In the authors' previous work, a sequence of image-processing algorithms was developed that was suitable for detecting and classifying ships from panchromatic Quickbird electro-optical satellite imagery. Presented in this paper are several new algorithms, which improve the performance and enhance the capabilities of the ship detection software, as well as an overview on how land masking is performed. Specifically, this paper describes the new algorithms for enhanced detection including for the reduction of false detects such as glint and clouds. Improved cloud detection and filtering algorithms are described as well as several texture classification algorithms are used to characterize the background statistics of the ocean texture. These detection algorithms employ both cloud and glint removal techniques, which we describe. Results comparing ship detection with and without these false detect reduction algorithms are provided. These are components of a larger effort to develop a low-cost solution for detecting the presence of ships from readily-available overhead commercial imagery and comparing this information against various open-source ship-registry databases to categorize contacts for follow-on analysis.