scholarly journals Multi-Source and Multi-Scale Platform for Quantitative Assessment of Shallow or/and Coastal Qater

Proceedings ◽  
2019 ◽  
Vol 30 (1) ◽  
pp. 20
Author(s):  
Anna Brook ◽  
Ran Reznikov ◽  
Martin Kanning ◽  
Thomas Jarmer
Keyword(s):  
Learning Process ◽  
Coastal Waters ◽  
Super Resolution ◽  
Test Site ◽  
Alternative Methods ◽  
Spatial Characteristic ◽  
Convolutional Network ◽  
Training Site ◽  
Inland Water ◽  
Multi Scale

Coastal waters are one of the most vulnerable resources that require comprehensive investigation in space and time. One of the key factors for effective coastal monitoring is the use of remote sensing technologies. Since the Coastal Zone Color Scanner (CZCS) in 1978, a long list of space-borne missions had been successfully launched. However, those missions are limited to coastal waters applications. Despite a large number of missions, the existing systems are still facing similar challenges as four decades ago. Spatial and spectral data reconstruction and recovery a high resolution (HR) imagery data from a low resolution (LR) imaging is a challenging task in many applications. The most promising technique in the field of digital image processing is known as Super Resolution (SR). Many techniques focus on reconstructing information at the sub-pixel level and dividing the original LR space into pixels corresponding to the HR space. Other methods assume that a series of LR images (in time) of a scene scanned from different perspectives (affine) will provide SR. Alternative methods use different data sources and proper image algorithms. In most cases, SR methods will perform a learning process in which the system will try to identify the inherent redundancy in the natural data in order to retrieve HR information from LR based on a spatial correlation between the original images. The learning process can be significantly efficient by using the Convolutional Neural Network (CNN). CNN submit to training through a large dataset that preserves the scene’s characteristics. The flexibility afforded by CNN is learning nonlinear relationships when reconstructing a spatial characteristic from an LR image to HR image. The main aim of this study is to identify spectral features related to the coastal water and inland water variations at different spatial and temporal scale and integrate them with a multi-scale information system. The main objectives of the study are developing of spatial-temporal-spectral fusion approach for multi-source data collected from the same geographical site; creating a new method for single image reconstruction from non-complementary information scene. The proposed method measures HR given LR by a downscaling process by turning HR into an LR. The deterministic process calculated using a Gaussian filter and by a photographic-focused distribution function. The correlation coefficient (at the LR-pixel level) used as an inverse ratio to upscaling. The proposed architecture is based on a three-convolutional network. In the first stage, the convolution is directly applied to the LR data, and then another sub-pixel convolution layer is subtracted to generate SR data from LR data through an upscaling process. This study performed in two sites, (1) a training site in Israel, (2) a test site in Germany. The training site is shallow seawaters around Oren River, Israel and the test site is Alfsee inland water in Germany. The results in both sites are SR imagery with full Sentinel 2 spectral resolution and spatial resolution of 0.3 m.

scholarly journals Mapping and Discriminating Rural Settlements Using Gaofen-2 Images and a Fully Convolutional Network

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6062
Author(s):  
Ziran Ye ◽  
Bo Si ◽  
Yue Lin ◽  
Qiming Zheng ◽  
Ran Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Rural Areas ◽  
Ground Truth ◽  
Representation Learning ◽  
Rural Settlement ◽  
Spatial Characteristic ◽  
Rural Settlements ◽  
Convolutional Network ◽  
Essential Information ◽  
Multi Scale

