Hyperspectral sensing is a valuable tool for detecting anomalies and distinguishing between materials in a scene. Hyperspectral anomaly detection (HS-AD) helps characterize the captured scenes and separates them into anomaly and background classes. It is vital in agriculture, environment, and military applications such as RSTA (reconnaissance, surveillance, and target acquisition) missions. We previously designed an equal voting ensemble of hyperspectral unmixing and three unsupervised HS-AD algorithms. We later utilized a supervised classifier to determine the weights of a voting ensemble, creating a hybrid of heterogeneous unsupervised HS-AD algorithms with a supervised classifier in a model stacking, which improved detection accuracy. However, supervised classification methods usually fail to detect novel or unknown patterns that substantially deviate from those seen previously. In this work, we evaluate our technique and other supervised and unsupervised methods using general hyperspectral data to provide new insights.
Research supporting improved anomaly detection performance benefits a wide range of technical applications, and thus, the definition of what anomalies are and the subsequent means to detect them are wide ranging. In this treatment, an overview of the development of an anomaly detection approach based on spectral signatures obtained with hyperspectral unmixing is presented. The algorithm is designed to address some of the shortcomings of current techniques whose functionality is dependent upon normalized differences between discrete frequencies or spectral components, or those based on estimated distances between background spectra and pixels under test. Details about the extracted endmembers and their use for effective anomaly detection will be presented as well as, some thoughts on the expected requirements for future machine learning based implementations.
KEYWORDS: Image restoration, Super resolution, Light sources and illumination, Deep learning, Microscopy, Image processing, Image enhancement, 3D modeling
In medical and microscopy imaging applications where the object is not directly visible, images are never identical to the ground truth. In three-dimensional structured illumination microscopy (3D-SIM), acquired images taken from the object have limited resolution due to the the point spread function (PSF) of the imaging system. Additionally, due to the data acquisition process, images taken under low light and in the presence of electrooptical noise can have a low signal-to-noise ratio as well as suffer from other undesirable aberrations. To obtain a high-resolution restored image, the data must be digitally processed. The inverse imaging problem in 3D-SIM has been solved using various computational imaging techniques. Traditional model-based computational approaches can result in image artifacts due to required, yet not accurately known system parameters. Furthermore, some iterative computational imaging methods can be computationally intensive. Deep learning (DL) approaches, as opposed to traditional image restoration methods, can tackle the issue without access to the analytical model. Although some are effective, they are biased since they do not use the 3D-SIM model. This research aims to provide an unrolled physics-informed (UPI) generative adversarial network (UPIGAN) for reconstructing 3D-SIM images utilizing data samples of mitochondria from a 3D-SIM system. This design uses the benefits of physics knowledge in the unrolling step. Moreover, the GAN employs a Residual Channel Attention super-resolution deep neural network (DNN) in its generator architecture. The results from both a qualitative and quantitative comparison, present a positive impact on the reconstruction when the UPI term is used in the GAN versus using the GAN architecture without it.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.