RSSIM: A Simulation Program for Optical Remote Sensing Systems

RSSIM is a comprehensive simulation tool for the study of multispectral remotely sensed images and associated system parameters. It has been developed to allow the creation of realistic multispectral images based on detailed models of the surface the atmosphere, and the sensor. It also can be used to study the effect of system parameters on an output measure, such as classification accuracy or class separability. In this report the operation and use of RSSIM is described. In this first section the implementation of the program is discussed, followed by examples of its use. In section 2 the structure and algorithms used in the major subroutines, along with the associated parameter files are discussed. Section 3 provides a complete listing of the program code

Modeling, Simulation, and Analysis of Optical Remote Sensing Systems

Remote Sensing of the Earth\u27s resources from space-based sensors has evolved in the past twenty years from a scientific experiment to a commonly used technological tool. The scientific applications and engineering aspects of remote sensing systems have been studied extensively. However, most of these studies have been aimed at understanding individual aspects of the remote sensing process while relatively few have studied their interrelations. A motivation for studying these interrelationships has arisen with the advent of highly sophisticated configurable sensors as part of the Earth Observing System (EOS) proposed by NASA for the 1990\u27s. These instruments represent a tremendous advance in sensor technology with data gathered In nearly 200 spectral bands, and with the ability for scientists to specify many observational parameters. It will be increasingly necessary for users of remote sensing systems to understand the tradeoffs and interrelationships of system parameters. In this report, two approaches to investigating remote sensing systems are developed. In one approach, detailed models of the scene, the sensor, and the processing aspects of the system are implemented In a discrete simulation, This approach is useful in creating simulated images with desired characteristics for use in sensor or processing algorithm development. A less complete, but computationally simpler method based on a parametric model of the system is also developed. In this analytical model the various informational classes are parameterized by their spectral mean vector and covariance matrix. These Class statistics are modified by models for the atmosphere, the sensor, and processing algorithms and an estimate made of the resulting classification accuracy among the informational classes. Application of these models is made to the study of the proposed High Resolution Imaging Spectrometer (HIRIS).; The interrelationships among observational conditions, sensor effects, and processing choices are investigated with several interesting results. Reduced classification accuracy in hazy atmospheres is seen to be due not only to sensor noise, but also to the increased path radiance scattered from the surface. The effect of the atmosphere is also seen in its relationship to view angle. In clear atmospheres, increasing the zenith view angle is seen to result in an increase in classification accuracy due to the reduced scene variation as the ground size of image pixels is increased. However, in hazy atmospheres the reduced transmittance and increased path radiance counter this effect and result in decreased accuracy with increasing view angle. The relationship between the Signal-to:Noise Ratio (SNR) and classification accuracy is seen to depend in a complex manner on spatial parameters and feature selection. Higher SNR values are seen to hot always result in higher accuracies, and even in cases of low SNR feature sets chosen appropriately can lead to high accuracies

Hybridization of Hyperspectral Imaging Target Detection Algorithm Chains

Detection of a known target in an image can be accomplished using several different approaches. The complexity and number of steps involved in the target detection process makes a comparison of the different possible algorithm chains desirable. Of the different steps involved, some have a more significant impact than others on the final result - the ability to find a target in an image. These more important steps often include atmospheric compensation, noise and dimensionality reduction, background characterization, and detection (matched filtering for this research). A brief overview of the algorithms to be compared for each step will be presented. This research seeks to identify the most effective set of algorithms for a particular image or target type. Several different algorithms for each step will be presented, to include ELM, FLAASH, MNF, PPI, MAXD, the structured background matched filters OSP, and ASD. The chains generated by these algorithms will be compared using the Forest Radiance I HYDICE data set. Finally, receiver operating characteristic (ROC) curves will be calculated for each algorithm chain and, as an end result, a comparison of the various algorithm chains will be presented

Comparisons Between Spectral Quality Metrics and Analyst Performance in Hyperspectral Target Detection

Quantitative methods to assess or predict the quality of a spectral image continue to be the subject of a number of current research activities. An accepted methodology would be highly desirable for use in data collection tasking or data archive searching in ways analogous to the current prediction of panchromatic image quality through the National Imagery Interpretation Rating Scale (NIIRS) using the General Image Quality Equation (GIQE). A number of approaches to the estimation of quality of a spectral image have been published, but most capture only the performance of automated algorithms applied to the spectral data. One recently introduced metric, however, the General Spectral Utility Metric (GSUM), provides for a framework to combine the performance from the spectral aspects together with the spatial aspects. In particular, this framework allows the metric to capture the utility of a spectral image resulting when the human analyst is included in the process. This is important since nearly all hyperspectral imagery analysis procedures include an analyst. To investigate the relationships between candidate spectral metrics and task performance from volunteer human analysts in conjunction with the automated results, simulated images are generated and processed in a blind test. The performance achieved by the analysts is then compared to predictions made from various spectral quality metrics to determine how well the metrics function. The task selected is one of finding a specific vehicle in a cluttered environment using a detection map produced from the hyperspectral image along with a panchromatic rendition of the image. Various combinations of spatial resolution, number of spectral bands, and signal-to-noise ratios are investigated as part of the effort

