A convolutional neural network for semi-automated lineament detection and vectorisation of remote sensing data using probabilistic clustering: A method and a challenge

2021 ◽  
Vol 151 ◽  
pp. 104724
Author(s):  
Amin Aghaee ◽  
Pejman Shamsipour ◽  
Shawn Hood ◽  
Rasmus Haugaard
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Remote Sensing Data ◽  
Probabilistic Clustering ◽  
Sensing Data

scholarly journals QUALITY EVALUATION OF LAND-COVER CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK

2018 ◽  
Vol XLII-3 ◽  
pp. 257-262 ◽  
Author(s):  
Y. Dang ◽  
J. Zhang ◽  
Y. Zhao ◽  
F. Luo ◽  
W. Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Land Cover ◽  
Convolutional Neural Network ◽  
Land Surface ◽  
Quality Evaluation ◽  
Remote Sensing Data ◽  
Land Cover Classification ◽  
Sensing Data ◽  
Evaluation Approach

Land-cover classification is one of the most important products of earth observation, which focuses mainly on profiling the physical characters of the land surface with temporal and distribution attributes and contains the information of both natural and man-made coverage elements, such as vegetation, soil, glaciers, rivers, lakes, marsh wetlands and various man-made structures. In recent years, the amount of high-resolution remote sensing data has increased sharply. Accordingly, the volume of land-cover classification products increases, as well as the need to evaluate such frequently updated products that is a big challenge. Conventionally, the automatic quality evaluation of land-cover classification is made through pixel-based classifying algorithms, which lead to a much trickier task and consequently hard to keep peace with the required updating frequency. In this paper, we propose a novel quality evaluation approach for evaluating the land-cover classification by a scene classification method Convolutional Neural Network (CNN) model. By learning from remote sensing data, those randomly generated kernels that serve as filter matrixes evolved to some operators that has similar functions to man-crafted operators, like Sobel operator or Canny operator, and there are other kernels learned by the CNN model that are much more complex and can’t be understood as existing filters. The method using CNN approach as the core algorithm serves quality-evaluation tasks well since it calculates a bunch of outputs which directly represent the image’s membership grade to certain classes. An automatic quality evaluation approach for the land-cover DLG-DOM coupling data (DLG for Digital Line Graphic, DOM for Digital Orthophoto Map) will be introduced in this paper. The CNN model as an robustness method for image evaluation, then brought out the idea of an automatic quality evaluation approach for land-cover classification. Based on this experiment, new ideas of quality evaluation of DLG-DOM coupling land-cover classification or other kinds of labelled remote sensing data can be further studied.

scholarly journals Application of Artificial Intelligence Algorithms to Control the Use of Flood-prone Areas

2021 ◽  
pp. 6-24
Author(s):  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Large Spatial Scale ◽  
Sensing Data ◽  
Hazardous Area ◽  
Flood Prone Areas

This article examines the possibility of using artificial intelligence tools to analyze the use of territories prone to flooding during floods. A modern system for monitoring the economic use of flood-prone areas should be based on the use of Earth remote sensing data. The analysis of satellite images, being a laborious task, can be automated through the use of specially trained convolutional neural networks of semantic segmentation based on the algorithm proposed in this article. In this work, on the previously identified flooding zones, using remote sensing data, development objects are automatically determined (segmented) for different times and, by combining information at different times, an assessment of the intensity of this construction in the inter-flood period is made. To form a training sample, a survey of several settlements in the Trans-Baikal Territory was carried out using unmanned aerial vehicles. The neural network was configured using the Python language and the PyTorch library. To select the best convolutional neural network configuration, various combinations of architectures and encoder types were tested for performance and accuracy. The best result in terms of speed and accuracy was shown by the U-Net architecture, built using a convolutional neural network with an SE-ResNeXt50 encoder. According to satellite images of high spatial resolution for the Aginskoye village of Trans-Baikal Kray, a development map was drawn in the flood hazardous area in 2013 and 2019. The objects of development in the period between floods were identified. The results of the study can make it possible to consider a number of important factors when planning the rational use of flood-prone areas in order to improve the quality of life in the region. The obtained maps of the development of flood-prone zones of a large spatial scale are planned to be recommended in the work of state authorities in the field of water resources protection and elimination of natural disasters.

scholarly journals A Fast Three-Dimensional Convolutional Neural Network-Based Spatiotemporal Fusion Method (STF3DCNN) Using a Spatial-Temporal-Spectral Dataset

2020 ◽  
Vol 12 (23) ◽  
pp. 3888
Author(s):  
Mingyuan Peng ◽  
Lifu Zhang ◽  
Xuejian Sun ◽  
Yi Cen ◽  
Xiaoyang Zhao
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Temporal Resolution ◽  
Three Dimensional ◽  
Remote Sensing Data ◽  
High Temporal Resolution ◽  
Fusion Method ◽  
Sensing Data ◽  
Resolution Data

