Objects imaged in synthetic aperture acoustic data have a unique appearance. Due to this, we propose that examining the texture between targets and non-targets will prove more descriptive and improve classification performance. A few common texture feature extraction methods are those derived from grey-level co-occurrence matrix (GLCM), local binary patterns (LBP), and local directional patterns (LDP). LDP uses a set of filters to measure the local directional response around each pixel and then builds a binary code like LBP. The feature vector is a histogram of those binary codes. However, the set of filters used may not be the optimal set needed to achieve the best classification accuracy and a binary coding may not be the best aggregation method. In this paper, we apply known sets of two-dimensional filters, not necessarily directional, as well as develop a new approach to aggregation. Different filter sets provide the algorithm with a broader description beyond the direction of edges and thus better representation of texture. A more complex aggregation method allows more information retention in the feature vector. These modifications, to the existing LDP algorithm, will allow classifiers to more accurately distinguish between the textures of targets and non-targets. A support vector machine (SVM) helps evaluate the performance of the new feature extraction method and compare its performance to other common extraction methods on data collected at a US Army test site. This will be used to build an online classifier system for testing on lane-based data.