Matched Filter Stochastic Background Characterization for Hyperspectral Target Detection

Algorithms exploiting hyperspectral imagery for target detection have continually evolved to provide improved detection results. Adaptive matched filters can be used to locate spectral targets by modeling scene background as either structured (geometric) with a set of endmembers (basis vectors) or as unstructured (stochastic) with a covariance or correlation matrix. These matrices are often calculated using all available pixels in a data set. In unstructured background research, various techniques for improving upon scene-wide methods have been developed, each involving either the removal of target signatures from the background model or the segmentation of image data into spatial or spectral subsets. Each of these methods increase the detection signal-to-background ratio (SBR) and the multivariate normality (MVN) of the data from which background statistics are calculated, thus increasing separation between target and non-target species in the detection statistic and ultimately improving thresholded target detection results. Such techniques for improved background characterization are widely practiced but not well documented or compared. This paper provides a review and comparison of methods in target exclusion, spatial subsetting and spectral pre-clustering, and introduces a new technique which combines these methods. The analysis provides insight into the merit of employing unstructured background characterization techniques, as well as limitations for their practical application

Myosin II Motors and F-Actin Dynamics Drive the Coordinated Movement of the Centrosome and Soma during CNS Glial-Guided Neuronal Migration

SummaryLamination of cortical regions of the vertebrate brain depends on glial-guided neuronal migration. The conserved polarity protein Par6α localizes to the centrosome and coordinates forward movement of the centrosome and soma in migrating neurons. The cytoskeletal components that produce this unique form of cell polarity and their relationship to polarity signaling cascades are unknown. We show that F-actin and Myosin II motors are enriched in the neuronal leading process and that Myosin II activity is necessary for leading process actin dynamics. Inhibition of Myosin II decreased the speed of centrosome and somal movement, whereas Myosin II activation increased coordinated movement. Ectopic expression or silencing of Par6α inhibited Myosin II motors by decreasing Myosin light-chain phosphorylation. These findings suggest leading-process Myosin II may function to “pull” the centrosome and soma forward during glial-guided migration by a mechanism involving the conserved polarity protein Par6α

Original Climax Films: historicizing the British hardcore pornography film business

This article presents findings from my research into the British hardcore pornography business. Porn studies has given little coverage to the British pornography business, with much of the academic literature focusing on the American adult entertainment industry. Recently, there has been a rising interest in the historical framework of porn cinemas both in popular culture and in academic work. This article contributes to this debate, taking both a cultural and an economic approach to explore the conditions that led to the emergence of British hardcore production as an alternative economy in the 1960s. In this economy, entrepreneurs make use of new technologies to produce artefacts that are exchanged for an economic benefit, while circumventing laws to distribute their artefacts. To historicize this economy, I draw on ethnohistorical research, which includes interviews with people involved in the British hardcore business and archival research. I argue that a combination of glamour filmmaking, a relaxation of political and cultural attitudes towards sexuality, the location of Soho, London, and emerging technologies for producing films collectively contribute to the emergence of an alternative economy of British hardcore production. I focus specifically on the practices of two entrepreneurs within this economy, Ivor Cook and Mike Freeman, considering how their actions inadvertently created the British hardcore film business, and played a significant role in the development of hardcore production outside of the United Kingdom

Leukaemia exposure alters the transcriptional profile and function of BCR::ABL1 negative macrophages in the bone marrow niche

Macrophages are fundamental cells of the innate immune system that support normal haematopoiesis and play roles in both anti-cancer immunity and tumour progression. Here we use a chimeric mouse model of chronic myeloid leukaemia (CML) and human bone marrow (BM) derived macrophages to study the impact of the dysregulated BM microenvironment on bystander macrophages. Utilising single-cell RNA sequencing (scRNA-seq) of Philadelphia chromosome (Ph) negative macrophages we reveal unique subpopulations of immature macrophages residing in the CML BM microenvironment. CML exposed macrophages separate from their normal counterparts by reduced expression of the surface marker CD36, which significantly reduces clearance of apoptotic cells. We uncover aberrant production of CML-secreted factors, including the immune modulatory protein lactotransferrin (LTF), that suppresses efferocytosis, phagocytosis, and CD36 surface expression in BM macrophages, indicating that the elevated secretion of LTF is, at least partially responsible for the supressed clearance function of Ph- macrophages