scholarly journals Delineation of Agricultural Field Boundaries from Sentinel-2 Images Using a Novel Super-Resolution Contour Detector Based on Fully Convolutional Networks

2019 ◽  
Vol 12 (1) ◽  
pp. 59 ◽  
Author(s):  
Khairiya Mudrik Masoud ◽  
Claudio Persello ◽  
Valentyn A. Tolpekin
Keyword(s):  
Contextual Information ◽  
Super Resolution ◽  
Contour Detection ◽  
Agricultural Field ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Learning Technique ◽  
Field Boundaries ◽  
Sentinel 2

Boundaries of agricultural fields are important features necessary for defining the location, shape, and spatial extent of agricultural units. They are commonly used to summarize production statistics at the field level. In this study, we investigate the delineation of agricultural field boundaries (AFB) from Sentinel-2 satellite images acquired over the Flevoland province, the Netherlands, using a deep learning technique based on fully convolutional networks (FCNs). We designed a multiple dilation fully convolutional network (MD-FCN) for AFB detection from Sentinel-2 images at 10 m resolution. Furthermore, we developed a novel super-resolution semantic contour detection network (named SRC-Net) using a transposed convolutional layer in the FCN architecture to enhance the spatial resolution of the AFB output from 10 m to 5 m resolution. The SRC-Net also improves the AFB maps at 5 m resolution by exploiting the spatial-contextual information in the label space. The results of the proposed SRC-Net outperform alternative upsampling techniques and are only slightly inferior to the results of the MD-FCN for AFB detection from RapidEye images acquired at 5 m resolution.

scholarly journals Advanced Fully Convolutional Networks for Agricultural Field Boundary Detection

2021 ◽  
Vol 13 (4) ◽  
pp. 722
Author(s):  
Alireza Taravat ◽  
Matthias P. Wagner ◽  
Rogerio Bonifacio ◽  
David Petit
Keyword(s):  
Network Architecture ◽  
Spatial Information ◽  
Cropping Systems ◽  
Boundary Detection ◽  
Jaccard Coefficient ◽  
Agricultural Field ◽  
Field Boundary ◽  
Convolutional Network ◽  
Fully Convolutional Networks ◽  
Field Boundaries

Accurate spatial information of agricultural fields is important for providing actionable information to farmers, managers, and policymakers. On the other hand, the automated detection of field boundaries is a challenging task due to their small size, irregular shape and the use of mixed-cropping systems making field boundaries vaguely defined. In this paper, we propose a strategy for field boundary detection based on the fully convolutional network architecture called ResU-Net. The benefits of this model are two-fold: first, residual units ease training of deep networks. Second, rich skip connections within the network could facilitate information propagation, allowing us to design networks with fewer parameters but better performance in comparison with the traditional U-Net model. An extensive experimental analysis is performed over the whole of Denmark using Sentinel-2 images and comparing several U-Net and ResU-Net field boundary detection algorithms. The presented results show that the ResU-Net model has a better performance with an average F1 score of 0.90 and average Jaccard coefficient of 0.80 in comparison to the U-Net model with an average F1 score of 0.88 and an average Jaccard coefficient of 0.77.

scholarly journals Image-Based Iron Slag Segmentation via Graph Convolutional Networks

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wang Long ◽  
Zheng Junfeng ◽  
Yu Hong ◽  
Ding Meng ◽  
Li Jiangyun
Keyword(s):  
Deep Learning ◽  
Contextual Information ◽  
Recognition Algorithm ◽  
Smart Manufacturing ◽  
Convolutional Network ◽  
Slag Removal ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Iron Slag ◽  
Wide Range

