[visionlist] CFP: Special Issue in Journal of Applied Remote Sensing: Feature and Deep Learning in Remote Sensing Applications

Call for Papers: Feature and Deep Learning in Remote Sensing Applications

The shift from ‘human features’ to machine-learned features has resulted in phenomenal results in numerous signal/image processing applications, from computer vision to speech recognition. Well-known examples of deep learning include deep belief nets (DBNs), convolutional neural networks (CNNs) and morphological shared weight neural networks (MSNNs), whereas feature learning in general includes techniques such as evolutionary constructed features (ECO) and improved ECO (iECO). Recently, feature and deep learning (FaDL) has made its way into numerous remote sensing applications, which includes analysis using sensors such as synthetic aperture radar (SAR), light detection and ranging (LiDAR), hyperspectral imaging, etc. These sensors provide heterogeneous data and they represent different regions of the electromagnetic spectrum. While FaDL has seen success in applications where large amounts of diverse data exist, FaDL in remote sensing is plagued by spectral, spatial, and temporal dimensionality, and usually has few training samples available due to the high cost of providing labeled data. In addition, most FaDL tools have a large number of parameters to estimate, and they take substantial hardware and time to train and test, which is often not realistic for many remotely sensed applications due to cost or time reasons.

The Journal of Applied Remote Sensing (JARS, Impact Factor: 0.937) will publish a special section on feature and deep learning applied to remote sensing applications. The scope includes, but is not limited to:

Remote sensing applications: agriculture, automated target detection, autonomy, change detection, disaster assessment, environmental sensing, forestry, hydrology, land cover classification, soil analysis, ocean sensing, urban analysis/planning, water resource analysis, and water control assessment.

Sensors: multi/hyperspectral, LiDAR, radar, synthetic aperture radar, automotive radar, stereo cameras, infrared (thermal), and sonar.

Multimodality: multisensor fusion at different stages in the data-processing lifetime.

FaDL challenges in remote sensing: limited training data, high spectral dimensionality, multisensor fusion, multiresolution data, and robust performance due to factors such as degradation effects like dust, rain, fog, etc.

Both application and theoretical papers are welcome. To submit to this special section, prepare the paper according to JARS guidelines (http://ift.tt/2hJnqLz) and submit via the online submission system (http://ift.tt/2gEpwXT). A cover letter indicating that the submission is intended for this special section should be included. Papers will be peer-reviewed in accordance with the journal’s established policies and procedures.

Manuscripts are due 31 March, 2017.

Guest Editors:

John E. BallMississippi State UniversityBagley College of EngineeringElectrical & Computer Engineering DepartmentMississippi State, Mississippi, United States E-mail: jeball@ece.msstate.edu

Derek T. AndersonMississippi State UniversityBagley College of EngineeringElectrical & Computer Engineering DepartmentMississippi State, Mississippi, United States E-mail: anderson@ece.msstate.edu

Chee Seng ChanUniversity of MalayaFaculty of Computer Science & Information TechnologyKuala Lumpur, Malaysia E-mail: cs.chan@um.edu.my

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