spektral komplexe Landnutzungsklassen wird mit Support Vector Maschinen ohne zusätzliche Merkmalsextraktion oder Differenzierung von Subklassen durchgeführt...thesi

Surface Reflectance Estimation and Natural Illumination Statistics

Humans recognize optical reflectance properties of surfaces such as metal, plastic, or paper from a single image without knowledge of illumination. We develop a machine vision system to perform similar recognition tasks automatically. Reflectance estimation under unknown, arbitrary illumination proves highly underconstrained due to the variety of potential illumination distributions and surface reflectance properties. We have found that the spatial structure of real-world illumination possesses some of the statistical regularities observed in the natural image statistics literature. A human or computer vision system may be able to exploit this prior information to determine the most likely surface reflectance given an observed image. We develop an algorithm for reflectance classification under unknown real-world illumination, which learns relationships between surface reflectance and certain features (statistics) computed from a single observed image. We also develop an automatic feature selection method

The applications of neural network in mapping, modeling and change detection using remotely sensed data

Thesis (Ph.D.)--Boston UniversityAdvances in remote sensing and associated capabilities are expected to proceed in a number of ways in the era of the Earth Observing System (EOS). More complex multitemporal, multi-source data sets will become available, requiring more sophisticated analysis methods. This research explores the applications of artificial neural networks in land-cover mapping, forward and inverse canopy modeling and change detection. For land-cover mapping a multi-layer feed-forward neural network produced 89% classification accuracy using a single band of multi-angle data from the Advanced Solidstate Array Spectroradiometer (ASAS). The principal results include the following: directional radiance measurements contain much useful information for discrimination among land-cover classes; the combination of multi-angle and multi-spectral data improves the overall classification accuracy compared with a single multi-angle band; and neural networks can successfully learn class discrimination from directional data or multi-domain data. Forward canopy modeling shows that a multi-layer feed-forward neural network is able to predict the bidirectional reflectance distribution function (BRDF) of different canopy sites with 90% accuracy. Analysis of the signal captured by the network indicates that the canopy structural parameters, and illumination and viewing geometry, are essential for predicting the BRDF of vegetated surfaces. The inverse neural network model shows that the R2 between the network-predicted canopy parameters and the actual canopy parameters is 0.85 for canopy density and 0.75 for both the crown shape and the height parameters. [TRUNCATED

Trying to break new ground in aerial archaeology

Aerial reconnaissance continues to be a vital tool for landscape-oriented archaeological research. Although a variety of remote sensing platforms operate within the earth’s atmosphere, the majority of aerial archaeological information is still derived from oblique photographs collected during observer-directed reconnaissance flights, a prospection approach which has dominated archaeological aerial survey for the past century. The resulting highly biased imagery is generally catalogued in sub-optimal (spatial) databases, if at all, after which a small selection of images is orthorectified and interpreted. For decades, this has been the standard approach. Although many innovations, including digital cameras, inertial units, photogrammetry and computer vision algorithms, geographic(al) information systems and computing power have emerged, their potential has not yet been fully exploited in order to re-invent and highly optimise this crucial branch of landscape archaeology. The authors argue that a fundamental change is needed to transform the way aerial archaeologists approach data acquisition and image processing. By addressing the very core concepts of geographically biased aerial archaeological photographs and proposing new imaging technologies, data handling methods and processing procedures, this paper gives a personal opinion on how the methodological components of aerial archaeology, and specifically aerial archaeological photography, should evolve during the next decade if developing a more reliable record of our past is to be our central aim. In this paper, a possible practical solution is illustrated by outlining a turnkey aerial prospection system for total coverage survey together with a semi-automated back-end pipeline that takes care of photograph correction and image enhancement as well as the management and interpretative mapping of the resulting data products. In this way, the proposed system addresses one of many bias issues in archaeological research: the bias we impart to the visual record as a result of selective coverage. While the total coverage approach outlined here may not altogether eliminate survey bias, it can vastly increase the amount of useful information captured during a single reconnaissance flight while mitigating the discriminating effects of observer-based, on-the-fly target selection. Furthermore, the information contained in this paper should make it clear that with current technology it is feasible to do so. This can radically alter the basis for aerial prospection and move landscape archaeology forward, beyond the inherently biased patterns that are currently created by airborne archaeological prospection