**2. Object recognition systems for automated image analysis**

Aerial or satellite survey consists of several steps that need to be followed. Starting from the data acquisition procedures, they include visual observations, the capturing of imagery and the use of metric measurements on the acquired images. Then, this raw information is typically used to produce a set of documents which consists of text and related images. However, the resulting documents are often hard to use since they have been created for a specific application and require expert knowledge to comprehend. In addition, image quality can vary depending on the remote sensing site conditions. Other challenges on image analysis from aerial surveys can include poor lighting conditions, occlusions, and varying depths. Also, limitations on the geospatial information provided with the acquired images can limit their spatial analysis. Restrictions on the field-of-view (FOV) and the image sensor footprint can affect the number of images collected per aerial survey. Then, consequently, a higher number of images require a higher processing time to be completed. Where time of completion is of essence for cost reasons, then someone can expect that automated image analysis tools are necessary for minimising those processing and completion times.

Integral part of such automated image analysis tools plays the object recognition system used which need to be designed in novel architectures, if they are to be applied for solving more complex problems [8]. Recent advances have led to the development of biologically-inspired also known as cognitive architectures [9] of object recognition systems with separate design blocks of a recognition unit and a separate knowledge learning unit [10, 11]. Thus, cognitive architectures need to exploit the non-linearity, the learning and adaptation to the input data, and provide an attentional mechanism for the hybrid system to be able to select certain input information to be included in its learning against other input. Therefore, knowledge representation and learning becomes a central issue in the design and implementation of such hybrid biologically-inspired pattern recognition [12]. Knowledge representation can have altering effects on problem knowledge learning and problem solving [13]. A problem solving system consists of a domain theory which specifies the task to be solved, the initial problem states and the targeted problem goals, and a control knowledge which guides the decision-making process. Thus, knowledge representation can have a direct effect to the efficiency of the problem solving process [14, 15].

## **2.1 Defining the problem**

In this chapter, we describe a new object recognition architecture for improving the speciation of an endangered bird species from aerial surveys. For reasons of confidentiality, we use here the term endangered bird sub-species 1 or, simply, endangered species 1, and the term endangered sub-species 2 or, simply, endangered species 2. In particular, we are looking to improve the accuracy and precision of the recognition of endangered bird sub-species 1and 2 in winter plumage which the task of correctly speciating them becomes harder than in their summer plumage. Our proposed cognitive object recognition architecture incorporates the features of shape, size and colour (3-bands R, G and B) of the endangered sub-species 1 and 2 in the architecture's knowledge representation, and then apply an unsupervised knowledge learning unit for improving the accuracy and precision in recognising them.
