Current marine oil spill detection and monitoring methods using high-resolution remote sensing imagery are quite limited. This study presented a new bottom-up and top-down visual saliency model. We used Landsat 8, GF-1, MAMS, HJ-1 oil spill imagery as dataset. A simplified, graph-based visual saliency model was used to extract bottom-up saliency. It could identify the regions with high visual saliency object in the ocean. A spectral similarity match model was used to obtain top-down saliency. It could distinguish oil regions and exclude the other salient interference by spectrums. The regions of interest containing oil spills were integrated using these complementary saliency detection steps. Then, the genetic neural network was used to complete the image classification. These steps increased the speed of analysis. For the test dataset, the average running time of the entire process to detect regions of interest was 204.56 s. During image segmentation, the oil spill was extracted using a genetic neural network. The classification results showed that the method had a low false-alarm rate (high accuracy of 91.42%) and was able to increase the speed of the detection process (fast runtime of 19.88 s). The test image dataset was composed of different types of features over large areas in complicated imaging conditions. The proposed model was proved to be robust in complex sea conditions.