SWIR1 - Landsat 7 Band

Next Article

AWEISH

2) Data Source: Satellite data can be collected from various web sources namely, USGS [24, 25], etc. We have collected multispectral Landsat imagery of the Guwahati city location for the year 2011. The imagery is freely available at this website address http://www.earthexplorer.usgs.gov. For the year 2011, we have used Landsat 7 satellite images. It comprises multispectral sensors that collect 6 spectral bands viz, Blue, Green, Red, NIR, SWIR1, SWIR2. All the bands have t h e same spatial resolution of 30 meters but with different spectral properties. The maps generated are projected in the WGS84

UTM 46N projection. To download the Landsat tile containing the study area, we have used the vector file Vs in USGS. It is seen from the footprint of tiles that in the NE region we can find images with very less cloud cover from January to April.

So, the tiles are collected between January to April do not contain any cloud cover on the study boundary. The complete

Guwahati administrative boundary fits into a single tile. The metadata information of the tiles downloaded is given in Table

II. 3) Image Correction: It generally involves the atmospheric correction of overall im- ages. Moreover, radiometric calibration and corrections [4] are important for classification. Landsat 8 has a higher radiometric and spectral resolution. So, to keep it simple we have selected Landsat 7 images for our research [26]. The raster data of the target area fetched with 0% cloud cover is referred to in Table II, so we could infer that no effect of cloud on the raster data of the study area. Many researchers have suggested the use of Semi-Automatic Classification to perform DOS1 atmospheric correction and conversion of DN number to the reflectance of Landsat images [27]. To keep it simple we have set the "No Data" pixels to value. And also, we have considered the "No Data" value as a classification label and feed to the deep learning algorithm. The algorithm for image correction is given in Algorithm 2. 4) Clipping of Study Area: The corrected raster contains pixels outside the study area boundary. For processing, the unwanted pixel is an extra overhead. So, we clipped the exact study area raster extent by overlapping the vector shapefile Vs of

Guwahati city that is generated earlier on the corrected raster. The clipped raster is then ready for further processing. The algorithm for clipping the GM raster based on GM boundaries is given in Algorithm 3 5) Generating Features for Model Training: In this study, we have considered both spectral and spatial characteristics of satellite images as features. The spectral indices like NDVI, NDWI, etc. were explored from the aspect of water body cover(W), forest cover(F), vegetation cover(V), and built-up areas (BU) using Landsat data. So, it is assumed that the indices will help in enhancing the accuracy of the influence of LC classes during generating model. All the indices for each raster data are computed and stacked together along with raster data. All the bands that are used in the calculation of index values have the same spatial resolution. Every indices have limitations. Like NDVI is highly sensitive to the brightness of soil and atmospheric effect, so we have considered some other indices, viz, MSAVI, EVI, ARVI, and NBR along with NDVI for more accurate classification of vegetation. Similarly, for identification of water features, water index like the NDWI (normalized difference water index), MNDWI (modified NDWI) [28, 29, 30, 31, 32] are considered. Built-up index enhances the spectral characteristics of built-up areas [33]. All the bands viz. blue, green, red, NIR, SWIR1 and SWIR2 of 2011 is than stacked with the generated indices map ARVI, AWEIsh, BI, EVI, MNDW2, MSAVI, NBR2, NBR, NDVI, NDWI in one file Rraster. The bands and indices used in this literature are shown in Table III. 6) Label Creation for Supervised Learning: The labels for training are generated by using an unsupervised learning approach. Kmeans clustering and Iterative Self Organizing Data Analysis Technique (ISODATA) are two algorithms that can be used for the cluster creation in unsupervised classification [39]. We have used the ISODATA algorithm to find 5 different clusters. The signature is seeded from band values of Landsat 7 satellite data. The minimum distance between the signature i s u s e d w i t h a distance threshold value of 0.01 and a maximum standard deviation of 0.02 for finding the 5 different clusters.

For verifying the identified and mapped landscapes class with actual raster data, we have used high resolution google earth images. The pixels of cluster corresponding to vegetation area in google map is labeled as V for vegetation, the cluster pixels corresponding to water bodies in google map is marked W for water bodies. Similarly, built-ups as B and land as S. The no data is treated as label as D. Finally, we got a labelled map Rlabel of timeline 2011 with five different class pixels (V, W, B, S, and

D) based on signature similarity and dissimilarity. This labelled map generated is also verified by masking it with the LULC thematic map downloaded from the website https://bhuvan-app1.nrsc.gov.in/thematic/thematic/index.php [25].