Local feature descriptor plays a significant role in texture classification. However, in the traditional local binary pattern method, image pixels are converted into a binary pattern based on the relationship between center and neighborhood pixels. This paper introduces a novel feature extraction method named local neighbourhood edge responsive binary pattern to extract and categorize reliable texture features from images for achieving an automated classification of different types of ocean bottom sediments. Initially, the basic magnitude variance pixel values are derived depending on an odd and even pixel value of a 3 × 3 image patch. Further, the edge information is extracted using the local directional pattern method from all images. The edge response of the image is obtained using a kirsch mask in all eight directions. The encoding condition is then applied to both the local intensity and the edge information to create a unique descriptor value. Finally, a new learning algorithm called GMJAYA-ELM combines the Gaussian mutated JAYA (GMJAYA) with an extreme learning machine (ELM) for texture classification. The GMJAYA is used to optimize the input weights and hidden biases of single-hidden-layer feed-forward neural networks. Experimental findings indicate that the suggested method yields better accuracy and sensitivity efficiency among various groups. The proposed algorithm is tested by contrasting outcomes with standard learning schemes such as PSO-ELM, GA-ELM, ABC-ELM, Birdswarm-ELM, and JAYA-ELM, suggesting the dominance of GMJAYA-ELM.