As such, automated methods for detecting and classifying the types of blood cells have important medical applications in this field. The diagnosis of blood related diseases involves the identification and characterization of a patient's blood sample. Specifically, we focus on unsolved challenges and opportunities as they relate to (i) inadequate data sets, (ii) human-understandable solutions for modeling physical phenomena, (iii) big data, (iv) nontraditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial, and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.
Namely, we focus on theories, tools, and challenges for the RS community. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research.We also review recent new developments in the DL field that can be used in DL for RS. This means that the RS community should not only be aware of advancements such as DL, but also be leading researchers in this area. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV, e.g., statistics, fusion, and machine learning, to name a few.
In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, and natural language processing.
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