New ongoing rural construction has resulted in an extensive mixture of new settlements with old ones in the rural areas of China. Understanding the spatial characteristic of these rural settlements is of crucial importance as it provides essential information for land management and decision-making. Despite a great advance in High Spatial Resolution (HSR) satellite images and deep learning techniques, it remains a challenging task for mapping rural settlements accurately because of their irregular morphology and distribution pattern. In this study, we proposed a novel framework to map rural settlements by leveraging the merits of Gaofen-2 HSR images and representation learning of deep learning. We combined a dilated residual convolutional network (Dilated-ResNet) and a multi-scale context subnetwork into an end-to-end architecture in order to learn high resolution feature representations from HSR images and to aggregate and refine the multi-scale features extracted by the aforementioned network. Our experiment in Tongxiang city showed that the proposed framework effectively mapped and discriminated rural settlements with an overall accuracy of 98% and Kappa coefficient of 85%, achieving comparable and improved performance compared to other existing methods. Our results bring tangible benefits to support other convolutional neural network (CNN)-based methods in accurate and timely rural settlement mapping, particularly when up-to-date ground truth is absent. The proposed method does not only offer an effective way to extract rural settlement from HSR images but open a new opportunity to obtain spatial-explicit understanding of rural settlements.

scholarly journals Image Super-Resolution Using Multi-scale Space Feature and Deformable Convolutional Network

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Guosong Jiang ◽  
Zhengwu Lu ◽  
Xuping Tu ◽  
Yurong Guan ◽  
Qingdong Wang
Keyword(s):  
Super Resolution ◽  
Scale Space ◽  
Convolutional Network ◽  
Multi Scale ◽  
Image Super Resolution

scholarly journals Remote Sensing Imagery Super Resolution Based on Adaptive Multi-Scale Feature Fusion Network

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1142
Author(s):  
Xinying Wang ◽  
Yingdan Wu ◽  
Yang Ming ◽  
Hui Lv
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Feature Fusion ◽  
Super Resolution ◽  
Convolutional Network ◽  
Remote Sensing Imagery ◽  
Resolution Image ◽  
Scale Feature ◽  
Multi Scale ◽  
Key Characteristics

Due to increasingly complex factors of image degradation, inferring high-frequency details of remote sensing imagery is more difficult compared to ordinary digital photos. This paper proposes an adaptive multi-scale feature fusion network (AMFFN) for remote sensing image super-resolution. Firstly, the features are extracted from the original low-resolution image. Then several adaptive multi-scale feature extraction (AMFE) modules, the squeeze-and-excited and adaptive gating mechanisms are adopted for feature extraction and fusion. Finally, the sub-pixel convolution method is used to reconstruct the high-resolution image. Experiments are performed on three datasets, the key characteristics, such as the number of AMFEs and the gating connection way are studied, and super-resolution of remote sensing imagery of different scale factors are qualitatively and quantitatively analyzed. The results show that our method outperforms the classic methods, such as Super-Resolution Convolutional Neural Network(SRCNN), Efficient Sub-Pixel Convolutional Network (ESPCN), and multi-scale residual CNN(MSRN).

Methods for studying dissolved oxygen levels in coastal and estuarine waters receiving combined sewer overflows

1995 ◽  
Vol 32 (2) ◽  
pp. 95-103
Author(s):  
José A. Revilla ◽  
Kalin N. Koev ◽  
Rafael Díaz ◽  
César Álvarez ◽  
Antonio Roldán
Keyword(s):  
Dissolved Oxygen ◽  
Coastal Waters ◽  
Computational Cost ◽  
Oxygen Deficit ◽  
Alternative Methods ◽  
Coastal Zones ◽  
Combined Sewer Overflows ◽  
Sewer Systems ◽  
Combined Sewer ◽  
High Computational Cost

One factor in determining the transport capacity of coastal interceptors in Combined Sewer Systems (CSS) is the reduction of Dissolved Oxygen (DO) in coastal waters originating from the overflows. The study of the evolution of DO in coastal zones is complex. The high computational cost of using mathematical models discriminates against the required probabilistic analysis being undertaken. Alternative methods, based on such mathematical modelling, employed in a limited number of cases, are therefore needed. In this paper two alternative methods are presented for the study of oxygen deficit resulting from overflows of CSS. In the first, statistical analyses focus on the causes of the deficit (the volume discharged). The second concentrates on the effects (the concentrations of oxygen in the sea). Both methods have been applied in a study of the coastal interceptor at Pasajes Estuary (Guipúzcoa, Spain) with similar results.

