Abstract
Abstract. Hyperspectral imaging, with its applications, offers promising tools for remote sensing and Earth observation. Recent development has increased the quality of the sensors. At the same time, the prices of the sensors are lowering. Anomaly detection is one of the popular remote sensing applications, which benefits from real-time solutions. A real-time solution has its limitations, for example, due to a large amount of hyperspectral data, platform’s (drones or a cube satellite) constraints on payload and processing capability. Other examples are the limitations of available energy and the complexity of the machine learning models. When anomalies are detected in real-time from the hyperspectral images, one crucial factor is to utilise a computationally efficient method. The Minimal Learning Machine is a distance-based classification algorithm, which can be modified for anomaly detection. Earlier studies confirms that the Minimal learning Machine (MLM) is capable of detecting efficiently global anomalies from the hyperspectral images with a false alarm rate of zero. In this study, we will show that by using a carefully selected lower threshold besides the higher threshold of the variance, it is possible to detect local and global anomalies with the MLM. The downside is that the improved method is highly sensitive with the respect to the noise. Thus, the second aim of this study is to improve the MLM’s robustness with respect to noise by introducing a novel approach, the piecewise MLM. With the new approach, the piecewise MLM can detect global and local anomalies, and the method is significantly more robust with respect to noise than the MLM. As a result, we have an interesting, easy to implement and computationally light method which is suitable for remote sensing applications.
Highlights
The Hyperspectral (HS) image typically consists of a stack of frames, where each frame represents the intensity of a different wavelength of light, and each pixel has its spectrum
Our hypothesis is that by implementing the Minimal Learning Machine (MLM) with a piecewise approach, class-by-class, we can significantly improve results of the previous MLM anomaly detection method
We developed a piecewise MLM approach, which aims to improve the accuracy rate of the previous version of the MLM anomaly detection method (Polonen et al, 2020) by including the detection of the local anomalies to the results being more robust for the noisiness of the data
Summary
The Hyperspectral (HS) image typically consists of a stack of frames, where each frame represents the intensity of a different wavelength of light, and each pixel has its spectrum. Each pixel spectrum is evaluated, and the aim is to detect pixels whose spectral signature differs from their surroundings. The challenges of the spectral anomaly detection methods are usually combinations of the large amounts of data, platform’s constraints on payload, processing capability, and restricted available energy with complex machine learning models (Haut et al, 2018, Caba et al, 2020). The exponentially growing high dimensional data challenges the real-time processing, the data analysis processes and the technical features (Chen et al, 2018, Bioucas-Dias et al, 2013). One of the main advantages of real-time processing is improved data quality since it is not compressed and transmitted to the processor (Chen et al, 2018). Other advantages are the reduced need for the communication between the ground equipment and the platform, the reduced need for the data processing on the ground and the possibility to get the real-time responses from the platform (Che et al, 2018)
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