Nearshore bars are important coastal features that influence beach–dune dynamics and protect the coast from high-energy wave events. Historically, in situ methods were used to study beach–dune dynamics and nearshore bars. Video monitoring systems, such as Argus, have been the favored remote sensing approach for studying nearshore bars dynamics and evolution. These systems are costly to install and maintain, and a limited number of stations are available. Our study introduces a lower cost alternative to study these coastal features. A rule-based classification object-based image analysis approach is introduced to identify nearshore bars (and their characteristics, such as bar length, width, count, and distance from the wet/dry line) from very-high-resolution (VHR) multispectral imagery. Nearshore bars were successfully identified and characterized as single and multiple bar systems. Our study demonstrates that VHR satellite remote sensing is a viable alternative with comparative results to video monitoring systems. Their capability of acquiring high-resolution imagery in large spatial extent is superior in continuous observation and characterization of nearshore bars. The proposed approach provides information at a more relevant spatiotemporal scale at which these systems evolve, which is helpful to coastal scientists and managers.