Slagging-off (i.e., slag removal) is an important preprocessing operation of steel-making to improve the purity of iron. Current manual-operated slag removal schemes are inefficient and labor-intensive. Automatic slagging-off is desirable but challenging as the reliable recognition of iron and slag is difficult. This work focuses on realizing an efficient and accurate recognition algorithm of iron and slag, which is conducive to realize automatic slagging-off operation. Motivated by the recent success of deep learning techniques in smart manufacturing, we introduce deep learning methods to this field for the first time. The monotonous gray value of industry images, poor image quality, and nonrigid feature of iron and slag challenge the existing fully convolutional networks (FCNs). To this end, we propose a novel spatial and feature graph convolutional network (SFGCN) module. SFGCN module can be easily inserted in FCNs to improve the reasoning ability of global contextual information, which is helpful to enhance the segmentation accuracy of small objects and isolated areas. To verify the validity of the SFGCN module, we create an industrial dataset and conduct extensive experiments. Finally, the results show that our SFGCN module brings a consistent performance boost for a wide range of FCNs. Moreover, by adopting a lightweight network as backbone, our method achieves real-time iron and slag segmentation. In the future work, we will dedicate our efforts to the weakly supervised learning for quick annotation of big data stream to improve the generalization ability of current models.

scholarly journals Deforestation Detection with Fully Convolutional Networks in the Amazon Forest from Landsat-8 and Sentinel-2 Images

2021 ◽  
Vol 13 (24) ◽  
pp. 5084
Author(s):  
Daliana Lobo Torres ◽  
Javier Noa Turnes ◽  
Pedro Juan Soto Vega ◽  
Raul Queiroz Feitosa ◽  
Daniel E. Silva ◽  
...  
Keyword(s):  
Change Detection ◽  
Forest Monitoring ◽  
Landsat 8 ◽  
Amazon Forest ◽  
Forest Change ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Detection Analysis ◽  
Sentinel 2

The availability of remote-sensing multisource data from optical-based satellite sensors has created new opportunities and challenges for forest monitoring in the Amazon Biome. In particular, change-detection analysis has emerged in recent decades to monitor forest-change dynamics, supporting some Brazilian governmental initiatives such as PRODES and DETER projects for biodiversity preservation in threatened areas. In recent years fully convolutional network architectures have witnessed numerous proposals adapted for the change-detection task. This paper comprehensively explores state-of-the-art fully convolutional networks such as U-Net, ResU-Net, SegNet, FC-DenseNet, and two DeepLabv3+ variants on monitoring deforestation in the Brazilian Amazon. The networks’ performance is evaluated experimentally in terms of Precision, Recall, F1-score, and computational load using satellite images with different spatial and spectral resolution: Landsat-8 and Sentinel-2. We also include the results of an unprecedented auditing process performed by senior specialists to visually evaluate each deforestation polygon derived from the network with the highest accuracy results for both satellites. This assessment allowed estimation of the accuracy of these networks simulating a process “in nature” and faithful to the PRODES methodology. We conclude that the high resolution of Sentinel-2 images improves the segmentation of deforestation polygons both quantitatively (in terms of F1-score) and qualitatively. Moreover, the study also points to the potential of the operational use of Deep Learning (DL) mapping as products to be consumed in PRODES.

scholarly journals Diurnal and nocturnal cloud segmentation of all-sky imager (ASI) images using enhancement fully convolutional networks

2019 ◽  
Vol 12 (9) ◽  
pp. 4713-4724
Author(s):  
Chaojun Shi ◽  
Yatong Zhou ◽  
Bo Qiu ◽  
Jingfei He ◽  
Mu Ding ◽  
...  
Keyword(s):  
Color Space ◽  
Histogram Equalization ◽  
Convolutional Network ◽  
Data Set ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Saturation Intensity ◽  
Hsi Color Space ◽  
Rgb Color Space ◽  
Sky Imager

