**1. Introduction**

### **1.1 Mining activity and remote sensing**

Mining comprises the activity that includes the extraction of geological materials from earth with tunnels, shafts or pits. Mining and mines can be classified in several ways. According to materials commonly mined, three classes of mining can be distinguished: metallic, non-metallic and fuel minerals. Based on the nature of excavation, mineral extraction can be categorized into two classes: underground and surface mining. The latter includes open-pit mining (also known as open-cast mining) that is implemented to extract deep and massive deposits that are not covered by a thick overburden. Underground mining of such deposits would be disadvantageous, since the material is mainly close to the surface [1].

Greece has been commonly included in the top lignite producers in Europe [2]. Mining of fuel minerals constitutes a critical activity, since a large percentage of the country's energy needs is covered by a solid fuel, lignite. The first lignite mine in Greece appeared in 1873, whereas systematic exploitation commenced after

1950. Nowadays, the primary lignite extraction basins are located in Ptolemaida and Amyntaio. Lignite exploitation in Greece is conducted by surface mining and specifically open-pit mining [3].

Mining and specifically coal mining activities may cause severe environmental impacts [4]. Landscapes formed by mining activities are vulnerable to several geomorphic hazards, for instance, landslides and rockfalls [1]. The stability of excavations is a critical aspect of Greek lignite mines which become larger and deeper in comparison with those in the past. During the last few years, many events of severe deformations and disastrous slope failures occurred [2]. In addition, flood is a probable hazard, since water can enter pits and tunnels [1]. Surrounding areas are affected by mining with economic, environmental and social impacts [5].

Taking into consideration their synoptic coverage and multitemporal data acquisition capabilities, remote sensing methods have been widely implemented in applications related to mining activities. Availability of high spatial resolution data resulted in an increased interest of using satellite data to monitor surface mining activities [4]. Remote sensing offers a valuable tool for acquiring rigorous data, while decreases the cost of field surveys both in time and money [6].

Remote sensing applications related to mining activities include the following: mapping of the surface mineralogy, topography and related changes that are quite valuable throughout the operation and planning of mine, identifying and monitoring environmental effects and mapping surface movements of mine structures in order to monitor safety features [7]. Furthermore, the size and location of mine areas as well as land-cover changes due to mining can be extracted from satellite images [5]. Remote sensing can make mine planning procedures easier, enhance safety during and after mine operation and monitor environmental effect as well as rehabilitation [7].

#### **1.2 Image segmentation in OBIA**

In contrast with traditional pixel-based approaches, the primary methodological component in Object-Based Image Analysis (OBIA) is the image object [8, 9]. OBIA produces meaningful image objects only if the imagery is partitioned into similar or relatively similar areas. This requires a low value of internal heterogeneity regarding the parameter that is examined in comparison with its adjoining areas [8].

Image segmentation is the first but also fundamental procedure to produce the core elements of OBIA [10]. It is about the partitioning of an image into spatially adjoining and homogenous groups of pixels (segments) that constitute the foundation for further analysis [8, 11]. These regions have similar spatial and spectral features, which, if considered as meaningful, depict a real-world object [9, 12]. By implementing image segmentation, the level of detail is decreased to make image content more comprehensive by lessening image complexity [9]. By transitioning from pixel to image object-based framework, in an effort to follow the example of visual interpretation, better management of spatial information can be accomplished, thus a more beneficial integration with Geographic Information System (GIS) can be achieved [13].

During the last decades, several segmentation methods were matured and employed in remote sensing applications [10]. Commonly, segmentation methods are classified into three broad categories: pixel-based, edge-based and region-based methods [14]. The selection of segmentation method is substantially influenced by the objective of image analysis study and it is typically acknowledged that it does not exist a perfect algorithm that will demonstrate adequate results with every satellite image. It has to be mentioned that most segmentation methods do not instantly produce meaningful image objects. However, clusters are generated with

#### *Delineation of Open-Pit Mining Boundaries on Multispectral Imagery DOI: http://dx.doi.org/10.5772/intechopen.94120*

generic labels, for example, region A, region B, etc. Then, these clusters have to be converted to meaningful image objects through a post-segmentation process [15].

A fairly demanding task in image segmentation procedure is the selection of segmentation parameters' values in order to generate segments that will comply with the needs of user and the purpose of study [10]. Since there is not a commonly accepted method to determine optimal segmentation parameters' values, image segmentation continues to be an interactive procedure that includes trial-and-error approaches.

A typical OBIA approach includes two main steps, image segmentation and object classification. On the other hand, there are studies that propose a methodology that includes only the step of image segmentation. It hast to be noted that the application objective is the definitive factor concerning the methodology implemented. This study does not follow the traditional OBIA approach.
