A Stacked Fully Convolutional Networks with Feature Alignment Framework for Multi-Label Land-cover Segmentation

Wu, Guoxing; Guo, Yan; Song, Xiangfei; Guo, Zhiling; Qiu, Rui; Shi, Xiaodan; Shibasaki, Ryosuke; Shao, Xiaowei

doi:10.3390/rs11091051

Cited by 21 publications

(17 citation statements)

References 51 publications

(52 reference statements)

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“…Because of information loss caused by subsampling and upsampling operations, the prediction results of classic FCN models often present blurred edges. Hence, advanced FCN-based methods using various strategies have been proposed, such as unpooling [30], deconvolution [31], skip connections [32,33], multi-constraints [34], and stacking [35]. Among FCN-based methods, two different approaches exist: (a) indirect and (b) direct approaches.…”

Section: Supervised Methodsmentioning

confidence: 99%

Learn to Extract Building Outline from Misaligned Annotation through Nearest Feature Selector

Wang

Guo

et al. 2020

Remote Sensing

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For efficient building outline extraction, many algorithms, including unsupervised or supervised, have been proposed over the past decades. In recent years, due to the rapid development of the convolutional neural networks, especially fully convolutional networks, building extraction is treated as a semantic segmentation task that deals with the extremely biased positive pixels. The state-of-the-art methods, either through direct or indirect approaches, are mainly focused on better network design. The shifts and rotations, which are coarsely presented in manually created annotations, have long been ignored. Due to the limited number of positive samples, the misalignment will significantly reduce the correctness of pixel-to-pixel loss that might lead to a gradient explosion. To overcome this, we propose a nearest feature selector (NFS) to dynamically re-align the prediction and slightly misaligned annotations. The NFS can be seamlessly appended to existing loss functions and prevent misleading by the errors or misalignment of annotations. Experiments on a large scale aerial image dataset with centered buildings and corresponding building outlines indicate that the additional NFS brings higher performance when compared to existing naive loss functions. In the classic L1 loss, the addition of NFS gains increments of 8.8% of f1-score, 8.9% of kappa coefficient, and 9.8% of Jaccard index, respectively.

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Section: Supervised Methodsmentioning

confidence: 99%

Learn to Extract Building Outline from Misaligned Annotation through Nearest Feature Selector

Wang

Guo

et al. 2020

Remote Sensing

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“…To further improve the feature engineering, automating this step via deep learning might be an idea worth investigating in future studies. Especially local context features, as generated by a CNN, have been successful in improving various land cover classifications, including detection of water [65,66].…”

Section: Features and Algorithmsmentioning

confidence: 99%

Extrapolating Satellite-Based Flood Masks by One-Class Classification—A Test Case in Houston

et al. 2021

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Flood masks are among the most common remote sensing products, used for rapid crisis information and as input for hydraulic and impact models. Despite the high relevance of such products, vegetated and urban areas are still unreliably mapped and are sometimes even excluded from analysis. The information content of synthetic aperture radar (SAR) images is limited in these areas due to the side-looking imaging geometry of radar sensors and complex interactions of the microwave signal with trees and urban structures. Classification from SAR data can only be optimized to reduce false positives, but cannot avoid false negatives in areas that are essentially unobservable to the sensor, for example, due to radar shadows, layover, speckle and other effects. We therefore propose to treat satellite-based flood masks as intermediate products with true positives, and unlabeled cells instead of negatives. This corresponds to the input of a positive-unlabeled (PU) learning one-class classifier (OCC). Assuming that flood extent is at least partially explainable by topography, we present a novel procedure to estimate the true extent of the flood, given the initial mask, by using the satellite-based products as input to a PU OCC algorithm learned on topographic features. Additional rainfall data and distance to buildings had only minor effect on the models in our experiments. All three of the tested initial flood masks were considerably improved by the presented procedure, with obtainable increases in the overall κ score ranging from 0.2 for a high quality initial mask to 0.7 in the best case for a standard emergency response product. An assessment of κ for vegetated and urban areas separately shows that the performance in urban areas is still better when learning from a high quality initial mask.

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“…DCCN [44] introduces a Coord-Conv module to improve the spatial information by putting the coordinate information into feature maps to reduce the loss of spatial features and strengthen the object boundaries. In [45], four stacked fully convolutional networks and one feature alignment framework are designed to calculate an alignment loss of features that are encoded from the four basic models, to balance their similarity and variety for multi-label land-cover segmentation. Relation-augmented FCN (RA-FCN) [20] proposes a spatial relation module and a channel relation module to model global relationships between any two spatial positions of feature maps to produce relation-augmented feature representations.…”

Section: B Semantic Segmentation Of Aerial Imagerymentioning

confidence: 99%

CSE-HRNet: A Context and Semantic Enhanced High-Resolution Network for Semantic Segmentation of Aerial Imagery

2020

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Semantic segmentation of high-resolution aerial images is a concerning issue of remote sensing applications. To address the issues of intra-class heterogeneity and inter-class homogeneity, a novel end-to-end semantic segmentation network, namely Context and Semantic Enhanced High-Resolution Network (CSE-HRNet), is proposed in this paper. Two procedures are considered comprehensively, which are multi-scale contextual feature extractor and multi-level semantic feature producer. Nested Dilated Residual Block (NDRB) is designed firstly, which could enhance the representational power of multi-scale contexts and tackle the issue of intra-class heterogeneity. The pyramidal feature hierarchy is introduced secondly, by which multi-level feature fusions could be utilized to enlarge inter-class semantic differences. Experimental results verify that, based on the Potsdam and Vaihingen benchmarks, the proposed CSE-HRNet can achieve competitive performance compared with other state-of-the-art methods. INDEX TERMS Semantic segmentation, image analysis, machine learning, remote sensing image.

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A Stacked Fully Convolutional Networks with Feature Alignment Framework for Multi-Label Land-cover Segmentation

Cited by 21 publications

References 51 publications

Learn to Extract Building Outline from Misaligned Annotation through Nearest Feature Selector

Learn to Extract Building Outline from Misaligned Annotation through Nearest Feature Selector

Extrapolating Satellite-Based Flood Masks by One-Class Classification—A Test Case in Houston

CSE-HRNet: A Context and Semantic Enhanced High-Resolution Network for Semantic Segmentation of Aerial Imagery

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