scholarly journals A Novel Post-Processing Method Based on a Weighted Composite Filter for Enhancing Semantic Segmentation Results

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5500
Author(s):  
Xin Cheng ◽  
Huashan Liu
Keyword(s):  
Conditional Random Fields ◽  
Minimum Spanning Tree ◽  
Weight Ratio ◽  
Semantic Segmentation ◽  
Post Processing ◽  
Unified Framework ◽  
Guided Image Filter ◽  
Boundary Information ◽  
Composite Filter ◽  
Almost All

Image semantic segmentation is one of the key problems in computer vision. Despite the enormous advances in applications, almost all the image semantic segmentation algorithms fail to achieve satisfactory segmentation results due to lack of sensitivity to details, or difficulty in evaluating the global similarity of pixels, or both. Posting-processing enhancement methods, as the outstandingly crucial means to ameliorate the above-mentioned inherent flaws of algorithms, are almost based on conditional random fields (CRFs). Inspired by CRFs, this paper proposes a novel post-processing enhancement framework with theoretical simplicity from the perspective of filtering, and a new weighted composite filter (WCF) is designed to enhance the segmentation masks in a unified framework. First, by adjusting the weight ratio, the WCF is decomposed into a local part and a global part. Secondly, a guided image filter is designed as the local filter, which can restore boundary information to present necessary details. Moreover, a minimum spanning tree (MST)-based filter is designed as the global filter to provide a natural measure of global pixel similarity for image matching. Thirdly, a unified post-processing enhancement framework, including selection and normalization, WCF and argmax, is designed. Finally, the effectiveness and superiority of the proposed method for enhancement, as well as its range of applications, are verified through experiments.

scholarly journals HRCNet: High-Resolution Context Extraction Network for Semantic Segmentation of Remote Sensing Images

2020 ◽  
Vol 13 (1) ◽  
pp. 71
Author(s):  
Zhiyong Xu ◽  
Weicun Zhang ◽  
Tianxiang Zhang ◽  
Jiangyun Li
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
High Resolution ◽  
Spatial Information ◽  
Semantic Segmentation ◽  
Context Information ◽  
Remote Sensing Images ◽  
Global Context ◽  
Boundary Information ◽  
Extraction Stage

Semantic segmentation is a significant method in remote sensing image (RSIs) processing and has been widely used in various applications. Conventional convolutional neural network (CNN)-based semantic segmentation methods are likely to lose the spatial information in the feature extraction stage and usually pay little attention to global context information. Moreover, the imbalance of category scale and uncertain boundary information meanwhile exists in RSIs, which also brings a challenging problem to the semantic segmentation task. To overcome these problems, a high-resolution context extraction network (HRCNet) based on a high-resolution network (HRNet) is proposed in this paper. In this approach, the HRNet structure is adopted to keep the spatial information. Moreover, the light-weight dual attention (LDA) module is designed to obtain global context information in the feature extraction stage and the feature enhancement feature pyramid (FEFP) structure is promoted and employed to fuse the contextual information of different scales. In addition, to achieve the boundary information, we design the boundary aware (BA) module combined with the boundary aware loss (BAloss) function. The experimental results evaluated on Potsdam and Vaihingen datasets show that the proposed approach can significantly improve the boundary and segmentation performance up to 92.0% and 92.3% on overall accuracy scores, respectively. As a consequence, it is envisaged that the proposed HRCNet model will be an advantage in remote sensing images segmentation.

scholarly journals ERN: Edge Loss Reinforced Semantic Segmentation Network for Remote Sensing Images

2018 ◽  
Vol 10 (9) ◽  
pp. 1339 ◽  
Author(s):  
Shuo Liu ◽  
Wenrui Ding ◽  
Chunhui Liu ◽  
Yu Liu ◽  
Yufeng Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Edge Detection ◽  
Qualitative Evaluation ◽  
Semantic Segmentation ◽  
Model Complexity ◽  
Semantic Ambiguity ◽  
Remote Sensing Images ◽  
Future Networks ◽  
Spatial Boundary ◽  
Boundary Information

The semantic segmentation of remote sensing images faces two major challenges: high inter-class similarity and interference from ubiquitous shadows. In order to address these issues, we develop a novel edge loss reinforced semantic segmentation network (ERN) that leverages the spatial boundary context to reduce the semantic ambiguity. The main contributions of this paper are as follows: (1) we propose a novel end-to-end semantic segmentation network for remote sensing, which involves multiple weighted edge supervisions to retain spatial boundary information; (2) the main representations of the network are shared between the edge loss reinforced structures and semantic segmentation, which means that the ERN simultaneously achieves semantic segmentation and edge detection without significantly increasing the model complexity; and (3) we explore and discuss different ERN schemes to guide the design of future networks. Extensive experimental results on two remote sensing datasets demonstrate the effectiveness of our approach both in quantitative and qualitative evaluation. Specifically, the semantic segmentation performance in shadow-affected regions is significantly improved.

scholarly journals Weakly Supervised Conditional Random Fields Model for Semantic Segmentation with Image Patches

2020 ◽  
Vol 10 (5) ◽  
pp. 1679
Author(s):  
Xinying Xu ◽  
Yujing Xue ◽  
Xiaoxia Han ◽  
Zhe Zhang ◽  
Jun Xie ◽  
...  
Keyword(s):  
Random Fields ◽  
Conditional Random Fields ◽  
Semantic Category ◽  
Semantic Segmentation ◽  
Potential Energy Function ◽  
Semantic Class ◽  
Image Patches ◽  
Benchmark Datasets ◽  
Weakly Supervised ◽  
Class Labels

Image semantic segmentation (ISS) is used to segment an image into regions with differently labeled semantic category. Most of the existing ISS methods are based on fully supervised learning, which requires pixel-level labeling for training the model. As a result, it is often very time-consuming and labor-intensive, yet still subject to manual errors and subjective inconsistency. To tackle such difficulties, a weakly supervised ISS approach is proposed, in which the challenging problem of label inference from image-level to pixel-level will be particularly addressed, using image patches and conditional random fields (CRF). An improved simple linear iterative cluster (SLIC) algorithm is employed to extract superpixels. for image segmentation. Specifically, it generates various numbers of superpixels according to different images, which can be used to guide the process of image patch extraction based on the image-level labeled information. Based on the extracted image patches, the CRF model is constructed for inferring semantic class labels, which uses the potential energy function to map from the image-level to pixel-level image labels. Finally, patch based CRF (PBCRF) model is used to accomplish the weakly supervised ISS. Experiments conducted on two publicly available benchmark datasets, MSRC and PASCAL VOC 2012, have demonstrated that our proposed algorithm can yield very promising results compared to quite a few state-of-the-art ISS methods, including some deep learning-based models.

scholarly journals Water Areas Segmentation from Remote Sensing Images Using a Separable Residual SegNet Network

2020 ◽  
Vol 9 (4) ◽  
pp. 256 ◽  
Author(s):  
Liguo Weng ◽  
Yiming Xu ◽  
Min Xia ◽  
Yonghong Zhang ◽  
Jia Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Spatial Information ◽  
Global Climate ◽  
Semantic Segmentation ◽  
Water Area ◽  
Remote Sensing Images ◽  
The One ◽  
High Level ◽  
Almost All

Changes on lakes and rivers are of great significance for the study of global climate change. Accurate segmentation of lakes and rivers is critical to the study of their changes. However, traditional water area segmentation methods almost all share the following deficiencies: high computational requirements, poor generalization performance, and low extraction accuracy. In recent years, semantic segmentation algorithms based on deep learning have been emerging. Addressing problems associated to a very large number of parameters, low accuracy, and network degradation during training process, this paper proposes a separable residual SegNet (SR-SegNet) to perform the water area segmentation using remote sensing images. On the one hand, without compromising the ability of feature extraction, the problem of network degradation is alleviated by adding modified residual blocks into the encoder, the number of parameters is limited by introducing depthwise separable convolutions, and the ability of feature extraction is improved by using dilated convolutions to expand the receptive field. On the other hand, SR-SegNet removes the convolution layers with relatively more convolution kernels in the encoding stage, and uses the cascading method to fuse the low-level and high-level features of the image. As a result, the whole network can obtain more spatial information. Experimental results show that the proposed method exhibits significant improvements over several traditional methods, including FCN, DeconvNet, and SegNet.

An alternative finite difference method for post-processing thermoelastic data using compatibility

1998 ◽  
Vol 33 (6) ◽  
pp. 437-447 ◽  
Author(s):  
S Barone ◽  
E A Patterson
Keyword(s):  
Finite Difference ◽  
Iterative Procedure ◽  
Boundary Values ◽  
Post Processing ◽  
Stress Concentrations ◽  
Compatibility Equation ◽  
Finite Difference Technique ◽  
Stress Separation ◽  
Boundary Information ◽  
Interior Points

A finite difference technique for processing thermoelastic data for biaxial problems is presented. The method enables thermoelastic data to be smoothed at interior points and boundary information to be calculated from measured non-boundary values by an iterative procedure based on the compatibility equation. The technique has been successfully applied to quantify stress concentrations and to perform stress separation.

scholarly journals Integrating Relation Constraints with Neural Relation Extractors

2020 ◽  
Vol 34 (05) ◽  
pp. 9442-9449
Author(s):  
Yuan Ye ◽  
Yansong Feng ◽  
Bingfeng Luo ◽  
Yuxuan Lai ◽  
Dongyan Zhao
Keyword(s):  
Neural Networks ◽  
Source Code ◽  
Relation Extraction ◽  
Rapid Progress ◽  
Post Processing ◽  
Unified Framework ◽  
Loss Term

Recent years have seen rapid progress in identifying predefined relationship between entity pairs using neural networks (NNs). However, such models often make predictions for each entity pair individually, thus often fail to solve the inconsistency among different predictions, which can be characterized by discrete relation constraints. These constraints are often defined over combinations of entity-relation-entity triples, since there often lack of explicitly well-defined type and cardinality requirements for the relations. In this paper, we propose a unified framework to integrate relation constraints with NNs by introducing a new loss term, Constraint Loss. Particularly, we develop two efficient methods to capture how well the local predictions from multiple instance pairs satisfy the relation constraints. Experiments on both English and Chinese datasets show that our approach can help NNs learn from discrete relation constraints to reduce inconsistency among local predictions, and outperform popular neural relation extraction (NRE) models even enhanced with extra post-processing. Our source code and datasets will be released at https://github.com/PKUYeYuan/Constraint-Loss-AAAI-2020.

scholarly journals Effects of Spatial Transformer Location on Segmentation Performance of a Dense Transformer Network

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
David Abou-Chacra ◽  
John Zelek
Keyword(s):  
Input Data ◽  
Spatial Information ◽  
Semantic Segmentation ◽  
Spatial Context ◽  
Class Label ◽  
Spatial Span ◽  
Almost All

Semantic segmentation solves the task of labelling every pixel inan image with its class label, and remains an important unsolvedproblem. While significant work has gone into using deep learningto solve this problem, almost all the existing research uses methodsthat do not make modifications on spatial context considered for thepixel being labelled. Spatial information is an important cue in taskssuch as segmentation, reusing the same spatial span for every pixeland every label may not be the best approach. Spatial TransformerNetworks have shown promising results in improving classificationperformance of existing networks by allowing networks to activelymanipulate their input data to achieve better performance. Our workshows the benefit of incorporating Spatial Transformer Networksand their corresponding decoders into networks tailored to semanticsegmentation. Our experiments show an improvement in performanceover baseline networks when using networks augmentedwith Spatial Transformers.

scholarly journals Waterfall Atrous Spatial Pooling Architecture for Efficient Semantic Segmentation

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5361 ◽  
Author(s):  
Bruno Artacho ◽  
Andreas Savakis
Keyword(s):  
Random Fields ◽  
Conditional Random Fields ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Training Time ◽  
Network Parameters ◽  
Spatial Pooling ◽  
Memory Footprint ◽  
Accuracy Increase ◽  
Spatial Pyramid

We propose a new efficient architecture for semantic segmentation, based on a “Waterfall” Atrous Spatial Pooling architecture, that achieves a considerable accuracy increase while decreasing the number of network parameters and memory footprint. The proposed Waterfall architecture leverages the efficiency of progressive filtering in the cascade architecture while maintaining multiscale fields-of-view comparable to spatial pyramid configurations. Additionally, our method does not rely on a postprocessing stage with Conditional Random Fields, which further reduces complexity and required training time. We demonstrate that the Waterfall approach with a ResNet backbone is a robust and efficient architecture for semantic segmentation obtaining state-of-the-art results with significant reduction in the number of parameters for the Pascal VOC dataset and the Cityscapes dataset.

Sign in / Sign up

Export Citation Format

Share Document