**Abstract**

From coastal landscapes to biodiversity remote sensing can on the one hand capture all the natural heritage elements and on the other hand can help in maintaining protected species. In a typical remote sensing application, a few thousands of super high-resolution images are captured and need to be processed. The next step of the processing involves converting those images to an appropriate format for visual display of the data. Then, the image analyst needs to define the regions of interests (ROIs) in each captured image. Next, ROIs need to be defined for identifying specific objects or extracting the required information. First drawback of this processing cycle is the use of image analysis tools which provide them only with scaling or zooming features. Second, there is no conceptual connection between the image analysis tools and the actual processing cycle. Third, such existing tools do not usually automate any steps in the processing cycle. We combine an optical correlator with a supervised or an unsupervised classifier learning algorithm and show how our proposed novel cognitive architecture is conceptually connected with the image analysis processing cycle. We test the architecture with captured images and describe how it can automate the processing cycle.

**Keywords:** object recognition, cognitive digital-optical architecture, image analysis, knowledge representation and learning, remote sensing
