Evaluating DCNN architecturesfor multinomial area classicationusing satellite data
Abstract: The most common approach to analysing satellite imagery is building or object segmentation,which expects an algorithm to find and segment objects with specific boundaries thatare present in the satellite imagery. The company Vricon takes satellite imagery analysisfurther with the goal of reproducing the entire world into a 3D mesh. This 3D reconstructionis performed by a set of complex algorithms excelling in different object reconstructionswhich need sufficient labeling in the original 2D satellite imagery to ensure validtransformations. Vricon believes that the labeling of areas can be used to improve the algorithmselection process further. Therefore, the company wants to investigate if multinomiallarge area classification can be performed successfully using the satellite image data availableat the company. To enable this type of classification, the company’s gold-standarddataset containing labeled objects such as individual buildings, single trees, roads amongothers, has been transformed into an large area gold-standard dataset in an unsupervisedmanner. This dataset was later used to evaluate large area classification using several stateof-the-art Deep Convolutional Neural Network (DCNN) semantic segmentation architectureson both RGB as well as RGB and Digital Surface Model (DSM) height data. Theresults yield close to 63% mIoU and close to 80% pixel accuracy on validation data withoutusing the DSM height data in the process. This thesis additionally contributes with a novelapproach for large area gold-standard creation from existing object labeled datasets.
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