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In this talk, we will introduce some hyperspectral image classification methods based on deep learning architecture. Recently, deep learning-based hyperspectral image classification has attracted more...
A significant surge has been observed with the development and research in remote sensing in recent years for hyperspectral applications in Earth observation. Subsequently, the development of software...
Hyperspectral images (HSIs) denoising is a critical research area in image processing duo to its importance in improving the quality of HSIs, which has a negative impact on object detection and classi...
Classification is one of the most significant applications of hyperspectral image processing and even remote sensing. Though various algorithms have been proposed to implement and improve this applica...
Python is a very popular programming language among data scientists around the world. Python can also be used in hyperspectral data analysis. There are some toolboxes designed for spectral imaging, su...
Recently, hyperspectral image analysis has obtained successful results in information extraction for earth remote sensing system. The data produced with this type of analysis is an important compone...
In this paper a new classification technique for hyperspectral data based on synergetics theory is presented. Synergetics – originally introduced by the physicist H. Haken – is an interdisciplinary th...
Dimensionality reduction algorithms, which aim to select a small set of efficient and discriminant features, have attracted great attention for Hyperspectral Image Classification. The manifold learnin...
In optically shallow waters, i.e., when the bottom is visible through the water, a tantalizing variety and level of detail about bottom characteristics are apparent in aerial imagery (Figure 1a). Some...

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