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. 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. This means that the RS community should not only be aware of advancements such as DL, but also be leading researchers in this area. 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. Namely, we focus on theories, tools, and challenges for the RS community. 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.
Finding a best clustering algorithm to tackle the problem of finding the optimal partition of a data set is always
an NP-hard problem. In general, solutions to the NP-hard problems involve searches through vast spaces of
possible solutions and evolutionary algorithms have been a success. In this paper, we explore one such approach
which is hardly known outside the search heuristic field - the Particle Swarm Optimisation+k-means (PSOk)
for this purpose. The proposed hybrid algorithm consists of two modules, the PSO module and the k-means
module. For the initial stage, the PSO module is executed for a short period to search for the clusters centroid
locations. Succeeding to the PSO module is the refining stage where the detected locations are transferred to
the k-means module for refinement and generation of the final optimal clustering solution. Experimental results
on two challenging datasets and a comparison with other hybrid PSO methods has demonstrated and validated
the effectiveness of the proposed solution in terms of precision and computational complexity.