Semi-automatic building extraction from very high resolution remote sensing imagery via energy minimization model

Tan, Yihua; Yu, Yujie; Xiong, Shengzhou; Tian, Jinwen

doi:10.1109/igarss.2016.7729165

Cited by 6 publications

(1 citation statement)

References 14 publications

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“…The practical application mainly depends on manual interpretation, which requires a lot of manpower and material resources. In fact, semi-automatic interpretation (also called interactive extraction) can obtain a much better result with only a small amount of manual operation required [3].…”

Section: Introductionmentioning

confidence: 99%

Attention-Guided Multi-Scale Segmentation Neural Network for Interactive Extraction of Region Objects from High-Resolution Satellite Imagery

Jiang

et al. 2020

Remote Sensing

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Automatic extraction of region objects from high-resolution satellite imagery presents a great challenge, because there may be very large variations of the objects in terms of their size, texture, shape, and contextual complexity in the image. To handle these issues, we present a novel, deep-learning-based approach to interactively extract non-artificial region objects, such as water bodies, woodland, farmland, etc., from high-resolution satellite imagery. First, our algorithm transforms user-provided positive and negative clicks or scribbles into guidance maps, which consist of a relevance map modified from Euclidean distance maps, two geodesic distance maps (for positive and negative, respectively), and a sampling map. Then, feature maps are extracted by applying a VGG convolutional neural network pre-trained on the ImageNet dataset to the image X, and they are then upsampled to the resolution of X. Image X, guidance maps, and feature maps are integrated as the input tensor. We feed the proposed attention-guided, multi-scale segmentation neural network (AGMSSeg-Net) with the input tensor above to obtain the mask that assigns a binary label to each pixel. After a post-processing operation based on a fully connected Conditional Random Field (CRF), we extract the selected object boundary from the segmentation result. Experiments were conducted on two typical datasets with diverse region object types from complex scenes. The results demonstrate the effectiveness of the proposed method, and our approach outperforms existing methods for interactive image segmentation.

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Section: Introductionmentioning

confidence: 99%

Attention-Guided Multi-Scale Segmentation Neural Network for Interactive Extraction of Region Objects from High-Resolution Satellite Imagery

Jiang

et al. 2020

Remote Sensing

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Automatic building footprint extraction from very high-resolution imagery using deep learning techniques

Rastogi

Bodani

Sharma

2020

Geocarto International

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A review of building detection from very high resolution optical remote sensing images

Huang

et al. 2022

GIScience & Remote Sensing

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Semi-automatic building extraction from very high resolution remote sensing imagery via energy minimization model

Cited by 6 publications

References 14 publications

Attention-Guided Multi-Scale Segmentation Neural Network for Interactive Extraction of Region Objects from High-Resolution Satellite Imagery

Attention-Guided Multi-Scale Segmentation Neural Network for Interactive Extraction of Region Objects from High-Resolution Satellite Imagery

Automatic building footprint extraction from very high-resolution imagery using deep learning techniques

A review of building detection from very high resolution optical remote sensing images

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