# Land Cover Classification Using a KOMPSAT-3A Multi-Spectral Satellite Image
## 1 Introduction  
Advances in science and technology have enabled many applications for decision making and problem solving applications. Remotes Sensing is one such rapidly advancing space technology that has been providing significant solutions in the areas of natural resource management and environmental assessments. With the availability of remotely sensed data from different sensors of various platform with a wide range of spatiotemporal, radiometric and spectral resolutions, the technology has been applied in a variety of sectors such as agriculture, forestry, geology, hydrology, land use mapping, oceanography and urban development and planning [1]. In recent years, new sets of satellite sensors are being frequently added annually to the constellation of remote sensing satellites. They offer improved specification to collect imagery on-demand over specific locations and for specific purposes. Recent high resolution satellite images are now competing with images obtained through aerial photography particularly due to the highly enhanced spatial resolution which enables them to be applied to cartography. For furtherinformation, [PERSON] et al. described space-based earth observation activities in South Korea [2]. The Korea Multi-Purpose Satellite (KOMPSAT) series of satellites is a multi-purposed satellite system developed by Korea Aerospace Research Institute (KARI). The recent satellite of KOMPSAT series, KOMPSAT-3A is a sister spacecraft of KOMPSAT 3 (Arirang 3) and is Korea's first earth observation/infrared satellite with two imaging systems on board. It is equipped with two imaging payloads; the Advanced Earth Imaging Sensor System A (AEISS-A) and an Infrared Imaging payload. It is now demonstrating high resolution panchromatic and multi-spectral imagery in near infrared (NIR), Red (R), Blue (B) and Green (G) bands for Geographical Information Systems (GIS) applications in environmental, agricultural and oceanographic sciences as well as natural disasters [3]. Land cover is an essential variable that impacts and connects various aspects of human and physical environments, and is critical for the study of ecosystems, climate change, and biodiversity. Land cover mapping is one of the core applications of remote sensing technology [4], which provides a map-like representation of the Earth's surface that is spatially continuous and highly consistent [5]. Accurate and up-to-date land cover maps provide better visualization of the environment and help in planning, modeling and decision-making processes in natural resource management. Traditionally, the land cover mapping was done by manual stereoscopic drawing, which has been overcome by digital methods in recent years. In the recent digital world, land cover mapping of multispectral data can also be done by manual digitizing on a computer screen. As manual digitizing takes much time and cost, the classification is usually carried out by implementing algorithms in computers. Various classification techniques that have been developed in recent decades can be categorized into three broad classes: unsupervised, supervised and object based classification. In unsupervised classification, methods such as K-Means and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) clusters are generated and classes were assigned later to those clusters [6]. In the case of supervised methods such as Mahalanobis Distance (MahD), Minimum Distance (MinD), Maximum Likelihood (ML), Decision Tree (DT), Neural Network (NN) and Support Vector Machine (SVM) training areas are selected based on expert knowledge and field work and classification is performed based on the generated signature file of the training data [7]. A little more advanced than supervised methods are object-based classification methods which first perform multiresolution segmentation, and then classification is performed based on conditional statistics of training segments. In order to effectively derive reliable information from satellite data, appropriate classification techniques are essential.