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Mapping buildings
| Content Provider | Scilit |
|---|---|
| Author | Cresson, Rémi |
| Copyright Year | 2020 |
| Description | In this chapter, the authors apply semantic segmentation to map the buildings over the Amsterdam region of interest. They intend to perform the semantic segmentation of buildings, from a Spot-7 image at 1.5m acquired over Amsterdam. The first step is to pre-process the Spot-7 image. Then, the authors prepare the terrain truth that will be used for the training of U-Net model. They introduce a very simple deep network, that inputs a single image. For this reason, the authors fuse the panchromatic channel and the multispectral image into one single image, prior to other processing. They performs the pansharpening of the Spot-7 product, meaning that it generate a multispectral image that has the same resolution as the panchromatic channel, and the same channels as the original multispectral image. The authors aim to select only patches that are fully annotated. Book Name: Deep Learning for Remote Sensing Images with Open Source Software |
| Related Links | https://content.taylorfrancis.com/books/download?dac=C2019-0-09527-7&isbn=9781003020851&doi=10.1201/9781003020851-12&format=pdf |
| Ending Page | 98 |
| Page Count | 16 |
| Starting Page | 83 |
| DOI | 10.1201/9781003020851-12 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2020-07-15 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Deep Learning for Remote Sensing Images with Open Source Software Remote Sensing Segmentation |
| Content Type | Text |
| Resource Type | Chapter |
