Abstract
Most object detection, recognition, and classification are performed using optical imagery. Images are unable to fully represent the real-world due to the limited range of the visible light spectrum reflected light from the surfaces of the objects. In this regard, physical and geometrical information from other data sources would compensate for the limitation of the optical imagery and bring a synergistic effect for training deep learning (DL) models. In this paper, we propose to classify terrain features using convolutional neural network (CNN) based SegNet model by utilizing 3D geospatial data including infrared (IR) orthoimages, digital surface model (DSM), and derived information. The slope, aspect, and shaded relief images (SRIs) were derived from the DSM and were used as training data for the DL model. The experiments were carried out using the Vaihingen and Potsdam dataset provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) through the International Society for Photogrammetry and Remote Sensing (ISPRS). The dataset includes IR orthoimages, DSM, airborne LiDAR data, and label data. The motivation of utilizing 3D data and derived information for training the DL model is that real-world objects are 3D features. The experimental results demonstrate that the proposed approach of utilizing and integrating various informative feature data could improve the performance of the DL for semantic segmentation. In particular, the accuracy of building classification is higher compared with other natural objects because derived information could provide geometric characteristics. Intersection-of-union (IoU) of the buildings for the test data and the new unseen data with combining all derived data were 84.90% and 52.45%, respectively.
Highlights
The field of deep learning (DL) has grown significantly over the past decade coupled with rapid improvements in computer performance
Another issue of the normalized DSM (nDSM) is for digital surface model (DSM) obtained from terrestrial light detection and ranging (LiDAR) data or images of the street scene
We propose utilizing various data for training the DL model to obtain reliable results
Summary
The field of DL has grown significantly over the past decade coupled with rapid improvements in computer performance. Images obtained from optical sensors are formed by recording reflected light, in the visible spectral range, from the surfaces of the terrain objects In this aspect, it is not sufficient to reveal real-world features by utilizing image alone. Combining multisource data (e.g., optical and multispectral imagery, point cloud data, and DSM) with derived information from original raw data (e.g., NDVI, cooccurrence features, and surface orientation) provide more reliable results. The main intent of this paper is to classify terrain objects by training the DL model using multisource data; optical IR images, DSM, and DSM-derived data including slope, aspect, and multidirectional SRIs. The experiments were carried out as follows: (1) training with each type of data independently, and (2) training with combining all data. Training by combining multisource data could provide a synergistic effect and multidirectional SRI plays an important role
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