Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network

Fu, Gang; Liu, Changjun; Rong, Zhou; Sun, Tao; Zhang, Qijian

doi:10.3390/rs9050498

Cited by 300 publications

(201 citation statements)

References 34 publications

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“…We offer this hybrid DCNN-CRF approach to semantic segmentation as a simpler alternative to so-called 'fully convolutional' DCNNs [8,39,73] which, in order to achieve accurate pixel level classifications, require much larger, more sophisticated DCNN architectures [37], which are often computationally more demanding to train. Since pooling within the DCNN results in a significant loss of spatial resolution, these architectures require an additional set of convolutional layers that learn the 'upscaling' between the last pooling layer, which will be significantly smaller than the input image, and the pixelwise labelling at the required finer resolution.…”

Section: Discussionmentioning

confidence: 99%

Landscape Classification with Deep Neural Networks

Buscombe

Ritchie

2018

Geosciences

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Abstract:The application of deep learning, specifically deep convolutional neural networks (DCNNs), to the classification of remotely-sensed imagery of natural landscapes has the potential to greatly assist in the analysis and interpretation of geomorphic processes. However, the general usefulness of deep learning applied to conventional photographic imagery at a landscape scale is, at yet, largely unproven. If DCNN-based image classification is to gain wider application and acceptance within the geoscience community, demonstrable successes need to be coupled with accessible tools to retrain deep neural networks to discriminate landforms and land uses in landscape imagery. Here, we present an efficient approach to train/apply DCNNs with/on sets of photographic images, using a powerful graphical method called a conditional random field (CRF), to generate DCNN training and testing data using minimal manual supervision. We apply the method to several sets of images of natural landscapes, acquired from satellites, aircraft, unmanned aerial vehicles, and fixed camera installations. We synthesize our findings to examine the general effectiveness of transfer learning to landscape-scale image classification. Finally, we show how DCNN predictions on small regions of images might be used in conjunction with a CRF for highly accurate pixel-level classification of images.

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Section: Discussionmentioning

confidence: 99%

Landscape Classification with Deep Neural Networks

Buscombe

Ritchie

2018

Geosciences

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“…The automatic extraction of ground object information from remote sensing images is a hot topic in the field of remote sensing image analysis . With the continuous improvement of the spatial resolution of remote sensing images, the information extraction methods for remote sensing images are not satisfied with pixel-based and objectbased methods (Fu G, Liu C, Zhou R, et al 2017). People want to mine a higher level of semantic information from the image, and ground objects forms different semantic scene categories through different spatial distribute pattern (Bratasanu et al 2011;Lienou et al 2010).…”

Section: Introductionmentioning

confidence: 99%

A Novel Framework for Remote Sensing Image Scene Classification

Jiang¹,

Zhao²,

Wu³

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:High resolution remote sensing (HRRS) images scene classification aims to label an image with a specific semantic category. HRRS images contain more details of the ground objects and their spatial distribution patterns than low spatial resolution images. Scene classification can bridge the gap between low-level features and high-level semantics. It can be applied in urban planning, target detection and other fields. This paper proposes a novel framework for HRRS images scene classification. This framework combines the convolutional neural network (CNN) and XGBoost, which utilizes CNN as feature extractor and XGBoost as a classifier. Then, this framework is evaluated on two different HRRS images datasets: UC-Merced dataset and NWPU-RESISC45 dataset. Our framework achieved satisfying accuracies on two datasets, which is 95.57% and 83.35% respectively. From the experiments result, our framework has been proven to be effective for remote sensing images classification. Furthermore, we believe this framework will be more practical for further HRRS scene classification, since it costs less time on training stage.

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“…However, it is also a bottleneck to improving accuracy, due to variation of ocean environment when oil spill occurs. Therefore, learning features automatically from a remote sensing data set rather than using manually designed features, and then performing classification on the learned features, is an effective way to improve the accuracy of classification [47]. Deep learning theory was explicitly proposed by Hinton et al [48] in 2006.…”

Section: More Advanced Classifier Should Be Introduced or Developed Fmentioning

confidence: 99%

“…Compared with the traditional machine learning theories, the most significant difference of deep learning is emphasizing automatic feature learning from a huge data set through the organization of multi-layer neurons. In recent years, various deep learning architectures such as Deep Belief Networks (DBN) [50], Convolutional Neural Networks (CNN) [47], and Recurrent Neural Networks (RNN) [51] have been proposed and applied in speech, vision and image recognition and classification fields [52], they have been shown to produce state-of-the-art results in these domains, nevertheless, deep learning usually requires big data [53]. In deep learning techniques, CNN has achieved remarkable results in image classification, recognition, and other vision tasks [54][55][56][57].…”

Section: More Advanced Classifier Should Be Introduced or Developed Fmentioning

confidence: 99%

Ocean Oil Spill Classification with RADARSAT-2 SAR Based on an Optimized Wavelet Neural Network

Song

Ding

et al. 2017

Remote Sensing

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Oil spill accidents from ship or oil platform cause damage to marine and coastal environment and ecosystems. To monitor such spill events from space, fully polarimetric (Pol-SAR) synthetic aperture radar (SAR) has been greatly used in improving oil spill observation. Aiming to promote ocean oil spill classification accuracy, we developed a new oil spill identification method by combining multiple fully polarimetric SAR features data with an optimized wavelet neural network classifier (WNN). Two sets of RADARSAT-2 fully polarimetric SAR data are applied to test the validity of the developed method. The experimental results show that: (1) the convergence ability of optimized WNN can be enhanced, improving overall classification accuracy of ocean oil spill, in comparison to the classification results based on a common un-optimized WNN classifier; and (2) the joint use of the multiple fully Pol-SAR features as the inputs of the classifier can achieve better classification result than that only with single fully Pol-SAR feature. The developed method can improve classification accuracy by 4.96% and 7.75%, compared with the classification results with un-optimized WNN and only with one single fully polarimetric SAR feature. The classification overall accuracy based on the proposed approach can reach 97.67%. Experimental results have proven that the proposed approach is effective and applicable to classify the ocean oil spill.

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Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network

Cited by 300 publications

References 34 publications

Landscape Classification with Deep Neural Networks

Landscape Classification with Deep Neural Networks

A Novel Framework for Remote Sensing Image Scene Classification

Ocean Oil Spill Classification with RADARSAT-2 SAR Based on an Optimized Wavelet Neural Network

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