Maritime Semantic Labeling of Optical Remote Sensing Images with Multi-Scale Fully Convolutional Network

Lin, Haoning; Shi, Zhenwei; Zou, Zhengxia

doi:10.3390/rs9050480

Cited by 74 publications

(38 citation statements)

References 52 publications

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“…[104]. Lastly, also new methodologies of segmentation based on NN (Neural Networks) analysis can be investigated to characterize the status of the monitored scene [105] at a semantic level. These techniques could also be applied at a low level (in situ), both using the information available from remote sensed database and re-process at granular scale, in order to improve eventually also remote sensed data reliability.…”

Section: Augmented Virtuality Visualizationmentioning

confidence: 99%

Augmented Virtuality for Coastal Management: A Holistic Use of In Situ and Remote Sensing for Large Scale Definition of Coastal Dynamics

Bartolini

Mecocci

Pozzebon

et al. 2018

IJGI

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Abstract:In this paper, the authors describe the architecture of a multidisciplinary data acquisition and visualization platform devoted to the management of coastal environments. The platform integrates heterogeneous data acquisition sub-systems that can be roughly divided into two main categories: remote sensing systems and in situ sensing systems. Remote sensing solutions that are going to be implemented include aerial and underwater data acquisition while in situ sensing solutions include the use of Radio Frequency IDentification (RFID) tracers, Wireless Sensor Networks and imaging techniques. All the data collected by these subsystems are stored, integrated and fused on a single platform that is also in charge of data visualization and analysis. This last task is carried out according to the paradigm of Augmented Virtuality that foresees the augmentation of a virtually reconstructed environment with data collected in the real world. The described solution proposes a novel holistic approach where different disciplines concur, with different data acquisition techniques, to a large scale definition of coastal dynamics, in order to better describe and face the coastal erosion phenomenon. The overall framework has been conceived by the so-called Team COSTE, a joint research team between the Universities of Pisa, Siena and Florence.

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Section: Augmented Virtuality Visualizationmentioning

confidence: 99%

Augmented Virtuality for Coastal Management: A Holistic Use of In Situ and Remote Sensing for Large Scale Definition of Coastal Dynamics

Bartolini

Mecocci

Pozzebon

et al. 2018

IJGI

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“…FCN has greatly increased the processing flexibility and computational efficiency, and the image-to-image mapping process is naturally suitable for the pixel-wise image labeling tasks. In remote sensing image interpretation fields, such tasks include land structure segmentation, sea-land segmentation and others [21][22][23]. However, raft labeling is different from the above labeling problems, where there is a huge difference between the semantic scales between them.…”

Section: Introductionmentioning

confidence: 99%

Automatic Raft Labeling for Remote Sensing Images via Dual-Scale Homogeneous Convolutional Neural Network

Shi

Zou

et al. 2018

Remote Sensing

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Raft-culture is a way of utilizing water for farming aquatic product. Automatic raft-culture monitoring by remote sensing technique is an important way to control the crop's growth and implement effective management. This paper presents an automatic pixel-wise raft labeling method based on fully convolutional network (FCN). As rafts are always tiny and neatly arranged in images, traditional FCN method fails to extract the clear boundary and other detailed information. Therefore, a homogeneous convolutional neural network (HCN) is designed, which only consists of convolutions and activations to retain all details. We further design a dual-scale structure (DS-HCN) to integrate higher-level contextual information for accomplishing sea-land segmentation and raft labeling at the same time in a uniform framework. A dataset with Gaofen-1 satellite images was collected to verify the effectiveness of our method. DS-HCN shows a satisfactory performance with a better interpretability and a more accurate labeling result.

