SEMANTIC SEGMENTATION OF MANMADE LANDSCAPE STRUCTURES IN DIGITAL TERRAIN MODELS
| Autor: | B. Kazimi, F. Thiemann, M. Sester |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol IV-2-W7, Pp 87-94 (2019) |
| Druh dokumentu: | article |
| ISSN: | 2194-9042 2194-9050 |
| DOI: | 10.5194/isprs-annals-IV-2-W7-87-2019 |
| Popis: | We explore the use of semantic segmentation in Digital Terrain Models (DTMS) for detecting manmade landscape structures in archaeological sites. DTM data are stored and processed as large matrices of depth 1 as opposed to depth 3 in RGB images. The matrices usually contain continuous real-valued information upper bound of which is not fixed, such as distance or height from a reference surface. This is different from RGB images that contain integer values in a fixed range of 0 to 255. Additionally, RGB images are usually stored in smaller multidimensional matrices, and are more suitable as inputs for a neural network while the large DTMs are necessary to be split into smaller sub-matrices to be used by neural networks. Thus, while the spatial information of pixels in RGB images are important only locally within a single image, for DTM data, they are important locally, within a single sub-matrix processed for neural network, and also globally, in relation to the neighboring sub-matrices. To cope with the two differences, we apply min-max normalization to each input matrix fed to the neural network, and use a slightly modified version of DeepLabv3+ model for semantic segmentation. We show that with the architecture change, and the preprocessing, better results are achieved. |
| Databáze: | Directory of Open Access Journals |
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