The availability of high-resolution remote sensing (HRRS) data has opened up the possibility for new interesting applications, such as per-pixel classification of individual objects in greater detail. This paper shows how a convolutional neural network (CNN) can be applied to multispectral orthoimagery and a digital surface model (DSM) of a small city for a full, fast and accurate per-pixel classification. The predicted low-level pixel classes are then used to improve the high-level segmentation. Various design choices of the CNN architecture are evaluated and analyzed. The investigated land area is fully manually labeled into five categories (vegetation, ground, roads, buildings and water), and the classification accuracy is compared to other per-pixel classification works on other land areas that have a similar choice of categories. The results of the full classification and segmentation on selected segments of the map show that CNNs are a viable tool for solving both the segmentation and object recognition task for remote sensing data.
Längkvist et al. REF proposed the classification and segmentation of satellite orthoimagery (including five classes: vegetation, ground, road, parking, railroad, building and water) using CNNs in a small city for a full, fast and accurate per-pixel classification.
Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks
Remote Sensing
Remote Sensing
Computer Science Geology