In mine warfare context, performances of automatic target recognition (ATR) processes are known to highly depend on the underwater environment. In this study, we focus on the detection of mine-like-objects (MLO) in sand ripple seafloors. This particular seafloor type degrades detector performance by decreasing the detection probability and increasing the number of false alarms. To tackle this issue, we propose a method based on the monogenic signal and the concept of intrinsic dimensionality to characterize ripple seafloors, and then identifying geometrical anomalies caused by the presence of a MLO. This environmental information about the ripple field is taken into consideration to automatically create a map in which the potential armful object is set clearly visible to facilitate its detection. To validate and assess the performance of the proposed detection algorithm, a study of real synthetic aperture sonar (SAS) images containing various mine-like targets is undertaken.