Aggregating Deep Convolutional Neural Network Scans of Broad-Area High-Resolution Remote Sensing Imagery

2018 ◽  
Author(s):  
Grant J. Scott ◽  
J. Alex Hurt ◽  
Richard A. Marcum ◽  
Derek T. Anderson ◽  
Curt H. Davis
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Broad Area ◽  
Remote Sensing Imagery

scholarly journals Road Extraction from High-Resolution Remote Sensing Imagery Using Deep Learning

2018 ◽  
Vol 10 (9) ◽  
pp. 1461 ◽  
Author(s):  
Yongyang Xu ◽  
Zhong Xie ◽  
Yaxing Feng ◽  
Zhanlong Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Road Network ◽  
Deep Convolutional Neural Network ◽  
Road Extraction ◽  
Remote Sensing Imagery ◽  
The Road

The road network plays an important role in the modern traffic system; as development occurs, the road structure changes frequently. Owing to the advancements in the field of high-resolution remote sensing, and the success of semantic segmentation success using deep learning in computer version, extracting the road network from high-resolution remote sensing imagery is becoming increasingly popular, and has become a new tool to update the geospatial database. Considering that the training dataset of the deep convolutional neural network will be clipped to a fixed size, which lead to the roads run through each sample, and that different kinds of road types have different widths, this work provides a segmentation model that was designed based on densely connected convolutional networks (DenseNet) and introduces the local and global attention units. The aim of this work is to propose a novel road extraction method that can efficiently extract the road network from remote sensing imagery with local and global information. A dataset from Google Earth was used to validate the method, and experiments showed that the proposed deep convolutional neural network can extract the road network accurately and effectively. This method also achieves a harmonic mean of precision and recall higher than other machine learning and deep learning methods.

scholarly journals Semantic Segmentation of Urban Buildings from VHR Remote Sensing Imagery Using a Deep Convolutional Neural Network

2019 ◽  
Vol 11 (15) ◽  
pp. 1774 ◽  
Author(s):  
Yaning Yi ◽  
Zhijie Zhang ◽  
Wanchang Zhang ◽  
Chuanrong Zhang ◽  
Weidong Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Semantic Segmentation ◽  
Deep Convolutional Neural Network ◽  
The Other ◽  
Feature Maps ◽  
Remote Sensing Imagery ◽  
Urban Buildings ◽  
Sampling Network

Urban building segmentation is a prevalent research domain for very high resolution (VHR) remote sensing; however, various appearances and complicated background of VHR remote sensing imagery make accurate semantic segmentation of urban buildings a challenge in relevant applications. Following the basic architecture of U-Net, an end-to-end deep convolutional neural network (denoted as DeepResUnet) was proposed, which can effectively perform urban building segmentation at pixel scale from VHR imagery and generate accurate segmentation results. The method contains two sub-networks: One is a cascade down-sampling network for extracting feature maps of buildings from the VHR image, and the other is an up-sampling network for reconstructing those extracted feature maps back to the same size of the input VHR image. The deep residual learning approach was adopted to facilitate training in order to alleviate the degradation problem that often occurred in the model training process. The proposed DeepResUnet was tested with aerial images with a spatial resolution of 0.075 m and was compared in performance under the exact same conditions with six other state-of-the-art networks—FCN-8s, SegNet, DeconvNet, U-Net, ResUNet and DeepUNet. Results of extensive experiments indicated that the proposed DeepResUnet outperformed the other six existing networks in semantic segmentation of urban buildings in terms of visual and quantitative evaluation, especially in labeling irregular-shape and small-size buildings with higher accuracy and entirety. Compared with the U-Net, the F1 score, Kappa coefficient and overall accuracy of DeepResUnet were improved by 3.52%, 4.67% and 1.72%, respectively. Moreover, the proposed DeepResUnet required much fewer parameters than the U-Net, highlighting its significant improvement among U-Net applications. Nevertheless, the inference time of DeepResUnet is slightly longer than that of the U-Net, which is subject to further improvement.

scholarly journals ME-Net: A Deep Convolutional Neural Network for Extracting Mangrove Using Sentinel-2A Data

