The detection of temporal changes or new activity in large areas of satellite imagery have been of great interest in many applications ranging from meteorology, environmental-sensing, to defense and disaster monitoring. Automated change detection algorithms play a significant role in analyzing large volumes of satellite data, which otherwise would be a manually laborious task. This paper deals with the evaluation of various change detection methods for detecting both structured and unstructured changes in multi-spectral satellite imagery. A comparison of the algorithm performance on both real and simulated satellite imagery is presented here. The real-world dataset (WorldView-2) required accurate geometric registration and manual ground-truth labelling, while on the other hand, the simulated satellite imagery (generated with the RIT DIRSIG tool) has the advantage of prompt availability and included ground-truth. A variety of structural spatial and spectral changes were incorporated into the simulated images according to the required complexity and the ground truth generated together with the imagery. An array of algorithms were evaluated and compared, including conventional methods such as differencing, ratioing, image transformation techniques; statistical methods such as chronochrome, covariance equalization; linear/non-linear distribution-based methods such as point density, simplex volume/complexity, mean-shift/outlier metrics, and non-linear graph-based methods. The Receiver-Operating-Characteristic (ROC) curve and Area-Under-Curve (AUC) metrics were used to evaluate the algorithms. Finally, the performance of various algorithms on simulated vs real satellite images is also discussed, which may be helpful in further applications of the simulated imagery, such as for training deep-learning networks, where real imagery is not readily available.
Blood stains are among the most important types of evidence for forensic investigation. They contain valuable DNA information, and the pattern of the stains can suggest specifics about the nature of the violence that transpired at the scene. Early detection of blood stains is particularly important since the blood reacts physically and chemically with air and materials over time. Accurate identification of blood remnants, including regions that might have been intentionally cleaned, is an important aspect of forensic investigation. Hyperspectral imaging might be a potential method to detect blood stains because it is non-contact and provides substantial spectral information that can be used to identify regions in a scene with trace amounts of blood. The potential complexity of scenes in which such vast violence occurs can be high when the range of scene material types and conditions containing blood stains at a crime scene are considered. Some stains are hard to detect by the unaided eye, especially if a conscious effort to clean the scene has occurred (we refer to these as “latent” blood stains). In this paper we present the initial results of a study of the use of hyperspectral imaging algorithms for blood detection in complex scenes. We describe a hyperspectral imaging system which generates images covering 400 nm - 700 nm visible range with a spectral resolution of 10 nm. Three image sets of 31 wavelength bands were generated using this camera for a simulated indoor crime scene in which blood stains were placed on a T-shirt and walls. To detect blood stains in the scene, Principal Component Analysis (PCA), Subspace Reed Xiaoli Detection (SRXD), and Topological Anomaly Detection (TAD) algorithms were used. Comparison of the three hyperspectral image analysis techniques shows that TAD is most suitable for detecting blood stains and discovering latent blood stains.
Blood stains are one of the most important types of evidence for forensic investigation. They contain valuable DNA information, and the pattern of the stains can suggest specifics about the nature of the violence that transpired at the scene. Blood spectral signatures containing unique reflectance or absorption features are important both for forensic on-site investigation and laboratory testing. They can be used for target detection and identification applied to crime scene hyperspectral imagery, and also be utilized to analyze the spectral variation of blood on various backgrounds. Non-blood stains often mislead the detection and can generate false alarms at a real crime scene, especially for dark and red backgrounds. This paper measured the reflectance of liquid blood and 9 kinds of non-blood samples in the range of 350 nm - 2500 nm in various crime scene backgrounds, such as pure samples contained in petri dish with various thicknesses, mixed samples with different colors and materials of fabrics, and mixed samples with wood, all of which are examined to provide sub-visual evidence for detecting and recognizing blood from non-blood samples in a realistic crime scene. The spectral difference between blood and non-blood samples are examined and spectral features such as “peaks” and “depths” of reflectance are selected. Two blood stain detection methods are proposed in this paper. The first method uses index to denote the ratio of “depth” minus “peak” over“depth” add“peak” within a wavelength range of the reflectance spectrum. The second method uses relative band depth of the selected wavelength ranges of the reflectance spectrum. Results show that the index method is able to discriminate blood from non-blood samples in most tested crime scene backgrounds, but is not able to detect it from black felt. Whereas the relative band depth method is able to discriminate blood from non-blood samples on all of the tested background material types and colors.
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