Ship detection and classification is very important for port and coastal security. Due to maritime safety and traffic control, highresolution images of ships should be obtained. High resolution color remote sensing ship images taken from short distances provide advantages in ship detection applications. But the analysis of these high-dimensional images is complicated and requires long time. Dividing the image data into smaller blocks and representing them with a vector with distinctive and independent features facilitates the analysis process. For this reason, a block division method is applied first, dividing the image data into small pixel blocks. These obtained image blocks are also represented by the hybrid feature vectors. These feature vectors are created by adding the sub-features extracted from the color and texture properties of the images one after another. Using the obtained hybrid vectors, the images are classified using machine learning methods on Apache Spark. Classification studies were realized using Naive Bayes, Decision Trees and Random Forest methods in the MLlib. The analysis of the images was realized much faster with the clustering architecture created on Apache Spark platform. According to the obtained classification results, 99.62% classification success was achieved by using Random Forest method. In addition, an average of 3.4 times acceleration was achieved by running each method on 1 master + 4 workers clustering architecture on Spark.