You can search for data using various spatial parameters. Once your account is created, log in to the website using your credentials. Follow the instructions to create an online account. You may need to acquire imagery from different seasons to capture vegetation phenology and that may require the use of data spanning multiple years. Or perhaps your application requires a very specific time stamp (data from a specific year, season or both), so you may be able to accept some cloud contamination. You may only need a portion of a scene to be cloud-free. This includes the spatial extent, the spatial resolution, temporal resolution and the level of cloud contamination. In this portion of the lab you will search Earth Explorer to identify “suitable” Landsat 8 imagery, download a scene and import it into ERDAS Imagine.Ī suitable image is defined by the user’s needs. Part1: Landsat Image Acquisition, Download and Import There are totally 9 snapshots you need to submit, please name your file as #_your last name (# represents 1-9). Because you only have one chance to submit your work, submit all the results after you finish the whole lab. You need to submit your results to BB this lab. You will also use multiple SPOT images to learn the image mosaic and subset processes and to subset the image to an area of interest. This lab allows you to become familiar with the process of searching for and downloading a Landsat scene, and how to stack layers from the original products. This lab serves as an introduction to downloading remotely sensed data from one, of the several, online sources and includes some common remote sensing image preprocessing functions. Lab - Data Acquisition and Image Preprocessing.Lab 09 - Descriptive Spatial Statistics and Point Pattern Analysis Lab 07 - Siting a New School with Model Builder and Fungus weight Lab 06 - Cost Distance, Region Groups, more Model Builder & Python Lab 01 - Data survey and database building Lab - ELECTROMAGNETIC RADIATION PRINCIPLES Lab - INTRODUCTION TO UNIT CONVERSION & SCALE PROBLEMS Lab - INTRODUCTION TO IMAGE INTERPRETATION Lab - Introduction to ERDAS IMAGINE (Part II) Lab - Data Acquisition and Image Preprocessing Lab 09 - Interpolation and Fire Hazard Modeling Lab 08 - Introduction to network analyist and ArcScene Lab 07 - Overlay & site suitability analysis Lab 04 - On-Screen Digitizing & Image Restoration Lab 03 - Using GPS for Field Data Collection Lab 02 - Projections and Coordinate Systems Lab 13 - Earth Observing Missions Imagery Lab 11 - Remotely Sensed Imagery and Color Composites Lab 03 - Coordinates and Position Measurements Nonetheless, the overall accuracy of building extraction with respect to area was found to be 85.38% in a set of 66 buildings, 73.81% in a set of 94 buildings and 70.64% in a set of 102 buildings.Lab 02 - Introduction to Google Earth Pro The branching factor, miss factor, building detection percentage and quality percentage were also calculated for accuracy assessment. Some patches of road and ground are also extracted as buildings. For the other two satellite images, the overall accuracy is low as compared to the first satellite image. Only one patch of road is extracted as a building. For one satellite image it has picked up all the buildings with a slight change in the area of footprints of buildings. The extracted buildings are compared with the manually digitized buildings. The approach is applied on three different satellite images. Imagine Objective tool of ERDAS 2011 has been used. The cleanup methods are applied to smoothen the extracted buildings and also to increase the accuracy of extraction of buildings. After converting the raster image into vector image, the building objects are extracted on the basis of area. After filtering the segments, the output raster image is converted into vector image. Then different filters are applied on the image to remove the objects which are not of our interest. After that, the high resolution image is segmented by using the split and merge segmentation so that the pixels that are grouped as raster objects have probability attributes. Firstly, Single Feature Classification is applied on the high resolution satellite image. In this paper, an object oriented approach for automatic building extraction from high resolution satellite image is developed.
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