Abstract. Cloud segmentation plays a very important role in astronomical observatory site selection. At present, few researchers segment cloud in nocturnal all-sky imager (ASI) images. This paper proposes a new automatic cloud segmentation algorithm that utilizes the advantages of deep-learning fully convolutional networks (FCNs) to segment cloud pixels from diurnal and nocturnal ASI images; it is called the enhancement fully convolutional network (EFCN). Firstly, all the ASI images in the data set from the Key Laboratory of Optical Astronomy at the National Astronomical Observatories of Chinese Academy of Sciences (CAS) are converted from the red–green–blue (RGB) color space to hue saturation intensity (HSI) color space. Secondly, the I channel of the HSI color space is enhanced by histogram equalization. Thirdly, all the ASI images are converted from the HSI color space to RGB color space. Then after 100 000 iterative trainings based on the ASI images in the training set, the optimum associated parameters of the EFCN-8s model are obtained. Finally, we use the trained EFCN-8s to segment the cloud pixels of the ASI image in the test set. In the experiments our proposed EFCN-8s was compared with four other algorithms (OTSU, FCN-8s, EFCN-32s, and EFCN-16s) using four evaluation metrics. Experiments show that the EFCN-8s is much more accurate in cloud segmentation for diurnal and nocturnal ASI images than the other four algorithms.

Spatial Relational Attention Using Fully Convolutional Networks for Image Caption Generation

2020 ◽  
Vol 19 (02) ◽  
pp. 2050011
Author(s):  
Teng Jiang ◽  
Liang Gong ◽  
Yupu Yang
Keyword(s):  
Spatial Relations ◽  
Image Features ◽  
Attention Mechanism ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Benchmark Datasets ◽  
Visual Concepts ◽  
Image Caption Generation ◽  
Image Caption

Attention-based encoder–decoder framework has greatly improved image caption generation tasks. The attention mechanism plays a transitional role by transforming static image features into sequential captions. To generate reasonable captions, it is of great significance to detect spatial characteristics of images. In this paper, we propose a spatial relational attention approach to consider spatial positions and attributes. Image features are firstly weighted by the attention mechanism. Then they are concatenated with contextual features to form a spatial–visual tensor. The tensor is feature extracted by a fully convolutional network to produce visual concepts for the decoder network. The fully convolutional layers maintain spatial topology of images. Experiments conducted on the three benchmark datasets, namely Flickr8k, Flickr30k and MSCOCO, demonstrate the effectiveness of our proposed approach. Captions generated by the spatial relational attention method precisely capture spatial relations of objects.

scholarly journals Deep Residual Autoencoder with Multiscaling for Semantic Segmentation of Land-Use Images

2019 ◽  
Vol 11 (18) ◽  
pp. 2142 ◽  
Author(s):  
Lianfa Li
Keyword(s):  
Land Use ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Remotely Sensed ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Residual Learning ◽  
Fully Convolutional Networks ◽  
Remotely Sensed Images ◽  
Real World Datasets

Semantic segmentation is a fundamental means of extracting information from remotely sensed images at the pixel level. Deep learning has enabled considerable improvements in efficiency and accuracy of semantic segmentation of general images. Typical models range from benchmarks such as fully convolutional networks, U-Net, Micro-Net, and dilated residual networks to the more recently developed DeepLab 3+. However, many of these models were originally developed for segmentation of general or medical images and videos, and are not directly relevant to remotely sensed images. The studies of deep learning for semantic segmentation of remotely sensed images are limited. This paper presents a novel flexible autoencoder-based architecture of deep learning that makes extensive use of residual learning and multiscaling for robust semantic segmentation of remotely sensed land-use images. In this architecture, a deep residual autoencoder is generalized to a fully convolutional network in which residual connections are implemented within and between all encoding and decoding layers. Compared with the concatenated shortcuts in U-Net, these residual connections reduce the number of trainable parameters and improve the learning efficiency by enabling extensive backpropagation of errors. In addition, resizing or atrous spatial pyramid pooling (ASPP) can be leveraged to capture multiscale information from the input images to enhance the robustness to scale variations. The residual learning and multiscaling strategies improve the trained model’s generalizability, as demonstrated in the semantic segmentation of land-use types in two real-world datasets of remotely sensed images. Compared with U-Net, the proposed method improves the Jaccard index (JI) or the mean intersection over union (MIoU) by 4-11% in the training phase and by 3-9% in the validation and testing phases. With its flexible deep learning architecture, the proposed approach can be easily applied for and transferred to semantic segmentation of land-use variables and other surface variables of remotely sensed images.

scholarly journals Detecting High-Rise Buildings from Sentinel-2 Data Based on Deep Learning Method