2021 ◽  
Vol 13 (7) ◽  
pp. 1292
Author(s):  
Mingqiang Guo ◽  
Zhongyang Yu ◽  
Yongyang Xu ◽  
Ying Huang ◽  
Chunfeng Li
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Discrimination Index ◽  
Classification Model ◽  
Pixel Classification ◽  
Remote Sensing Imagery ◽  
Boundary Fitting ◽  
Sentinel 2A

Mangroves play an important role in many aspects of ecosystem services. Mangroves should be accurately extracted from remote sensing imagery to dynamically map and monitor the mangrove distribution area. However, popular mangrove extraction methods, such as the object-oriented method, still have some defects for remote sensing imagery, such as being low-intelligence, time-consuming, and laborious. A pixel classification model inspired by deep learning technology was proposed to solve these problems. Three modules in the proposed model were designed to improve the model performance. A multiscale context embedding module was designed to extract multiscale context information. Location information was restored by the global attention module, and the boundary of the feature map was optimized by the boundary fitting unit. Remote sensing imagery and mangrove distribution ground truth labels obtained through visual interpretation were applied to build the dataset. Then, the dataset was used to train deep convolutional neural network (CNN) for extracting the mangrove. Finally, comparative experiments were conducted to prove the potential for mangrove extraction. We selected the Sentinel-2A remote sensing data acquired on 13 April 2018 in Hainan Dongzhaigang National Nature Reserve in China to conduct a group of experiments. After processing, the data exhibited 2093 × 2214 pixels, and a mangrove extraction dataset was generated. The dataset was made from Sentinel-2A satellite, which includes five original bands, namely R, G, B, NIR, and SWIR-1, and six multispectral indices, namely normalization difference vegetation index (NDVI), modified normalized difference water index (MNDWI), forest discrimination index (FDI), wetland forest index (WFI), mangrove discrimination index (MDI), and the first principal component (PCA1). The dataset has a total of 6400 images. Experimental results based on datasets show that the overall accuracy of the trained mangrove extraction network reaches 97.48%. Our method benefits from CNN and achieves a more accurate intersection and union ratio than other machine learning and pixel classification methods by analysis. The designed model global attention module, multiscale context embedding, and boundary fitting unit are helpful for mangrove extraction.

scholarly journals R-CNN-Based Ship Detection from High Resolution Remote Sensing Imagery

2019 ◽  
Vol 11 (6) ◽  
pp. 631 ◽  
Author(s):  
Shaoming Zhang ◽  
Ruize Wu ◽  
Kunyuan Xu ◽  
Jianmei Wang ◽  
Weiwei Sun
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Target Detection ◽  
Detection Methods ◽  
Optical Remote Sensing ◽  
Remote Sensing Imagery ◽  
Ship Detection ◽  
Inland River

Offshore and inland river ship detection has been studied on both synthetic aperture radar (SAR) and optical remote sensing imagery. However, the classic ship detection methods based on SAR images can cause a high false alarm ratio and be influenced by the sea surface model, especially on inland rivers and in offshore areas. The classic detection methods based on optical images do not perform well on small and gathering ships. This paper adopts the idea of deep networks and presents a fast regional-based convolutional neural network (R-CNN) method to detect ships from high-resolution remote sensing imagery. First, we choose GaoFen-2 optical remote sensing images with a resolution of 1 m and preprocess the images with a support vector machine (SVM) to divide the large detection area into small regions of interest (ROI) that may contain ships. Then, we apply ship detection algorithms based on a region-based convolutional neural network (R-CNN) on ROI images. To improve the detection result of small and gathering ships, we adopt an effective target detection framework, Faster-RCNN, and improve the structure of its original convolutional neural network (CNN), VGG16, by using multiresolution convolutional features and performing ROI pooling on a larger feature map in a region proposal network (RPN). Finally, we compare the most effective classic ship detection method, the deformable part model (DPM), another two widely used target detection frameworks, the single shot multibox detector (SSD) and YOLOv2, the original VGG16-based Faster-RCNN, and our improved Faster-RCNN. Experimental results show that our improved Faster-RCNN method achieves a higher recall and accuracy for small ships and gathering ships. Therefore, it provides a very effective method for offshore and inland river ship detection based on high-resolution remote sensing imagery.

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