Remotely sensed hyperspectral sensors provide image data containing rich information in both the spatial and the spectral domain, and this information can be used to address detection tasks in many applications. In many surveillance applications, the size of the objects (targets) searched for constitutes a very small fraction of the total search area and the spectral signatures associated to the targets are generally different from those of the background, hence the targets can be seen as anomalies. In hyperspectral imaging, many algorithms have been proposed for automatic target and anomaly detection. Given the dimensionality of hyperspectral scenes, these techniques can be time-consuming and difficult to apply in applications requiring real-time performance. In this paper, we develop several new parallel implementations of automatic target and anomaly detection algorithms. The proposed parallel algorithms are quantitatively evaluated using hyperspectral data collected by the NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) system over theWorld Trade Center (WTC) in New York, five days after the terrorist attacks that collapsed the two main towers in theWTC complex.
SUMMARYHyperspectral imaging instruments are capable of collecting hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. One of the main problems in the analysis of hyperspectral data cubes is the presence of mixed pixels, which arise when the spatial resolution of the sensor is not enough to separate spectrally distinct materials. Hyperspectral unmixing is one of the most popular techniques to analyze hyperspectral data. It comprises two stages: (i) automatic identification of pure spectral signatures (endmembers) and (ii) estimation of the fractional abundance of each endmember in each pixel. The spectral unmixing process is quite expensive in computational terms, mainly due to the extremely high dimensionality of hyperspectral data cubes. Although this process maps nicely to high performance systems such as clusters of computers, these systems are generally expensive and difficult to adapt to real-time data processing requirements introduced by several applications, such as wildland fire tracking, biological threat detection, monitoring of oil spills, and other types of chemical contamination. In this paper, we develop an implementation of the full hyperspectral unmixing chain on commodity graphics processing units (GPUs). The proposed methodology has been implemented, using the CUDA (compute device unified architecture), and tested on three different GPU architectures: NVidia Tesla C1060, NVidia GeForce GTX 275, and NVidia GeForce 9800 GX2, achieving near real-time unmixing performance in some configurations tested when analyzing two different hyperspectral images, collected over the World Trade Center complex in New York City and the Cuprite mining district in Nevada.
The spread of aquatic invasive plants is a major concern in several zones of the world's geography. These plants, which are not part of the natural ecosystem, cause a negative impact to the environment, as well as to economy and society. In Spain, large areas of Guadiana (the second longest river in Spain) have been invaded by such plants. Among the strategies to address this problem, monitoring and detection play an important role to control the spatio-temporal distribution of the invasive plants. The main objective of this work is to develop a methodology able to automatically detect the geo-location of aquatic invasive plants using remote sensing and machine learning techniques. To this end, several classification algorithms have been applied to freely available multispectral satellite imagery, collected by ESA's Sentinel-2 satellite. A quantitative and comparative assessment is conducted using different machine and deep learning algorithms, from classical methods such as unsupervised K-means to supervised random forests (RFs) and convolutional neural networks (CNNs). This study also proposes a methodology for validating the obtained classification results, generating synthetic ground truth images based on available high spatial resolution imagery. The obtained results demonstrate the suitability of some of the considered algorithms for automatic detection of aquatic weeds in satellite images with medium spatial resolution.
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