Deep Learning-Based Semantic Segmentation of Urban Features in Satellite Images: A Review and Meta-Analysis
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TLDR
In this paper, a review and meta-analysis of deep learning-based semantic segmentation in urban remote sensing images is presented. But, the focus of this paper is on urban sensing images.Abstract:
Availability of very high-resolution remote sensing images and advancement of deep learning methods have shifted the paradigm of image classification from pixel-based and object-based methods to deep learning-based semantic segmentation. This shift demands a structured analysis and revision of the current status on the research domain of deep learning-based semantic segmentation. The focus of this paper is on urban remote sensing images. We review and perform a meta-analysis to juxtapose recent papers in terms of research problems, data source, data preparation methods including pre-processing and augmentation techniques, training details on architectures, backbones, frameworks, optimizers, loss functions and other hyper-parameters and performance comparison. Our detailed review and meta-analysis show that deep learning not only outperforms traditional methods in terms of accuracy, but also addresses several challenges previously faced. Further, we provide future directions of research in this domain.read more
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References
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Problems of encoder-decoder frameworks for high-resolution remote sensing image segmentation: Structural stereotype and insufficient learning
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Bispace Domain Adaptation Network for Remotely Sensed Semantic Segmentation
TL;DR: A bispace alignment network for DA named BSANet, designed to have a dual-branch structure which is able to extract features in the image domain and the wavelet domain simultaneously, and shows the ability to train an end-to-end network for semantic segmentation without using any label in the target domain.
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Smallholder Crop Area Mapped with a Semantic Segmentation Deep Learning Method
TL;DR: Overall, the proposed approach can train a precise and effective model that is capable of adequately describing the small, irregular fields of smallholder agriculture and handling the great level of details in RGB high spatial resolution images.
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Recurrent Multiresolution Convolutional Networks for VHR Image Classification
TL;DR: In this paper, a single-stage framework embedding the processing stages in a recurrent multiresolution convolutional network trained in an end-to-end manner is proposed to match the resolution of the panchromatic and multispectral bands.
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Semantic Segmentation on Remotely Sensed Images Using an Enhanced Global Convolutional Network with Channel Attention and Domain Specific Transfer Learning
Teerapong Panboonyuen,Kulsawasd Jitkajornwanich,Siam Lawawirojwong,Panu Srestasathiern,Peerapon Vateekul +4 more
TL;DR: Zhang et al. as discussed by the authors proposed a novel CNN for semantic segmentation particularly for remote sensing corpora with three main contributions: applying a recent CNN called a global convolutional network (GCN), since it can capture different resolutions by extracting multi-scale features from different stages of the network.