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“…For example, researchers have used CNN to carry out remote sensing image segmentation and used conditional random fields to further refine the output class map [45][46][47][48]. To suit the characteristics of specific remote sensing imagery, other researchers have established new convolution-based per-pixel-label models, such as multi-scale fully convolutional networks [49], patch-based CNNs [50], and two-branch CNNs [51]. Effective work has also been carried out in extracting information from remote sensing imagery using convolution-based per-pixel-label models, e.g., extracting crop information for rice [52,53], wheat [54], leaf [55], and rape [56], as well as target detection for weeds [57][58][59], diseases [60][61][62], and extracting road information using improved FCN [63].…”

mentioning

confidence: 99%

A New CNN-Bayesian Model for Extracting Improved Winter Wheat Spatial Distribution from GF-2 imagery

Zhang

Han

Li³

et al. 2019

Remote Sensing

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When the spatial distribution of winter wheat is extracted from high-resolution remote sensing imagery using convolutional neural networks (CNN), field edge results are usually rough, resulting in lowered overall accuracy. This study proposed a new per-pixel classification model using CNN and Bayesian models (CNN-Bayesian model) for improved extraction accuracy. In this model, a feature extractor generates a feature vector for each pixel, an encoder transforms the feature vector of each pixel into a category-code vector, and a two-level classifier uses the difference between elements of category-probability vectors as the confidence value to perform per-pixel classifications. The first level is used to determine the category of a pixel with high confidence, and the second level is an improved Bayesian model used to determine the category of low-confidence pixels. The CNN-Bayesian model was trained and tested on Gaofen 2 satellite images. Compared to existing models, our approach produced an improvement in overall accuracy, the overall accuracy of SegNet, DeepLab, VGG-Ex, and CNN-Bayesian was 0.791, 0.852, 0.892, and 0.946, respectively. Thus, this approach can produce superior results when winter wheat spatial distribution is extracted from satellite imagery. 2 of 21 over the past few decades at regional or global scales [6][7][8]. As extraction of crop spatial distribution mainly relies on pixel-based image classification, correctly determining pixel features for accurate classification is the basis for this approach [9][10][11][12].The spectral characteristics of low-and middle-resolution remote sensing images are usually stable. Vegetation indexes are generally used as pixel features in studies using data from sources including the Moderate Resolution Imaging Spectroradiometer (MODIS) [6,[13][14][15][16], Enhanced Thematic Mapper/Thematic Mapper [13,17], and Systeme Probatoire d' Observation de la Terre [7,10]. These indices include the normalized difference vegetation index (NDVI) [5,6,[13][14][15], relationship analysis of NDVI [8], and enhanced vegetation index (EVI) [3,18], which are extracted from band values. Common classification methods include decision trees [5,11,13], linear regression [6], statistics [7], filtration [13], time-series analysis [14,15], the iterative self-organizing data analysis technique (ISODATA) [16], and the Mahalanobis distance [17]. Texture features can better describe the spatial structure of pixels, the Gray-Level Co-Occurrence Matrix is a commonly used texture feature [19], and Gabor [20] and wavelet transforms [19,21] are often used to extract texture features. Moreover, object-based image analysis technology is also widely used in pre-pixel classification [22,23]. Such methods can successfully extract the spatial distribution of winter wheat and other crops, but limitations in spatial resolution restrict the applicability of the results.The spatial resolution and precision of crop extraction can be significantly improved by using high-resolution imagery [8,24,25]. However,...

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Maritime Semantic Labeling of Optical Remote Sensing Images with Multi-Scale Fully Convolutional Network

Cited by 74 publications

References 52 publications

Augmented Virtuality for Coastal Management: A Holistic Use of In Situ and Remote Sensing for Large Scale Definition of Coastal Dynamics

Augmented Virtuality for Coastal Management: A Holistic Use of In Situ and Remote Sensing for Large Scale Definition of Coastal Dynamics

Automatic Raft Labeling for Remote Sensing Images via Dual-Scale Homogeneous Convolutional Neural Network

A New CNN-Bayesian Model for Extracting Improved Winter Wheat Spatial Distribution from GF-2 imagery

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