Detection of trace amount chemical substances in hyperspectral images

Hyperspectral images, by their very nature, provide a great deal of information about the contents of an image. In this way, they open the way to reveal the evidences that cannot be distinguished or detected properly by the analysis of single or 3-band images. Shots made with hyperspectral cameras, which provide an information vector consisting of hundreds of bands for each pixel, provide spectral analysis even in cases where spatial analysis of the objects in the image is not possible or can be made very limited. Spectral separation of objects or backgrounds that can resemble each other in color or shape is made possible with hyperspectral imaging technology. There are many approaches that can be tried to analyze hyperspectral images, to classify the objects in their content using these images, or to detect a searched object. These approaches can be shaped based on different methods, depending on whether there is a preliminary information about the searched object, whether there is a rich data set on the analysis of the problem, or whether the data acquisition conditions change based on different parameters settings. For example, anomaly detection-based methods are used for the detection of objects that are not known exactly but separated from the background, while spectral signature matching-oriented methods can be used in cases where the preliminary information about the sought object is provided. v In this thesis, studies for the detection and identification of trace amounts of chemical substances placed at levels measured in nanograms (500ng -10000ng) on different backgrounds are described. Many different difficulties are faced such as target and background diversity, placement of the very small amount of target, using raw data without any radiometric correction, change of light and target position. The solutions developed to avoid these difficulties and to produce a robust algorithm are presented step by step. First of all, when the raw radiance data taken from the sensor is examined, it is observed that the background and target separation cannot be made well in raw data. For this reason, a number of data conversion algorithms are applied for data rectification with different operations on the received radiance data. Thus, target and background separation is performed more successfully. Afterwards, various signature-based target detection algorithms have been tried and the methods that produce successful results for the problem that is the subject of this thesis have been revealed. Solving the problem of detection and identification of chemicals requires creating an algorithm that takes images and other necessary information as input and gives target type and location information as output. Hence, creating score maps is not enough. Steps that produce the necessary outputs by using relevant score maps are also needed. For this purpose, post-processing algorithms that lead to more robust results are also part of the work done in this thesis. These steps eliminate false alarms as much as possible. In summary, a hybrid method called "Multiple Target Detection" has been developed by using different spectral signature matching-oriented methods in the literature to solve the above-mentioned problem. First, the hyperspectral image taken in the ultraviolet and near infrared spectrum and the preprocessing steps (whitening and Savitzky-Golay filter) applied to the target reference signatures sought in the image are detailed. Afterwards, the applied hybrid detection method is explained in detail. A two-stage score map generation algorithm based on cross-correlation and spectral information divergence algorithms is described. Then information is given about the final processing steps, which include conditions such as thresholding according to the score and selecting final detected areas by connective pixel analysis for potential target clusters. In addition to the "Multiple Target Detection" method, a method named POSTNET, which provides the final processing steps applied on the previously obtained score maps with deep learning, has been developed and presented in detail. In addition, the great contribution of vi the pre-processing steps applied to the hyperspectral data and the post-processing steps applied after creation of score maps are emphasized.