Shadow Detection in Remotely Sensed Images Based on Self-Adaptive Feature Selection

Shadows in remotely sensed images create difficulties in many applications; thus, they should be effectively detected prior to further processing. This paper presents a novel semiautomatic shadow detection method that meets the requirements of both high accuracy and wide practicability in remote sensing applications. The proposed method uses only the properties derived from the shadow samples to dynamically generate a feature space and calculate decision parameters; then, it employs a series of transformations to separate shadow and nonshadow regions. The proposed method can detect shadows from both color and gray images. If the chromatic properties of color images do not agree with the defined rules through the shadow samples, then the shadow detection process will automatically reduce to the process for gray images. As the shadow samples are manually selected from the input image by the user, the derived parameters conform well to the characteristics of the input image. Experiments and comparisons indicate that the proposed self-adaptive feature selection algorithm is accurate, effective, and widely applicable to shadow detection in practical applications.