Special Section Guest Editorial: Feature and Deep Learning in Remote Sensing Applications

John E. Ball, Derek T. Anderson, Chee Seng Chan
2018 Journal of Applied Remote Sensing  
Remote sensing is an extremely active area of research that impacts global topics like agriculture, disaster monitoring and response, defense and security, weather, and non-earth observations. The technologies that power remote sensing-i.e., allow us to observe the universe-include hyperspectral imaging, synthetic aperture radar (SAR), electro-optical, thermal, light detection and ranging (LiDAR), etc. However, while we have advanced optical tools to sense the universe, we lack in computational
more » ... sophistication to automatically transform this objective data to human-centric decisions. Specifically, humans have been the architects to date of features, algorithms (e.g., classifiers) and their fusion within and across sensors and platforms (e.g., satellites, UAVs, etc.). In recent times, it has become clear that even the best experts are not always able to decide what set of transformations (features, classifiers, etc.) is sufficient for a given problem. The last two decades have represented an uprising against "hand-crafted solutions" in areas like signal/image processing, computer vision, and machine learning. The most famous of these revolts is deep learning, a resurrection of neural networks. The crux of this approach is that machines are better than humans at tasks like those outlined above. This special section is centered on recent advancements in deep learning (and just feature learning in general) in the area of remote sensing. Deep learning has become the de facto for tasks like detection in computer vision on RGB imagery. However, it has not yet made the same impact on remote sensing. In part, this is because remote sensing has many unique challenges. For example, geospatial systems are plagued by factors like lack of (spatial, spectral, and temporal) labeled training data, high (spatial, spectral, and temporal) dimensionality, domain constraints (e.g., physics), and the need to integrate multiple sources (humans, machines, and sensors), to name a few. Whereas we are excited about the potential of deep learning for remote sensing, we are equally nervous about whether this technology can deliver. Furthermore, deep learning typically results in black-box solutions that give us little to no insight into how they are working and why we should trust them. Regardless of its fate, it is an analytics tool to help us better understand these sensors, platforms, and applications. In this special section, we requested a combination of theory and applications papers on a variety of topics in remote sensing to showcase what has been done, what is being done, and what big questions remain and need to be tackled by the community. The special section encompassed twenty papers, which included one survey paper; three SAR papers; two papers on ocean remote sensing; four papers on classification and labeling; two papers using multi-modal processing; two papers utilizing spectral-spatial processing for hyperspectral image analysis; three papers on object tracking and recognition; one paper studying how deep networks need to be for remote sensing; one paper on domain adaptation; and one paper on feature extraction methods. These papers are discussed briefly below, where we highlight the main contributions and how certain challenges are overcome in the proposed methods. A common theme encountered was the use of nonremote sensing pretrained networks and transfer learning. Most articles used or extended convolutional neural networks (CNNs) and were application oriented, with a few providing new deep learning models and modules. Most papers exploited electro-optical data, but there were some SAR, hyperspectral, and multitemporal
doi:10.1117/1.jrs.11.042601 fatcat:pq3xg2sggfdtljjs3hrmp7tzdm