![]() The HSI processing chain virtually always includes their segmentation, being the process of finding coherent regions within an input image of similar characteristics, hence delineating the boundaries of the same-class objects within a scene. ![]() This issue negatively affects our abilities to train well-generalizing supervised learners for HSI analysis that could benefit from large and representative training sets. Additionally, generating ground-truth image data containing manually-delineated objects of interest is not only user-dependent and prone to human errors but also cumbersome and costly, as it requires transferring raw image data for further analysis, e.g., from an imaging satellite. Since these kinds of data are extremely highly-dimensional, its efficient acquisition, transfer, storage and analysis are important real-life challenges that need to be faced in practical scenarios. Because such imagery can provide very detailed characteristics of the scanned objects and can help extract insights that are invisible to the human eye, hyperspectral imaging has attracted research interest in various fields of science and industry, including mineralogy, precision agriculture, medicine, chemistry, forensics, and remote sensing. Hyperspectral images (HSIs) capture a large number of narrow channels, referred to as bands, acquired for a continuous span of the electromagnetic spectrum. ![]() The experiments, performed for both hyperspectral data classification and unmixing, show that our deep ensembles outperform base spectral and spectral-spatial deep models and classical ensembles employing voting and averaging as a fusing scheme in both hyperspectral image analysis tasks. Furthermore, we propose a model augmentation technique that allows us to synthesize new deep networks based on the original one by injecting Gaussian noise into the model’s weights. In this paper, we introduce the deep ensembles benefiting from different architectural advances of convolutional base models and suggest a new approach towards aggregating the outputs of base learners using a supervised fuser. Although in both of them, the deep learning techniques have bloomed as an extremely effective tool, designing the deep models that generalize well over the unseen data is a serious practical challenge in emerging applications. The research efforts have been focusing on developing algorithms for hyperspectral data classification and unmixing, which are two main tasks in the analysis chain of such imagery. However, such image data are difficult to transfer due to their large volume, and generating new ground-truth datasets that could be utilized to train supervised learners is costly, time-consuming, very user-dependent, and often infeasible in practice. Hyperspectral images capture very detailed information about scanned objects and, hence, can be used to uncover various characteristics of the materials present in the analyzed scene.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |