scholarly journals Building Outline Extraction Directly Using the U2-Net Semantic Segmentation Model from High-Resolution Aerial Images and a Comparison Study

2021 ◽  
Vol 13 (16) ◽  
pp. 3187
Author(s):  
Xinchun Wei ◽  
Xing Li ◽  
Wei Liu ◽  
Lianpeng Zhang ◽  
Dayu Cheng ◽  
...  
Keyword(s):  
Edge Detection ◽  
Loss Function ◽  
Semantic Segmentation ◽  
Cross Entropy ◽  
Aerial Images ◽  
Building Extraction ◽  
Precise Position ◽  
Entropy Loss ◽  
Imbalance Problem ◽  
Outline Extraction

Deep learning techniques have greatly improved the efficiency and accuracy of building extraction using remote sensing images. However, high-quality building outline extraction results that can be applied to the field of surveying and mapping remain a significant challenge. In practice, most building extraction tasks are manually executed. Therefore, an automated procedure of a building outline with a precise position is required. In this study, we directly used the U2-net semantic segmentation model to extract the building outline. The extraction results showed that the U2-net model can provide the building outline with better accuracy and a more precise position than other models based on comparisons with semantic segmentation models (Segnet, U-Net, and FCN) and edge detection models (RCF, HED, and DexiNed) applied for two datasets (Nanjing and Wuhan University (WHU)). We also modified the binary cross-entropy loss function in the U2-net model into a multiclass cross-entropy loss function to directly generate the binary map with the building outline and background. We achieved a further refined outline of the building, thus showing that with the modified U2-net model, it is not necessary to use non-maximum suppression as a post-processing step, as in the other edge detection models, to refine the edge map. Moreover, the modified model is less affected by the sample imbalance problem. Finally, we created an image-to-image program to further validate the modified U2-net semantic segmentation model for building outline extraction.

scholarly journals A Novel Focal Phi Loss for Power Line Segmentation with Auxiliary Classifier U-Net

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2803
Author(s):  
Rabeea Jaffari ◽  
Manzoor Ahmed Hashmani ◽  
Constantino Carlos Reyes-Aldasoro
Keyword(s):  
Loss Function ◽  
Class Imbalance ◽  
Power Line ◽  
Aerial Images ◽  
Class Imbalance Problem ◽  
Trade Off ◽  
Urban Scenes ◽  
Imbalance Problem ◽  
A Minor ◽  
Evaluation Parameters

The segmentation of power lines (PLs) from aerial images is a crucial task for the safe navigation of unmanned aerial vehicles (UAVs) operating at low altitudes. Despite the advances in deep learning-based approaches for PL segmentation, these models are still vulnerable to the class imbalance present in the data. The PLs occupy only a minimal portion (1–5%) of the aerial images as compared to the background region (95–99%). Generally, this class imbalance problem is addressed via the use of PL-specific detectors in conjunction with the popular class balanced cross entropy (BBCE) loss function. However, these PL-specific detectors do not work outside their application areas and a BBCE loss requires hyperparameter tuning for class-wise weights, which is not trivial. Moreover, the BBCE loss results in low dice scores and precision values and thus, fails to achieve an optimal trade-off between dice scores, model accuracy, and precision–recall values. In this work, we propose a generalized focal loss function based on the Matthews correlation coefficient (MCC) or the Phi coefficient to address the class imbalance problem in PL segmentation while utilizing a generic deep segmentation architecture. We evaluate our loss function by improving the vanilla U-Net model with an additional convolutional auxiliary classifier head (ACU-Net) for better learning and faster model convergence. The evaluation of two PL datasets, namely the Mendeley Power Line Dataset and the Power Line Dataset of Urban Scenes (PLDU), where PLs occupy around 1% and 2% of the aerial images area, respectively, reveal that our proposed loss function outperforms the popular BBCE loss by 16% in PL dice scores on both the datasets, 19% in precision and false detection rate (FDR) values for the Mendeley PL dataset and 15% in precision and FDR values for the PLDU with a minor degradation in the accuracy and recall values. Moreover, our proposed ACU-Net outperforms the baseline vanilla U-Net for the characteristic evaluation parameters in the range of 1–10% for both the PL datasets. Thus, our proposed loss function with ACU-Net achieves an optimal trade-off for the characteristic evaluation parameters without any bells and whistles. Our code is available at Github.

scholarly journals Semantic Segmentation of Urban Buildings Using a High-Resolution Network (HRNet) with Channel and Spatial Attention Gates

2021 ◽  
Vol 13 (16) ◽  
pp. 3087
Author(s):  
Seonkyeong Seong ◽  
Jaewan Choi
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Spatial Attention ◽  
Semantic Segmentation ◽  
Aerial Images ◽  
Building Extraction ◽  
Learning Models ◽  
Urban Buildings

In this study, building extraction in aerial images was performed using csAG-HRNet by applying HRNet-v2 in combination with channel and spatial attention gates. HRNet-v2 consists of transition and fusion processes based on subnetworks according to various resolutions. The channel and spatial attention gates were applied in the network to efficiently learn important features. A channel attention gate assigns weights in accordance with the importance of each channel, and a spatial attention gate assigns weights in accordance with the importance of each pixel position for the entire channel. In csAG-HRNet, csAG modules consisting of a channel attention gate and a spatial attention gate were applied to each subnetwork of stage and fusion modules in the HRNet-v2 network. In experiments using two datasets, it was confirmed that csAG-HRNet could minimize false detections based on the shapes of large buildings and small nonbuilding objects compared to existing deep learning models.

scholarly journals Approximating the Gradient of Cross-Entropy Loss Function

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 111626-111635
Author(s):  
Li Li ◽  
Milos Doroslovacki ◽  
Murray H. Loew
Keyword(s):  
Loss Function ◽  
Cross Entropy ◽  
Entropy Loss

scholarly journals MPCE: A Maximum Probability Based Cross Entropy Loss Function for Neural Network Classification

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 146331-146341 ◽  
Author(s):  
Yangfan Zhou ◽  
Xin Wang ◽  
Mingchuan Zhang ◽  
Junlong Zhu ◽  
Ruijuan Zheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Loss Function ◽  
Cross Entropy ◽  
Maximum Probability ◽  
Entropy Loss ◽  
Neural Network Classification

scholarly journals Shaping the learning landscape in neural networks around wide flat minima

2019 ◽  
Vol 117 (1) ◽  
pp. 161-170 ◽  
Author(s):  
Carlo Baldassi ◽  
Fabrizio Pittorino ◽  
Riccardo Zecchina
Keyword(s):  
Neural Networks ◽  
Loss Function ◽  
Critical Points ◽  
Learning Process ◽  
Numerical Study ◽  
Cross Entropy ◽  
Stochastic Gradient Descent ◽  
Neural Network Models ◽  
Entropy Loss ◽  
Error Loss

Learning in deep neural networks takes place by minimizing a nonconvex high-dimensional loss function, typically by a stochastic gradient descent (SGD) strategy. The learning process is observed to be able to find good minimizers without getting stuck in local critical points and such minimizers are often satisfactory at avoiding overfitting. How these 2 features can be kept under control in nonlinear devices composed of millions of tunable connections is a profound and far-reaching open question. In this paper we study basic nonconvex 1- and 2-layer neural network models that learn random patterns and derive a number of basic geometrical and algorithmic features which suggest some answers. We first show that the error loss function presents few extremely wide flat minima (WFM) which coexist with narrower minima and critical points. We then show that the minimizers of the cross-entropy loss function overlap with the WFM of the error loss. We also show examples of learning devices for which WFM do not exist. From the algorithmic perspective we derive entropy-driven greedy and message-passing algorithms that focus their search on wide flat regions of minimizers. In the case of SGD and cross-entropy loss, we show that a slow reduction of the norm of the weights along the learning process also leads to WFM. We corroborate the results by a numerical study of the correlations between the volumes of the minimizers, their Hessian, and their generalization performance on real data.

scholarly journals AttentionBased Deep Feature Fusion for the Scene Classification of HighResolution Remote Sensing Images

2019 ◽  
Vol 11 (17) ◽  
pp. 1996 ◽  
Author(s):  
Zhu ◽  
Yan ◽  
Mo ◽  
Liu
Keyword(s):  
Remote Sensing ◽  
Loss Function ◽  
Feature Fusion ◽  
Cross Entropy ◽  
Scene Classification ◽  
Remote Sensing Images ◽  
Graphic Processing Units ◽  
Entropy Loss ◽  
Deep Feature

Scene classification of highresolution remote sensing images (HRRSI) is one of the most important means of landcover classification. Deep learning techniques, especially the convolutional neural network (CNN) have been widely applied to the scene classification of HRRSI due to the advancement of graphic processing units (GPU). However, they tend to extract features from the whole images rather than discriminative regions. The visual attention mechanism can force the CNN to focus on discriminative regions, but it may suffer from the influence of intraclass diversity and repeated texture. Motivated by these problems, we propose an attention-based deep feature fusion (ADFF) framework that constitutes three parts, namely attention maps generated by Gradientweighted Class Activation Mapping (GradCAM), a multiplicative fusion of deep features and the centerbased cross-entropy loss function. First of all, we propose to make attention maps generated by GradCAM as an explicit input in order to force the network to concentrate on discriminative regions. Then, deep features derived from original images and attention maps are proposed to be fused by multiplicative fusion in order to consider both improved abilities to distinguish scenes of repeated texture and the salient regions. Finally, the centerbased cross-entropy loss function that utilizes both the cross-entropy loss and center loss function is proposed to backpropagate fused features so as to reduce the effect of intraclass diversity on feature representations. The proposed ADFF architecture is tested on three benchmark datasets to show its performance in scene classification. The experiments confirm that the proposed method outperforms most competitive scene classification methods with an average overall accuracy of 94% under different training ratios.

scholarly journals Ranking Loss: Maximizing the Success Rate in Deep Learning Side-Channel Analysis

2020 ◽  
pp. 25-55
Author(s):  
Gabriel Zaid ◽  
Lilian Bossuet ◽  
François Dassance ◽  
Amaury Habrard ◽  
Alexandre Venelli
Keyword(s):  
Deep Learning ◽  
Success Rate ◽  
Loss Function ◽  
Estimation Error ◽  
Approximation Error ◽  
Cross Entropy ◽  
Side Channel ◽  
Entropy Loss ◽  
The Cross ◽  
Ranking Loss

The side-channel community recently investigated a new approach, based on deep learning, to significantly improve profiled attacks against embedded systems. Compared to template attacks, deep learning techniques can deal with protected implementations, such as masking or desynchronization, without substantial preprocessing. However, important issues are still open. One challenging problem is to adapt the methods classically used in the machine learning field (e.g. loss function, performance metrics) to the specific side-channel context in order to obtain optimal results. We propose a new loss function derived from the learning to rank approach that helps preventing approximation and estimation errors, induced by the classical cross-entropy loss. We theoretically demonstrate that this new function, called Ranking Loss (RkL), maximizes the success rate by minimizing the ranking error of the secret key in comparison with all other hypotheses. The resulting model converges towards the optimal distinguisher when considering the mutual information between the secret and the leakage. Consequently, the approximation error is prevented. Furthermore, the estimation error, induced by the cross-entropy, is reduced by up to 23%. When the ranking loss is used, the convergence towards the best solution is up to 23% faster than a model using the cross-entropy loss function. We validate our theoretical propositions on public datasets.

scholarly journals Adaptive Feature Pyramid Network to Predict Crisp Boundaries via NMS Layer and ODS F-Measure Loss Function

Information ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 32
Author(s):  
Gang Sun ◽  
Hancheng Yu ◽  
Xiangtao Jiang ◽  
Mingkui Feng
Keyword(s):  
Edge Detection ◽  
Loss Function ◽  
State Of The Art ◽  
Cross Entropy ◽  
Post Processing ◽  
Multi Scale ◽  
Feature Pyramid ◽  
Multi Level ◽  
Different Levels ◽  
F Measure

Edge detection is one of the fundamental computer vision tasks. Recent methods for edge detection based on a convolutional neural network (CNN) typically employ the weighted cross-entropy loss. Their predicted results being thick and needing post-processing before calculating the optimal dataset scale (ODS) F-measure for evaluation. To achieve end-to-end training, we propose a non-maximum suppression layer (NMS) to obtain sharp boundaries without the need for post-processing. The ODS F-measure can be calculated based on these sharp boundaries. So, the ODS F-measure loss function is proposed to train the network. Besides, we propose an adaptive multi-level feature pyramid network (AFPN) to better fuse different levels of features. Furthermore, to enrich multi-scale features learned by AFPN, we introduce a pyramid context module (PCM) that includes dilated convolution to extract multi-scale features. Experimental results indicate that the proposed AFPN achieves state-of-the-art performance on the BSDS500 dataset (ODS F-score of 0.837) and the NYUDv2 dataset (ODS F-score of 0.780).

scholarly journals Precise Extraction of Buildings from High-Resolution Remote-Sensing Images Based on Semantic Edges and Segmentation

2021 ◽  
Vol 13 (16) ◽  
pp. 3083
Author(s):  
Liegang Xia ◽  
Junxia Zhang ◽  
Xiongbo Zhang ◽  
Haiping Yang ◽  
Meixia Xu
Keyword(s):  
Remote Sensing ◽  
Edge Detection ◽  
Semantic Segmentation ◽  
Building Extraction ◽  
The Past ◽  
Basic Task ◽  
Effective Combination ◽  
Level Information ◽  
Drawing Tasks ◽  
High Level

Building extraction is a basic task in the field of remote sensing, and it has also been a popular research topic in the past decade. However, the shape of the semantic polygon generated by semantic segmentation is irregular and does not match the actual building boundary. The boundary of buildings generated by semantic edge detection has difficulty ensuring continuity and integrity. Due to the aforementioned problems, we cannot directly apply the results in many drawing tasks and engineering applications. In this paper, we propose a novel convolutional neural network (CNN) model based on multitask learning, Dense D-LinkNet (DDLNet), which adopts full-scale skip connections and edge guidance module to ensure the effective combination of low-level information and high-level information. DDLNet has good adaptability to both semantic segmentation tasks and edge detection tasks. Moreover, we propose a universal postprocessing method that integrates semantic edges and semantic polygons. It can solve the aforementioned problems and more accurately locate buildings, especially building boundaries. The experimental results show that DDLNet achieves great improvements compared with other edge detection and semantic segmentation networks. Our postprocessing method is effective and universal.

Sign in / Sign up

Export Citation Format

Share Document