Learning deep representations for semantic image parsing: a comprehensive overview

Huang, Lili; Peng, Jiefeng; Zhang, Ruimao; Li, Guanbin; Li, Lin

doi:10.1007/s11704-018-7195-8

Cited by 11 publications

(3 citation statements)

References 84 publications

(210 reference statements)

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“…We used a UNet neural network architecture [46], which is especially useful for small datasets, as it is clearly the case here. Here, small refers to the number of images commonly used for training CNNs (often tens of thousands in case of semantic segmentation [67]). In contrast to pixel-based classification, deep learning-based semantic segmentation can capture context information [46].…”

Section: Machine Learning Based On Neural Network-deep Learningmentioning

confidence: 99%

Towards Circumpolar Mapping of Arctic Settlements and Infrastructure Based on Sentinel-1 and Sentinel-2

et al. 2020

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Infrastructure expands rapidly in the Arctic due to industrial development. At the same time, climate change impacts are pronounced in the Arctic. Ground temperatures are, for example, increasing as well as coastal erosion. A consistent account of the current human footprint is needed in order to evaluate the impact on the environments as well as risk for infrastructure. Identification of roads and settlements with satellite data is challenging due to the size of single features and low density of clusters. Spatial resolution and spectral characteristics of satellite data are the main issues regarding their separation. The Copernicus Sentinel-1 and -2 missions recently provided good spatial coverage and at the same time comparably high pixel spacing starting with 10 m for modes available across the entire Arctic. The purpose of this study was to assess the capabilities of both, Sentinel-1 C-band Synthetic Aperture Radar (SAR) and the Sentinel-2 multispectral information for Arctic focused mapping. Settings differ across the Arctic (historic settlements versus industrial, locations on bedrock versus tundra landscapes) and reference data are scarce and inconsistent. The type of features and data scarcity demand specific classification approaches. The machine learning approaches Gradient Boosting Machines (GBM) and deep learning (DL)-based semantic segmentation have been tested. Records for the Alaskan North Slope, Western Greenland, and Svalbard in addition to high-resolution satellite data have been used for validation and calibration. Deep learning is superior to GBM with respect to users accuracy. GBM therefore requires comprehensive postprocessing. SAR provides added value in case of GBM. VV is of benefit for road identification and HH for detection of buildings. Unfortunately, the Sentinel-1 acquisition strategy is varying across the Arctic. The majority is covered in VV+VH only. DL is of benefit for road and building detection but misses large proportions of other human-impacted areas, such as gravel pads which are typical for gas and oil fields. A combination of results from both GBM (Sentinel-1 and -2 combined) and DL (Sentinel-2; Sentinel-1 optional) is therefore suggested for circumpolar mapping.

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Section: Machine Learning Based On Neural Network-deep Learningmentioning

confidence: 99%

Towards Circumpolar Mapping of Arctic Settlements and Infrastructure Based on Sentinel-1 and Sentinel-2

et al. 2020

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“…In this section, we briefly review scene parsing methods based on the classic level set and FCNs. For more details, we refer the readers to the comprehensive survey [20], [21].…”

Section: Related Workmentioning

confidence: 99%

Deep Multiphase Level Set for Scene Parsing

Zhang

Liu

Lei

et al. 2020

IEEE Trans. on Image Process.

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Recently, Fully Convolutional Network (FCN) seems to be the go-to architecture for image segmentation, including semantic scene parsing. However, it is difficult for a generic FCN to discriminate pixels around the object boundaries, thus FCN based methods may output parsing results with inaccurate boundaries. Meanwhile, level set based active contours are superior to the boundary estimation due to the sub-pixel accuracy that they achieve. However, they are quite sensitive to initial settings. To address these limitations, in this paper we propose a novel Deep Multiphase Level Set (DMLS) method for semantic scene parsing, which efficiently incorporates multiphase level sets into deep neural networks. The proposed method consists of three modules, i.e., recurrent FCNs, adaptive multiphase level set, and deeply supervised learning. More specifically, recurrent FCNs learn multi-level representations of input images with different contexts. Adaptive multiphase level set drives the discriminative contour for each semantic class, which makes use of the advantages of both global and local information. In each time-step of the recurrent FCNs, deeply supervised learning is incorporated for model training. Extensive experiments on three public benchmarks have shown that our proposed method achieves new state-of-the-art performances.

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“…Since the textures of clothing images are very fine-grained and diverse, acquisition of finegrained segments is particularly important. Finally, obtaining accurate semantic segments of images is an important goal to achieve in semantic segmentation researches [1].…”

Section: Introductionmentioning

confidence: 99%

Stable Acquisition of Fine-Grained Segments Using Batch Normalization and Focal Loss with L1 Regularization in U-Net Structure

Choi

Jung

2020

IJFIS

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Acquisition of fine-grained segments in semantic segmentation is important in most sementic segmentation applications, especially for clothing images composed of fine-grained textures. However, most existing semantic segmentation methods based on fully convolutional network (FCN) were not enough to acquire fine-grained segments because they are based on a single resolution and can not well distinguish between objects in the images. To stabilize the acquisition of fine-grained segments, we propose a method that is composed of two additional components in the U-Net structure for processing multi-scale fine-grained segments. The first component is to use normalization at all layers. We found from experiments that normalization is a key process in stabilizing the acquisition of fine-grained segments, especially in the U-Net based methods because they operate on a multi-scale fine-grained segment. An additional component is to use model prediction correction using focal loss with L1 regularization. Focal loss can be used to control the model prediction term as regularization in the training process. From experiments, we found that our method was better than the existing methods.

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Learning deep representations for semantic image parsing: a comprehensive overview

Cited by 11 publications

References 84 publications

Towards Circumpolar Mapping of Arctic Settlements and Infrastructure Based on Sentinel-1 and Sentinel-2

Towards Circumpolar Mapping of Arctic Settlements and Infrastructure Based on Sentinel-1 and Sentinel-2

Deep Multiphase Level Set for Scene Parsing

Stable Acquisition of Fine-Grained Segments Using Batch Normalization and Focal Loss with L1 Regularization in U-Net Structure

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