Automatic building change detection through adaptive local textural features and sequential background removal

Sidike, Paheding; Prince, Daniel; Essa, Almabrok; Asari, Vijayan K.

doi:10.1109/igarss.2016.7729738

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…However, such methods are susceptible to interference from vegetation-covered areas or roofs. Several solutions have been proposed to improve the method's robustness, including principal component analysis (Deng et al, 2008), texture features (Sidike et al, 2016;Sofina and Ehlers, 2016), and shape analysis (Abdessetar and Zhong, 2017). However, these methods cannot overcome the ambiguities caused by ground-object interference.…”

Section: Related Workmentioning

confidence: 99%

“…Most existing building-verification methods only consider the 2D outlines of buildings, commonly using remote-sensing images to extract roof top information (Hussain et al, 2013;Hong et al, 2019). For example, the normalized difference vegetation index was used to extract the roof areas (Rottensteiner, 2007;Singh et al, 2012), which is inevitably disturbed by high-rise vegetation near buildings; feature engineering or learned representations are also common approaches to identify roof areas (Sidike et al, 2016;Sofina and Ehlers, 2016), which is hard to overcome confusing textures in urban environment using only the nadir views, such as pavements, bridge decks and streets.…”

Section: Introductionmentioning

confidence: 99%

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Depth-Enhanced Feature Pyramid Network for Occlusion-Aware Verification of Buildings from Oblique Images

Zhu,

Huang,

et al. 2020

Preprint

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Detecting the changes of buildings in urban environments is essential. Existing methods that use only nadir images suffer from severe problems of ambiguous features and occlusions between buildings and other regions. Furthermore, buildings in urban environments vary significantly in scale, which leads to performance issues when using single-scale features. To solve these issues, this paper proposes a fused feature pyramid network, which utilizes both color and depth data for the 3D verification of existing buildings 2D footprints from oblique images. First, the color data of oblique images are enriched with the depth information rendered from 3D mesh models. Second, multiscale features are fused in the feature pyramid network to convolve both the color and depth data. Finally, multi-view information from both the nadir and oblique images is used in a robust voting procedure to label changes in existing buildings. Experimental evaluations using both the ISPRS benchmark datasets and Shenzhen datasets reveal that the proposed method outperforms the ResNet and EfficientNet networks by 5% and 2%, respectively, in terms of recall rate and precision. We demonstrate that the proposed method can successfully detect all changed buildings; therefore, only those marked as changed need to be manually checked during the pipeline updating procedure; this significantly reduces the manual quality control requirements. Moreover, ablation studies indicate that using depth data, feature pyramid modules, and multi-view voting strategies can lead to clear and progressive improvements.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Depth-Enhanced Feature Pyramid Network for Occlusion-Aware Verification of Buildings from Oblique Images

Zhu,

Huang,

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…At present, multi-temporal remote sensing images play an important role in many fields, such as transform detection [1][2][3][4], image segmentation [5], and image matching [6]. In the process of acquiring remote sensing images, due to the difference in shooting angle and shooting time, the collected images have a low image coincidence rate and exhibit significant image distortion [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…To obtain pairs of points in the same spatial position between images, it is necessary to extract information accurately and extensively from the images. (1) Firstly, the features in the interest region must be extracted. Remote sensing images have a complex structure, are rich in information, and have a large number of feature points.…”

Section: Introductionmentioning

confidence: 99%

Bidirectional Symmetry Network with Dual-Field Cyclic Attention for Multi-Temporal Aerial Remote Sensing Image Registration

Chen

Zhang

et al. 2021

Symmetry

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Multi-temporal remote sensing image registration is a geometric symmetry process that involves matching a source image with a target image. To improve the accuracy and enhance the robustness of the algorithm, this study proposes an end-to-end registration network—a bidirectional symmetry network based on dual-field cyclic attention for multi-temporal remote sensing image registration, which mainly improves feature extraction and feature matching. (1) We propose a feature extraction framework combining an attention module and a pre-training model, which can accurately locate important areas in images and quickly extract features. Not only is the dual receptive field module designed to enhance attention in the spatial region, a loop structure is also used to improve the network model and improve overall accuracy. (2) Matching has not only directivity but also symmetry. We design a symmetric network of two-way matching to reduce the registration deviation caused by one-way matching and use a Pearson correlation method to improve the cross-correlation matching and enhance the robustness of the matching relation. In contrast with two traditional methods and three deep learning-based algorithms, the proposed approach works well under five indicators in three public multi-temporal datasets. Notably, in the case of the Aerial Image Dataset, the accuracy of the proposed method is improved by 39.8% compared with the Two-stream Ensemble method under a PCK (Percentage of Correct Keypoints) index of 0.05. When the PCK index is 0.03, accuracy increases by 46.8%, and increases by 18.7% under a PCK index of 0.01. Additionally, when adding the innovation points in feature extraction into the basic network CNNGeo (Convolutional Neural Network Architecture for Geometric Matching), accuracy is increased by 36.7% under 0.05 PCK, 18.2% under 0.03 PCK, and 8.4% under 0.01 PCK. Meanwhile, by adding the innovation points in feature matching into CNNGeo, accuracy is improved by 16.4% under 0.05 PCK, 9.1% under 0.03 PCK, and 5.2% under 0.01 PCK. In most cases, this paper reports high registration accuracy and efficiency for multi-temporal remote sensing image registration.

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Building Detection based on Rectangle Approximation and Region Growing

Yin

Hao

Gao

et al. 2020

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

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Automatic building change detection through adaptive local textural features and sequential background removal

Cited by 7 publications

References 8 publications

Depth-Enhanced Feature Pyramid Network for Occlusion-Aware Verification of Buildings from Oblique Images

Depth-Enhanced Feature Pyramid Network for Occlusion-Aware Verification of Buildings from Oblique Images

Bidirectional Symmetry Network with Dual-Field Cyclic Attention for Multi-Temporal Aerial Remote Sensing Image Registration

Building Detection based on Rectangle Approximation and Region Growing

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