2021 ◽  
Vol 13 (20) ◽  
pp. 4073
Author(s):  
Liwei Li ◽  
Jinming Zhu ◽  
Gang Cheng ◽  
Bing Zhang
Keyword(s):  
Large Scale ◽  
3D Structure ◽  
Image Features ◽  
Convolutional Networks ◽  
High Rise ◽  
Fully Convolutional Networks ◽  
Four Seasons ◽  
Training Samples ◽  
Definition Of ◽  
Sentinel 2

High-rise buildings (HRBs) as a modern and visually distinctive land use play an important role in urbanization. Large-scale monitoring of HRBs is valuable in urban planning and environmental protection and so on. Due to the complex 3D structure and seasonal dynamic image features of HRBs, it is still challenging to monitor large-scale HRBs in a routine way. This paper extends our previous work on the use of the Fully Convolutional Networks (FCN) model to extract HRBs from Sentinel-2 data by studying the influence of seasonal and spatial factors on the performance of the FCN model. 16 Sentinel-2 subset images covering four diverse regions in four seasons were selected for training and validation. Our results indicate the performance of the FCN-based method at the extraction of HRBs from Sentinel-2 data fluctuates among seasons and regions. The seasonal change of accuracy is larger than that of the regional change. If an optimal season can be chosen to get a yearly best result, F1 score of detected HRBs can reach above 0.75 for all regions with most errors located on the boundary of HRBs. FCN model can be trained on seasonally and regionally combined samples to achieve similar or even better overall accuracy than that of the model trained on an optimal combination of season and region. Uncertainties exist on the boundary of detected results and may be relieved by revising the definition of HRBs in a more rigorous way. On the whole, the FCN based method can be largely effective at the extraction of HRBs from Sentinel-2 data in regions with a large diversity in culture, latitude, and landscape. Our results support the possibility to build a powerful FCN model on a larger size of training samples for operational monitoring HRBs at the regional level or even on a country scale.

scholarly journals Automated delineation of agricultural field boundaries from Sentinel-2 images using recurrent residual U-Net

2021 ◽  
Vol 105 ◽  
pp. 102557
Author(s):  
Huanxue Zhang ◽  
Mingxu Liu ◽  
Yuji Wang ◽  
Jiali Shang ◽  
Xiangliang Liu ◽  
...  
Keyword(s):  
Agricultural Field ◽  
Field Boundaries ◽  
Sentinel 2

scholarly journals Attention-Based Context Aware Network for Semantic Comprehension of Aerial Scenery

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1983
Author(s):  
Weipeng Shi ◽  
Wenhu Qin ◽  
Zhonghua Yun ◽  
Peng Ping ◽  
Kaiyang Wu ◽  
...  
Keyword(s):  
High Resolution ◽  
Semantic Segmentation ◽  
Aerial Images ◽  
Aerial Image ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Semantic Labeling ◽  
Autonomous Cars ◽  
High Resolution Images

It is essential for researchers to have a proper interpretation of remote sensing images (RSIs) and precise semantic labeling of their component parts. Although FCN (Fully Convolutional Networks)-like deep convolutional network architectures have been widely applied in the perception of autonomous cars, there are still two challenges in the semantic segmentation of RSIs. The first is to identify details in high-resolution images with complex scenes and to solve the class-mismatch issues; the second is to capture the edge of objects finely without being confused by the surroundings. HRNET has the characteristics of maintaining high-resolution representation by fusing feature information with parallel multi-resolution convolution branches. We adopt HRNET as a backbone and propose to incorporate the Class-Oriented Region Attention Module (CRAM) and Class-Oriented Context Fusion Module (CCFM) to analyze the relationships between classes and patch regions and between classes and local or global pixels, respectively. Thus, the perception capability of the model for the detailed part in the aerial image can be enhanced. We leverage these modules to develop an end-to-end semantic segmentation model for aerial images and validate it on the ISPRS Potsdam and Vaihingen datasets. The experimental results show that our model improves the baseline accuracy and outperforms some commonly used CNN architectures.

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