Structure extraction in urbanized aerial images from a single view using a CNN-based approach

Osuna-Coutiño, J. A. de Jesús; Martínez-Carranza, José

doi:10.1080/01431161.2020.1767821

Cited by 9 publications

(4 citation statements)

References 32 publications

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“…Rojas-Perez et al 44 implemented CNNs for automatic detection zone for UAS in urban environments with a public dataset and synthetic data. In a further work, Osuna-Coutiño and Martinez-Carranza 45 extended the use of the CNN-based approach processing a single image seeking to interpret areas where the human-made structures are observed. Lopez-Campos and Martinez-Carranza 46 advanced the synthetic data application by generating photogrammetric aerial-images from photo-realistic scenes.…”

Section: Background and Related Workmentioning

confidence: 99%

Analyzing visual imagery for emergency drone landing on unknown environments

Bektash,

Naundrup,

la Cour-Harbo

2022

International Journal of Micro Air Vehicles

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Autonomous landing is a fundamental aspect of drone operations which is being focused upon by the industry, with ever-increasing demands on safety. As the drones are likely to become indispensable vehicles in near future, they are expected to succeed in automatically recognizing a landing spot from the nearby points, maneuvering toward it, and ultimately, performing a safe landing. Accordingly, this paper investigates the idea of vision-based location detection on the ground for an automated emergency response system which can continuously monitor the environment and spot safe places when needed. A convolutional neural network which learns from image-based feature representation at multiple scales is introduced. The model takes the ground images, assign significance to various aspects in them and recognize the landing spots. The results provided support for the model, with accurate classification of ground image according to their visual content. They also demonstrate the feasibility of computationally inexpensive implementation of the model on a small computer that can be easily embedded on a drone.

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Section: Background and Related Workmentioning

confidence: 99%

Analyzing visual imagery for emergency drone landing on unknown environments

Bektash,

Naundrup,

la Cour-Harbo

2022

International Journal of Micro Air Vehicles

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“…In [39], a novel deep learning framework is developed to retrieve similar architectural floor plan layouts from a repository, analyzing the effect of individual deep convolutional neural network layers for the floor plan retrieval task. In [40] the results of a novel method for building structure extraction in urbanized aerial images are presented. Most of the methods are based on CNN.…”

Section: Related Workmentioning

confidence: 99%

Classification of Geometric Forms in Mosaics Using Deep Neural Network

et al. 2021

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The paper addresses an image processing problem in the field of fine arts. In particular, a deep learning-based technique to classify geometric forms of artworks, such as paintings and mosaics, is presented. We proposed and tested a convolutional neural network (CNN)-based framework that autonomously quantifies the feature map and classifies it. Convolution, pooling and dense layers are three distinct categories of levels that generate attributes from the dataset images by introducing certain specified filters. As a case study, a Roman mosaic is considered, which is digitally reconstructed by close-range photogrammetry based on standard photos. During the digital transformation from a 2D perspective view of the mosaic into an orthophoto, each photo is rectified (i.e., it is an orthogonal projection of the real photo on the plane of the mosaic). Image samples of the geometric forms, e.g., triangles, squares, circles, octagons and leaves, even if they are partially deformed, were extracted from both the original and the rectified photos and originated the dataset for testing the CNN-based approach. The proposed method has proved to be robust enough to analyze the mosaic geometric forms, with an accuracy higher than 97%. Furthermore, the performance of the proposed method was compared with standard deep learning frameworks. Due to the promising results, this method can be applied to many other pattern identification problems related to artworks.

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“…In recent years, deep learning technology has made breakthrough progress in the field of computer vision [23]. In particular, convolutional neural networks (CNNs) can automatically extract high-level features from image patches through a series of convolutional and pooling layers and have demonstrated excellent representation and classification capabilities for object shape, texture, and context information [24,25]. These methods thus avoid the tedious and time-consuming hand-crafted feature engineering required in traditional remote sensing image analysis methods [26].…”

Section: Introductionmentioning

confidence: 99%

Object-Based Semi-Supervised Spatial Attention Residual UNet for Urban High-Resolution Remote Sensing Image Classification

Lu,

Li,

Zhang

et al. 2024

Remote Sensing

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Accurate urban land cover information is crucial for effective urban planning and management. While convolutional neural networks (CNNs) demonstrate superior feature learning and prediction capabilities using image-level annotations, the inherent mixed-category nature of input image patches leads to classification errors along object boundaries. Fully convolutional neural networks (FCNs) excel at pixel-wise fine segmentation, making them less susceptible to heterogeneous content, but they require fully annotated dense image patches, which may not be readily available in real-world scenarios. This paper proposes an object-based semi-supervised spatial attention residual UNet (OS-ARU) model. First, multiscale segmentation is performed to obtain segments from a remote sensing image, and segments containing sample points are assigned the categories of the corresponding points, which are used to train the model. Then, the trained model predicts class probabilities for all segments. Each unlabeled segment’s probability distribution is compared against those of labeled segments for similarity matching under a threshold constraint. Through label propagation, pseudo-labels are assigned to unlabeled segments exhibiting high similarity to labeled ones. Finally, the model is retrained using the augmented training set incorporating the pseudo-labeled segments. Comprehensive experiments on aerial image benchmarks for Vaihingen and Potsdam demonstrate that the proposed OS-ARU achieves higher classification accuracy than state-of-the-art models, including OCNN, 2OCNN, and standard OS-U, reaching an overall accuracy (OA) of 87.83% and 86.71%, respectively. The performance improvements over the baseline methods are statistically significant according to the Wilcoxon Signed-Rank Test. Despite using significantly fewer sparse annotations, this semi-supervised approach still achieves comparable accuracy to the same model under full supervision. The proposed method thus makes a step forward in substantially alleviating the heavy sampling burden of FCNs (densely sampled deep learning models) to effectively handle the complex issue of land cover information identification and classification.

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Structure extraction in urbanized aerial images from a single view using a CNN-based approach

Cited by 9 publications

References 32 publications

Analyzing visual imagery for emergency drone landing on unknown environments

Analyzing visual imagery for emergency drone landing on unknown environments

Classification of Geometric Forms in Mosaics Using Deep Neural Network

Object-Based Semi-Supervised Spatial Attention Residual UNet for Urban High-Resolution Remote Sensing Image Classification

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