High-frequency waves (3-30 MHz) interact with the sea surface. Thus, a high-frequency surface wave radar (HFSWR) is well suited to perform remote sensing of the sea. The HFSWR coverage range is not limited by the radio horizon: it is possible to keep watch over the sea up to a few hundred kilometers from the coastline. Oceanographic parameters (i.e., wave height, surface current velocity, wind direction, and wind velocity) are derived from the so-called sea spectrum. Moreover, the HFSWR can be used for maritime surveillance of the Exclusive Economic Zone. In that case, the sea spectrum is an unwanted signal because it can mask targets. Sea spectrum extraction is an important issue for HFSWR signal processing since it is a key point for the remote sensing accuracy and the target detection features. In this letter, we show how wavelets may be applied to improve the remote sensing of oceanographic parameters and the target detection using wavelet-based sea clutter extraction. The results obtained using real data with opportune targets validate our approach.