**1. Introduction**

Cultural heritage consists of all the tangible and intangible elements from monuments and cultural traditions to natural landscape, including all the extinct or current biological species. Cultural heritage and specifically tourism activities related to cultural heritage contribute to economic growth, regeneration, education and tourism [1, 2]. A previous report [3] published by the HLF and Visit Britain revealed that the heritage tourism is a £12.4 billion a year industry. £7.3 billion of heritage expenditure is based on visits to built heritage attractions and museums, with the overall £12.4 billion including visits to parks and countryside as well. In a recent article, it was reported that to reduce the risk of extinction for all the threatened species worldwide would cost annually approximately £2.97bn, with an additional £47.4bn required per

year to establish and manage protected areas for species known to be at risk from habitat loss, hunting and other human activities [4].

Remote sensing has become one of the main technologies nowadays used for protecting cultural heritage due to its non-invasiveness [5]. Thus, remote sensing has been used in many cases for the conservation analysis of monuments, archaeological site detection and risk protection, together with the protection of natural landscapes consisting of living species. Aerial photography is one of the forms of modern remote sensing. It has been applied in many application areas [6]. For example, aerial photography has been successfully used in locating ancient civilisation structures amongst thick jungle vegetation [7].

One of the key tools in remote sensing image analysis is the object recognition. Advanced machine learning (ML) and artificial intelligence (AI) algorithms can be used for detecting and classifying different classes of objects in different applications, such as environmental monitoring, geological hazard detection, land-use/land-cover (LULC) mapping, geographic information systems (GIS), precision agriculture and urban planning. Still, object recognition in remote sensing images (RSIs) can be very challenging due to the large variations in the visual appearance of objects caused by camera viewpoint variations, occlusion, background clutter, and illumination changes. Thus, in low spatial resolution satellite images such as Landsat the recognition of objects becomes even harder. Therefore, higher resolution satellite images such as IKONOS or Quickbird are preferred since provide researchers and image analysts with more detailed spatial and textural information. In effect, a greater range of object categories can be recognised due to the increased sub-meter resolution.

Nowadays the technological advancements in satellite and aerial RSIs have offered us opportunities for new applications in many image analysis areas. Pixels in an image can be grouped and clustered into regions of interest (ROIs) where then object recognition algorithms can be applied for classifying a range of categories of objects. Supervised and unsupervised such classifiers can be utilised depending on the application. Supervised classification require larger amount of data than unsupervised with manual annotation and labelling of a bounding box which contains the true class object for training purposes. However, such manual annotation suffers from scaling up issues when a very large amount of such data is needed. Moreover, it becomes a significantly difficult task either when the ROIs consist of a cluster of a few pixels in an RSI or the objects are occluded, camouflaged and may include complex textures.

Thus, object recognition algorithms become of great importance in applications of RSIs. Extracting the features of an object either through manually labelling them or through a classifier algorithm becomes essential when aiming to recognise them in image analysis applications. However, when dealing with large or very large (big) data, then this task turns complex with high computational costs for processing those extracted feature vectors. The manual labelling is time consuming and unreliable since duplicates or redundant features are often created. Here, we will be focusing more on automated feature extraction through a classifier algorithm for large or very large data.

In Section 2, we discuss the object recognition systems and their design characteristics. In Section 3, we explain the k-means algorithm. Section 4 discusses the optical correlator classifiers. In Section 5, our cognitive object recognition architecture is described. Then, in Section 6 we discuss the results recorded when applying our cognitive architecture. Section 7 contains the Conclusions and future work.

*A Cognitive Digital-Optical Architecture for Object Recognition Applications in Remote… DOI: http://dx.doi.org/10.5772/intechopen.109028*
