Estimating The Spatial Resolution of Overhead Imagery Using Convolutional Neural Networks

Liang, Haolin; Newsam, Shawn

doi:10.1109/icip.2019.8802954

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…This is usually not a problem in RS image analysis since this information is typically available. We have also investigated methods to automatically estimate the spatial resolution of RS imagery using CNNs [30]. Another issue is that resampling images, using bilinear interpolation for example, is not equivalent to images whose scale varies due to having been acquired at different altitudes, etc.…”

Section: Discussionmentioning

confidence: 99%

Scale Aware Adaptation for Land-Cover Classification in Remote Sensing Imagery

Deng

Zhu

Tian

et al. 2020

Preprint

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Land-cover classification using remote sensing imagery is an important Earth observation task. Recently, land cover classification has benefited from the development of fully connected neural networks for semantic segmentation. The benchmark datasets available for training deep segmentation models in remote sensing imagery tend to be small, however, often consisting of only a handful of images from a single location with a single scale. This limits the models' ability to generalize to other datasets. Domain adaptation has been proposed to improve the models' generalization but we find these approaches are not effective for dealing with the scale variation commonly found between remote sensing image collections. We therefore propose a scale aware adversarial learning framework to perform joint cross-location and cross-scale landcover classification. The framework has a dual discriminator architecture with a standard feature discriminator as well as a novel scale discriminator. We also introduce a scale attention module which produces scale-enhanced features. Experimental results show that the proposed framework outperforms state-of-the-art domain adaptation methods by a large margin. The open-sourced codes are available on Github: https://github.com/xdeng7/ scale-aware_da.

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Section: Discussionmentioning

confidence: 99%

Scale Aware Adaptation for Land-Cover Classification in Remote Sensing Imagery

Deng

Zhu

Tian

et al. 2020

Preprint

Self Cite

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“…To our knowledge, the only previous work on estimating the spatial resolution of overhead imagery is our own [6]. That work also uses deep learning regression but does not use an SAE frontend for improved feature extraction.…”

Section: Related Workmentioning

confidence: 99%

Estimating the Spatial Resolution of Very High-Resolution Overhead Imagery

Liang

Newsam

2019

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery

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We investigate the problem of estimating the spatial resolution of overhead imagery. More overhead imagery is becoming available without such meta-data either because it was not collected in the first place or was not preserved with the imagery. Knowing the spatial resolution can be important for a range of automated image understanding tasks such as object detection, semantic segmentation, etc. In this paper, we explore a regression framework with a feature extraction frontend and a dilated convolution backend to estimate the spatial resolution of an overhead image. We show that a stacked auto-encoder frontend outperforms a standard convolution neural network feature extractor. In order to demonstrate our approach, we construct an evaluation dataset consisting of a large collection of very high-resolution overhead images with spatial resolutions ranging from 0.15 to 1.0 meters per pixel. CCS CONCEPTS • Computing methodologies → Scene understanding; Supervised learning by regression; Neural networks.

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A Robust Framework to Estimate the Spatial Resolution of Overhead Images Using Off-the-Shelf Object Detectors

Liang

Newsam

2021

2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS

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Estimating The Spatial Resolution of Overhead Imagery Using Convolutional Neural Networks

Cited by 3 publications

References 7 publications

Scale Aware Adaptation for Land-Cover Classification in Remote Sensing Imagery

Scale Aware Adaptation for Land-Cover Classification in Remote Sensing Imagery

Estimating the Spatial Resolution of Very High-Resolution Overhead Imagery

A Robust Framework to Estimate the Spatial Resolution of Overhead Images Using Off-the-Shelf Object Detectors

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