Deep learning for geometric and semantic tasks in photogrammetry and remote sensing

Heipke, Christian; Rottensteiner, Franz

doi:10.1080/10095020.2020.1718003

Cited by 52 publications

(30 citation statements)

References 35 publications

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“…Semantic analysis firstly of 2D and more recently of 3D scenes has become an increasingly studied topic in various applications, such as photogrammetry, remote sensing, computer vision and robotics (Heipke and Rottensteiner, 2020). Input data might be images, point clouds or textured meshes.…”

Section: Related Workmentioning

confidence: 99%

Semantic Segmentation for Building Façade 3d Point Cloud From 2d Orthophoto Images Using Transfer Learning

Murtiyoso

Lhenry

Landes

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The task of semantic segmentation is an important one in the context of 3D building modelling. Indeed, developments in 3D generation techniques have rendered the point cloud ubiquitous. However pure data acquisition only captures geometric information and semantic classification remains to be performed, often manually, in order to give a tangible sense to the 3D data. Recently progress in computing power also opened the way for massive application of deep learning methods, including for semantic segmentation purposes. Although well established in the processing of 2D images, deep learning solutions remain an open question for 3D data. In this study, we aim to benefit from the vastly more developed 2D semantic segmentation by performing transfer learning on a photogrammetric orthoimage. The neural network was trained using labelled and rectified images of building façades. Another programme was then written to permit the passage between 2D orthoimage and 3D point cloud. Results show that the approach worked well and presents an alternative to help the automation process for point cloud semantic segmentation, at least in the case of photogrammetric data.

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Section: Related Workmentioning

confidence: 99%

Semantic Segmentation for Building Façade 3d Point Cloud From 2d Orthophoto Images Using Transfer Learning

Murtiyoso

Lhenry

Landes

et al. 2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…The main principle of such methods is to map an input image to a score map in which the value (score) for each pixel can be interpreted as the probability of being a distinctive feature. The parameters of the mapping functions used in this process are determined from training data by machine learning techniques, widely used in photogrammetry and remote sensing today (Heipke and Rottensteiner 2020).…”

Section: Detectors Based On Machine Learningmentioning

confidence: 99%

Feature detection and description for image matching: from hand-crafted design to deep learning

Chen

Rottensteiner

Heipke

2020

Geo-spatial Information Science

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“…Machine learning methods have played a more effective role in cloud detection than the images with high spatial resolution, while the conventional methods have no acceptable accuracy. Using deep learning methods (in this study, deep convolutional neural networks are considered), which are among the complete subsets of machine learning methods, has been highly regarded in remote sensing image processing [ 36 , 37 ]. One of the fundamental needs of deep learning methods is the need for big data [ 38 , 39 ].…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Method for Near-Real-Time Cloud and Cloud Shadow Segmentation from Gaofen-1 Images

Khoshboresh-Masouleh

Shah-Hosseini

2020

Computational Intelligence and Neuroscience

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In this study, an essential application of remote sensing using deep learning functionality is presented. Gaofen-1 satellite mission, developed by the China National Space Administration (CNSA) for the civilian high-definition Earth observation satellite program, provides near-real-time observations for geographical mapping, environment surveying, and climate change monitoring. Cloud and cloud shadow segmentation are a crucial element to enable automatic near-real-time processing of Gaofen-1 images, and therefore, their performances must be accurately validated. In this paper, a robust multiscale segmentation method based on deep learning is proposed to improve the efficiency and effectiveness of cloud and cloud shadow segmentation from Gaofen-1 images. The proposed method first implements feature map based on the spectral-spatial features from residual convolutional layers and the cloud/cloud shadow footprints extraction based on a novel loss function to generate the final footprints. The experimental results using Gaofen-1 images demonstrate the more reasonable accuracy and efficient computational cost achievement of the proposed method compared to the cloud and cloud shadow segmentation performance of two existing state-of-the-art methods.

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Deep learning for geometric and semantic tasks in photogrammetry and remote sensing

Cited by 52 publications

References 35 publications

Semantic Segmentation for Building Façade 3d Point Cloud From 2d Orthophoto Images Using Transfer Learning

Semantic Segmentation for Building Façade 3d Point Cloud From 2d Orthophoto Images Using Transfer Learning

Feature detection and description for image matching: from hand-crafted design to deep learning

A Deep Learning Method for Near-Real-Time Cloud and Cloud Shadow Segmentation from Gaofen-1 Images

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