With the growing development of remote sensors, huge volumes of remote sensing data are being utilized in related applications, bringing new challenges to the efficiency and capability of processing huge datasets. Spatiotemporal remote sensing data fusion can restore high spatial and high temporal resolution remote sensing data from multiple remote sensing datasets. However, the current methods require long computing times and are of low efficiency, especially the newly proposed deep learning-based methods. Here, we propose a fast three-dimensional convolutional neural network-based spatiotemporal fusion method (STF3DCNN) using a spatial-temporal-spectral dataset. This method is able to fuse low-spatial high-temporal resolution data (HTLS) and high-spatial low-temporal resolution data (HSLT) in a four-dimensional spatial-temporal-spectral dataset with increasing efficiency, while simultaneously ensuring accuracy. The method was tested using three datasets, and discussions of the network parameters were conducted. In addition, this method was compared with commonly used spatiotemporal fusion methods to verify our conclusion.

scholarly journals Synergistic Use of Multi-Temporal RADARSAT-2 and VENµS Data for Crop Classification Based on 1D Convolutional Neural Network

2020 ◽  
Vol 12 (5) ◽  
pp. 832 ◽  
Author(s):  
Chunhua Liao ◽  
Jinfei Wang ◽  
Qinghua Xie ◽  
Ayman Al Baz ◽  
Xiaodong Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Remote Sensing Data ◽  
Support Vector ◽  
Learning Methods ◽  
Sensing Data ◽  
Multi Temporal ◽  
Crop Classification

Annual crop inventory information is important for many agriculture applications and government statistics. The synergistic use of multi-temporal polarimetric synthetic aperture radar (SAR) and available multispectral remote sensing data can reduce the temporal gaps and provide the spectral and polarimetric information of the crops, which is effective for crop classification in areas with frequent cloud interference. The main objectives of this study are to develop a deep learning model to map agricultural areas using multi-temporal full polarimetric SAR and multi-spectral remote sensing data, and to evaluate the influence of different input features on the performance of deep learning methods in crop classification. In this study, a one-dimensional convolutional neural network (Conv1D) was proposed and tested on multi-temporal RADARSAT-2 and VENµS data for crop classification. Compared with the Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN) and non-deep learning methods including XGBoost, Random Forest (RF), and Support Vector Machina (SVM), the Conv1D performed the best when the multi-temporal RADARSAT-2 data (Pauli decomposition or coherency matrix) and VENµS multispectral data were fused by the Minimum Noise Fraction (MNF) transformation. The Pauli decomposition and coherency matrix gave similar overall accuracy (OA) for Conv1D when fused with the VENµS data by the MNF transformation (OA = 96.65 ± 1.03% and 96.72 ± 0.77%). The MNF transformation improved the OA and F-score for most classes when Conv1D was used. The results reveal that the coherency matrix has a great potential in crop classification and the MNF transformation of multi-temporal RADARSAT-2 and VENµS data can enhance the performance of Conv1D.

scholarly journals DFCNN-Based Semantic Recognition of Urban Functional Zones by Integrating Remote Sensing Data and POI Data

2020 ◽  
Vol 12 (7) ◽  
pp. 1088
Author(s):  
Hanqing Bao ◽  
Dongping Ming ◽  
Ya Guo ◽  
Kui Zhang ◽  
Keqi Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Spatial Information ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Social Sensing ◽  
Sensing Data ◽  
Functional Zone ◽  
Functional Zones

The urban functional zone, as a special fundamental unit of the city, helps to understand the complex interaction between human space activities and environmental changes. Based on the recognition of physical and social semantics of buildings, combining remote sensing data and social sensing data is an effective way to quickly and accurately comprehend urban functional zone patterns. From the object level, this paper proposes a novel object-wise recognition strategy based on very high spatial resolution images (VHSRI) and social sensing data. First, buildings are extracted according to the physical semantics of objects; second, remote sensing and point of interest (POI) data are combined to comprehend the spatial distribution and functional semantics in the social function context; finally, urban functional zones are recognized and determined by building with physical and social functional semantics. When it comes to building geometrical information extraction, this paper, given the importance of building boundary information, introduces the deeper edge feature map (DEFM) into the segmentation and classification, and improves the result of building boundary recognition. Given the difficulty in understanding deeper semantics and spatial information and the limitation of traditional convolutional neural network (CNN) models in feature extraction, we propose the Deeper-Feature Convolutional Neural Network (DFCNN), which is able to extract more and deeper features for building semantic recognition. Experimental results conducted on a Google Earth image of Shenzhen City show that the proposed method and model are able to effectively, quickly, and accurately recognize urban functional zones by combining building physical semantics and social functional semantics, and are able to ensure the accuracy of urban functional zone recognition.

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