Extraction of land covers from remote sensing images based on a deep learning model of NDVI-RSU-Net

2021 ◽  
Vol 14 (20) ◽  
Author(s):  
Chen Yang ◽  
Jingwei Hou ◽  
Yanjuan Wang
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Learning Model ◽  
Remote Sensing Images ◽  
Land Covers ◽  
Deep Learning Model

An Improved Deep-Learning Model for Road Extraction from Very-High-Resolution Remote Sensing Images

2021 ◽  
Author(s):  
Wangyao Shen ◽  
Yunping Chen ◽  
Yuanlei Cheng ◽  
Kangzhuo Yang ◽  
Xiang Guo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Learning Model ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Deep Learning Model ◽  
Very High

scholarly journals Model Specialization for the Use of ESRGAN on Satellite and Airborne Imagery

2021 ◽  
Vol 13 (20) ◽  
pp. 4044
Author(s):  
Étienne Clabaut ◽  
Myriam Lemelin ◽  
Mickaël Germain ◽  
Yacine Bouroubi ◽  
Tony St-Pierre
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Texture Analysis ◽  
Spatial Resolution ◽  
Super Resolution ◽  
Learning Model ◽  
Generative Adversarial Networks ◽  
Remote Sensing Images ◽  
Adversarial Networks ◽  
Deep Learning Model

Training a deep learning model requires highly variable data to permit reasonable generalization. If the variability in the data about to be processed is low, the interest in obtaining this generalization seems limited. Yet, it could prove interesting to specialize the model with respect to a particular theme. The use of enhanced super-resolution generative adversarial networks (ERSGAN), a specific type of deep learning architecture, allows the spatial resolution of remote sensing images to be increased by “hallucinating” non-existent details. In this study, we show that ESRGAN create better quality images when trained on thematically classified images than when trained on a wide variety of examples. All things being equal, we further show that the algorithm performs better on some themes than it does on others. Texture analysis shows that these performances are correlated with the inverse difference moment and entropy of the images.

scholarly journals A Stacking Ensemble Deep Learning Model for Building Extraction from Remote Sensing Images

2021 ◽  
Vol 13 (19) ◽  
pp. 3898
Author(s):  
Duanguang Cao ◽  
Hanfa Xing ◽  
Man Sing Wong ◽  
Mei-Po Kwan ◽  
Huaqiao Xing ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Optimization Method ◽  
Learning Model ◽  
Ensemble Model ◽  
Building Extraction ◽  
Remote Sensing Images ◽  
Basic Model ◽  
Building Information ◽  
Deep Learning Model

Automatically extracting buildings from remote sensing images with deep learning is of great significance to urban planning, disaster prevention, change detection, and other applications. Various deep learning models have been proposed to extract building information, showing both strengths and weaknesses in capturing the complex spectral and spatial characteristics of buildings in remote sensing images. To integrate the strengths of individual models and obtain fine-scale spatial and spectral building information, this study proposed a stacking ensemble deep learning model. First, an optimization method for the prediction results of the basic model is proposed based on fully connected conditional random fields (CRFs). On this basis, a stacking ensemble model (SENet) based on a sparse autoencoder integrating U-NET, SegNet, and FCN-8s models is proposed to combine the features of the optimized basic model prediction results. Utilizing several cities in Hebei Province, China as a case study, a building dataset containing attribute labels is established to assess the performance of the proposed model. The proposed SENet is compared with three individual models (U-NET, SegNet and FCN-8s), and the results show that the accuracy of SENet is 0.954, approximately 6.7%, 6.1%, and 9.8% higher than U-NET, SegNet, and FCN-8s models, respectively. The identification of building features, including colors, sizes, shapes, and shadows, is also evaluated, showing that the accuracy, recall, F1 score, and intersection over union (IoU) of the SENet model are higher than those of the three individual models. This suggests that the proposed ensemble model can effectively depict the different features of buildings and provides an alternative approach to building extraction with higher accuracy.

scholarly journals Semantic segmentation of PolSAR image data using advanced deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rajat Garg ◽  
Anil Kumar ◽  
Nikunj Bansal ◽  
Manish Prateek ◽  
Shashi Kumar
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Urban Area ◽  
Urban Areas ◽  
Learning Algorithms ◽  
Semantic Segmentation ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Deep Learning Model

AbstractUrban area mapping is an important application of remote sensing which aims at both estimation and change in land cover under the urban area. A major challenge being faced while analyzing Synthetic Aperture Radar (SAR) based remote sensing data is that there is a lot of similarity between highly vegetated urban areas and oriented urban targets with that of actual vegetation. This similarity between some urban areas and vegetation leads to misclassification of the urban area into forest cover. The present work is a precursor study for the dual-frequency L and S-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission and aims at minimizing the misclassification of such highly vegetated and oriented urban targets into vegetation class with the help of deep learning. In this study, three machine learning algorithms Random Forest (RF), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) have been implemented along with a deep learning model DeepLabv3+ for semantic segmentation of Polarimetric SAR (PolSAR) data. It is a general perception that a large dataset is required for the successful implementation of any deep learning model but in the field of SAR based remote sensing, a major issue is the unavailability of a large benchmark labeled dataset for the implementation of deep learning algorithms from scratch. In current work, it has been shown that a pre-trained deep learning model DeepLabv3+ outperforms the machine learning algorithms for land use and land cover (LULC) classification task even with a small dataset using transfer learning. The highest pixel accuracy of 87.78% and overall pixel accuracy of 85.65% have been achieved with DeepLabv3+ and Random Forest performs best among the machine learning algorithms with overall pixel accuracy of 77.91% while SVM and KNN trail with an overall accuracy of 77.01% and 76.47% respectively. The highest precision of 0.9228 is recorded for the urban class for semantic segmentation task with DeepLabv3+ while machine learning algorithms SVM and RF gave comparable results with a precision of 0.8977 and 0.8958 respectively.

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