NIR - Landsat 7 Band

Next Article

NBR

In this research, we include 10 sets of indices including 6 Landsat bands. Though some work used indices for specific class classification [17,18,20]. We have combined specific indices relevant to each class for multiclass classification. It is seen that fusion of these multiple indices shown in table III, with basic spectral bands shows impressive result. The proposed architecture can handle input data with no-data pixels. It is seen that the proposed technique outperforms many states of art algorithms, such as SVM and KNN.

III. STUDY AREA

Guwahati is the main hub and metropolis in Assam and Northeast India. It can be called the heart of the North-Eastern region. It comes under the jurisdiction of the Guwahati Metropolitan Development Authority (GMDA). The total area of it is around 216 Sq. Km. The approximated population density according to the 2011 census is about 1 million and was estimated togrowmillion by2025 [7]. The cityis bounded on the northern side by the great river Brahmaputra, and on the southern side by hill rocks that are extensions of the Khasi highlands. The Rani Reserve Forest, Deepor Beel wetland, and alluvial tracts of the Brahmaputra plain are located in the west and southwest. Many endangered birds and rare animals as Asian elephants, pythons, and one-horned rhino can be found in the Guwahati region. The cityhas been divided byGuwahati Municipal Corporation into five divisions within which twenty zones and sixty wards are demarcated for better service. The Guwahati city map with true color is shown in Figure 2. The map is created by overlapping administrative boundary vector data collected from OpenStreetMap on Landsat 7 satellite image.

IV. MOTIVATION AND METHODOLOGY

For decades it is seen that changes in particular land cover classes are always impacted or come under the influence of either naturally occurring factors or man-made factors over time. Naturally occurring factors like climate change, sudden earthquakes, landslides, increasing flood, water source, soil erosion is a major river, etc., and man-made factors like industries setup, infrastructure setup, communication channel, population, illegal settlements, etc., [22]. Also, the same LC class is influenced by its surrounding classes over time [10]. Some indirect factors are also responsible for the changes like weak society economy, improper planning, sports and communication disruption, monitoring problem, etc. Due to these naturally occurring, man-made, and indirect factors, the area is affected by many challenges, especially in NE states. As NE is surrounded by rivers and mountains, it becomes very challenging to proper field study and faces monitoring problems. Due to these challenges, NE is facing some issues like increasing population, destruction of natural habitat, illegal settlements, ecosystem disruption, etc. Guwahati city is one of the prominent hubs of State Assam in the NE region because of state economic, industrial prospect, main communication gateway for other NE states, heritage like Maa Kamakhya temple and Ramdesir site Deepor Beel. So, for the prospect of urbanization and to continue to maintain the existence of ecologically sensitive areas, it is important to do proper assessment and planning for better development of the city. Many kinds of literature have found remote sensing and GIS techniques as an adequate method of LULC detection, classification, and analysis [1, 3, 6, 9]. So, we have considered developing a model based on deep learning which could be able to classify and predict different land cover classes. The architecture of the proposed methodology is shown in Figure 1. The proposed methodology is composed of mainly three sections as described below.

1 ) Preprocessing of satellite images 2 ) Model Training 3 ) Model Testing

A. Preprocessing of Satellite Images Preprocessing of satellite data plays an important role in the analysis of final output. In this study, the preprocessing of Landsat imagery involves the following steps. 1) Creation of Guwahati City Boundary Vector File: As for downloading and extracting the raster data of the study area, we need to specify the extent of the target location. So, we have extracted the polygon of the administrative boundary of Guwahati city from the OSM layer [23]. This extracted polygon is saved as a vector file Vs for further use. The algorithm for the creation of the boundary vector file is given in Algorithm 1