Two-Dimensional Data Conversion for One-Dimensional Adaptive Noise Canceler in Low-Frequency SAR Change Detection

One-dimensional (1-D) adaptive noise canceler (ANC) has been used for false alarm reduction in low-frequency SAR change detection. The paper presents possibilities to process 2-D data by a 1-D ANC. Beside concatenating the rows of 2-D data in a matrix form to convert it to 1-D data in a vector form, two conversion approaches are considered-concatenating the columns of 2-D data and local concatenation, i.e., the conversion to 1-D is performed locally on each block of the 2-D data. A ground object can occupy more than one row and/or more than one column of 2-D data. In addition, the properties in cross range and range of an image are not the same. Thus, different conversion approaches may lead to different performance of an 1-D ANC and hence different change detection results. Among the considered approaches, the local concatenating approach is shown to provide slightly better performance in terms of probability of detection and false alarm rate.