Semantic Relation Model and Dataset for Remote Sensing Scene Understanding

2021 ◽  
Vol 10 (7) ◽  
pp. 488
Author(s):  
Peng Li ◽  
Dezheng Zhang ◽  
Aziguli Wulamu ◽  
Xin Liu ◽  
Peng Chen
Keyword(s):  
Remote Sensing ◽  
Scene Understanding ◽  
Deep Understanding ◽  
Remote Sensing Images ◽  
Convolutional Network ◽  
Scene Graph ◽  
Multi Scale ◽  
Relationship Extraction ◽  
High Level ◽  
Graph Generation

A deep understanding of our visual world is more than an isolated perception on a series of objects, and the relationships between them also contain rich semantic information. Especially for those satellite remote sensing images, the span is so large that the various objects are always of different sizes and complex spatial compositions. Therefore, the recognition of semantic relations is conducive to strengthen the understanding of remote sensing scenes. In this paper, we propose a novel multi-scale semantic fusion network (MSFN). In this framework, dilated convolution is introduced into a graph convolutional network (GCN) based on an attentional mechanism to fuse and refine multi-scale semantic context, which is crucial to strengthen the cognitive ability of our model Besides, based on the mapping between visual features and semantic embeddings, we design a sparse relationship extraction module to remove meaningless connections among entities and improve the efficiency of scene graph generation. Meanwhile, to further promote the research of scene understanding in remote sensing field, this paper also proposes a remote sensing scene graph dataset (RSSGD). We carry out extensive experiments and the results show that our model significantly outperforms previous methods on scene graph generation. In addition, RSSGD effectively bridges the huge semantic gap between low-level perception and high-level cognition of remote sensing images.

scholarly journals Multi-FAN: multi-spectral mosaic super-resolution via multi-scale feature aggregation network

2021 ◽  
Vol 32 (2) ◽  
Author(s):  
Mehrdad Sheoiby ◽  
Sadegh Aliakbarian ◽  
Saeed Anwar ◽  
Lars Petersson
Keyword(s):  
Super Resolution ◽  
Scale Feature ◽  
Multi Scale ◽  
Feature Aggregation

scholarly journals Attention Multi-Scale Network for Automatic Layer Extraction of Ice Radar Topological Sequences

2021 ◽  
Vol 13 (12) ◽  
pp. 2425
Author(s):  
Yiheng Cai ◽  
Dan Liu ◽  
Jin Xie ◽  
Jingxian Yang ◽  
Xiangbin Cui ◽  
...  
Keyword(s):  
Deep Learning ◽  
Global Climate ◽  
Ice Sheets ◽  
Ice Sheet ◽  
Sheet Thickness ◽  
Learning Methods ◽  
Convolutional Network ◽  
Multi Scale ◽  
Ice Surface ◽  
Layer Extraction

Analyzing the surface and bedrock locations in radar imagery enables the computation of ice sheet thickness, which is important for the study of ice sheets, their volume and how they may contribute to global climate change. However, the traditional handcrafted methods cannot quickly provide quantitative, objective and reliable extraction of information from radargrams. Most traditional handcrafted methods, designed to detect ice-surface and ice-bed layers from ice sheet radargrams, require complex human involvement and are difficult to apply to large datasets, while deep learning methods can obtain better results in a generalized way. In this study, an end-to-end multi-scale attention network (MsANet) is proposed to realize the estimation and reconstruction of layers in sequences of ice sheet radar tomographic images. First, we use an improved 3D convolutional network, C3D-M, whose first full connection layer is replaced by a convolution unit to better maintain the spatial relativity of ice layer features, as the backbone. Then, an adjustable multi-scale module uses different scale filters to learn scale information to enhance the feature extraction capabilities of the network. Finally, an attention module extended to 3D space removes a redundant bottleneck unit to better fuse and refine ice layer features. Radar sequential images collected by the Center of Remote Sensing of Ice Sheets in 2014 are used as training and testing data. Compared with state-of-the-art deep learning methods, the MsANet shows a 10% reduction (2.14 pixels) on the measurement of average mean absolute column-wise error for detecting the ice-surface and ice-bottom layers, runs faster and uses approximately 12 million fewer parameters.

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