ISSN (O) 2278-1021, ISSN (P) 2319-5940 IJARCCE International Journal of Advanced Research in Computer and Communication Engineering ISO 3297:2007 CertifiedImpact Factor 7.918Vol. 11, Issue 10, October 2022 DOI: 10.17148/IJARCCE.2022.111002 © IJARCCE This work is licensed under a Creative Commons Attribution 4.0 International License 15 Land Cover Segmentation of Multispectral Images Using Multiresolution Algorithm Herlawati 1 , Prima Dina Atika 2 , Rahmadya Trias Handayanto 3 Faculty of Computer Science, Universitas Bhayangkara Jakarta Raya, Jakarta, Indonesia 1,2 Computer Engineering Department, Universitas Islam 45, Bekasi, Indonesia 3 Abstract: Semantic segmentation is needed by regional planners to know the composition of land cover in their area, so that they can take the right policy. Several methods from manual to automatic have been researched, both based on colour and pattern. Each method has their strong and weakness, so it is necessary to make the right choice when applying the method. Currently, multispectral imagery is still very rarely used, even though sources of information from the internet are easy to find, i.e. Landsat imagery from the United States Geological Survey. This study uses two methods for segmenting three-channel multispectral images (red, green, and blue), namely iterative self-organizing clustering (ISOCLSUT), which is based on a colour sensor, and a multiresolution algorithm, which is based on colour and pattern. For the experiment, the pre-processed satellite image of Karawang district was segmented using the ISOCLUST as well as multiresolution algorithm. The experimental results show that land cover segmentation with multiresolution algorithm is better than ISOCLUST for RGB but for more than three channels, i.e., seven frequency channels, ISOCLUST shows better performance compared to real image conditions. Keywords: Satellite Imagery, Multispectral, ISOCLUST, Multiresolution I. INTRODUCTION In the Geographic Information System (GIS) there are two land use maps, namely land use and land cover. Land cover describes how a land is covered by a biophysical environment such as buildings, trees, water, and the like, while land use describes how human socio-economic activities on land such as housing, business, industry, schools and the like [3]. A land cover, for example a building, in a land use can be residential, industrial, or business. For land cover, the term classification is usually given the term segmentation. Moreover, in the computer science literature a similar term to classification and segmentation is object detection. In the segmentation process, each pixel of the satellite image will be segmented into several categories such as buildings, vegetation, waters, and other types of land cover. Currently, the main source of land cover data is imagery derived from remote sensing such as satellites, drones, unmanned aerial vehicles (UAVs). Image processing is needed to produce a raw image into a segmented image. There are two types of processing involved: colour pixel based and object/pattern based [2], [3]. Initially, the map was made by direct survey to the area. However, since the development of remote sensing, most maps are drawn using satellite imagery. Applications that are currently developing such as Google Map, WAZE, among others, use Global Positioning Service (GPS) on smartphones connected to these applications. These applications work using existing techniques and methods in the field of Geographic Information Systems (GIS). In other words, remote sensing captures images and represents them with GIS tools. [4]. Digital image processing is needed in managing satellite capture from remote sensing. Satellite images have hyperspectral characteristics, namely one image capture has a number of frequency bands, for example Sentinel-2, and Landsat-9 images have 13 frequency bands [5]. Several remote sensing applications are available that are capable of classifying based on colour differences from satellite imagery. The application works well for land cover but is not able to distinguish buildings that have a specific use (land use), whether settlements, factories, roads, terminals, airports, and others. Dyna- Clue and Idrisi/TerrSet are applications that are often used in land cover classification. Other applications then emerged to perform pattern/object-based land cover segmentation known as Object-Based Image Analysis (OBIA), such as eCognition. For example, OBIA can distinguish between a river and a lake, although they are both water because they have different object shapes.