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Journal ArticleDOI: 10.1080/17538947.2020.1831087

Incorporating DeepLabv3+ and object-based image analysis for semantic segmentation of very high resolution remote sensing images

04 Mar 2021-International Journal of Digital Earth (Taylor & Francis)-Vol. 14, Iss: 3, pp 357-378
Abstract: Semantic segmentation of remote sensing images is an important but unsolved problem in the remote sensing society. Advanced image semantic segmentation models, such as DeepLabv3+, have achieved ast...

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Topics: Segmentation (53%)
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8 results found


Open accessJournal ArticleDOI: 10.3390/RS13040808
23 Feb 2021-Remote Sensing
Abstract: Availability of very high-resolution remote sensing images and advancement of deep learning methods have shifted the paradigm of image classification from pixel-based and object-based methods to deep learning-based semantic segmentation. This shift demands a structured analysis and revision of the current status on the research domain of deep learning-based semantic segmentation. The focus of this paper is on urban remote sensing images. We review and perform a meta-analysis to juxtapose recent papers in terms of research problems, data source, data preparation methods including pre-processing and augmentation techniques, training details on architectures, backbones, frameworks, optimizers, loss functions and other hyper-parameters and performance comparison. Our detailed review and meta-analysis show that deep learning not only outperforms traditional methods in terms of accuracy, but also addresses several challenges previously faced. Further, we provide future directions of research in this domain.

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4 Citations


Open accessJournal ArticleDOI: 10.1016/J.NEUCOM.2021.03.066
Zheng Tong, Philippe Xu, Thierry Denoeux1Institutions (1)
25 Aug 2021-Neurocomputing
Abstract: We propose a new classifier based on Dempster-Shafer (DS) theory and a convolutional neural network (CNN) architecture for set-valued classification. In this classifier, called the evidential deep-learning classifier, convolutional and pooling layers first extract high-dimensional features from input data. The features are then converted into mass functions and aggregated by Dempster’s rule in a DS layer. Finally, an expected utility layer performs set-valued classification based on mass functions. We propose an end-to-end learning strategy for jointly updating the network parameters. Additionally, an approach for selecting partial multi-class acts is proposed. Experiments on image recognition, signal processing, and semantic-relationship classification tasks demonstrate that the proposed combination of deep CNN, DS layer, and expected utility layer makes it possible to improve classification accuracy and to make cautious decisions by assigning confusing patterns to multi-class sets.

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Topics: Deep learning (58%), Convolutional neural network (57%), Dempster–Shafer theory (55%) ... show more

3 Citations


Open accessJournal ArticleDOI: 10.3390/APP11125551
15 Jun 2021-Applied Sciences
Abstract: Depletion of natural resources, population growth, urban migration, and expanding drought conditions are some of the reasons why environmental monitoring programs are required and regularly produced and updated. Additionally, the usage of artificial intelligence in the geospatial field of Earth observation (EO) and regional land monitoring missions is a challenging issue. In this study, land cover and land use mapping was performed using the proposed CNN–MRS model. The CNN–MRS model consisted of two main steps: CNN-based land cover classification and enhancing the classification with spatial filter and multiresolution segmentation (MRS). Different band numbers of Sentinel-2A imagery and multiple patch sizes (32 × 32, 64 × 64, and 128 × 128 pixels) were used in the first experiment. The algorithms were evaluated in terms of overall accuracy, precision, recall, F1-score, and kappa coefficient. The highest overall accuracy was obtained with the proposed approach as 97.31% in Istanbul test site area and 98.44% in Kocaeli test site area. The accuracies revealed the efficiency of the CNN–MRS model for land cover map production in large areas. The McNemar test measured the significance of the models used. In the second experiment, with the Zurich Summer dataset, the overall accuracy of the proposed approach was obtained as 92.03%. The results are compared quantitatively with state-of-the-art CNN model results and related works.

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Topics: Land cover (62%)

2 Citations


Open accessJournal ArticleDOI: 10.1109/JSTARS.2021.3078631
Ning Zang1, Yun Cao1, Yuebin Wang1, Bo Huang2  +2 moreInstitutions (4)
Abstract: Land-use mapping (LUM) using high-spatial resolution remote sensing images (HSR-RSIs) is a challenging and crucial technology. However, due to the characteristics of HSR-RSIs, such as different image acquisition conditions and massive, detailed information, and performing LUM faces unique scientific challenges. With the emergence of new deep learning (DL) algorithms in recent years, methods to LUM with DL have achieved huge breakthroughs, which offer novel opportunities for the development of LUM for HSR-RSIs. This article aims to provide a thorough review of recent achievements in this field. Existing high spatial resolution datasets in the research of semantic segmentation and single-object segmentation are presented first. Next, we introduce several basic DL approaches that are frequently adopted for LUM. After reviewing DL-based LUM methods comprehensively, which highlights the contributions of researchers in the field of LUM for HSR-RSIs, we summarize these DL-based approaches based on two LUM criteria. Individually, the first one has supervised learning, semisupervised learning, or unsupervised learning, while another one is pixel-based or object-based. We then briefly review the fundamentals and the developments of the development of semantic segmentation and single-object segmentation. At last, quantitative results that experiment on the dataset of ISPRS Vaihingen and ISPRS Potsdam are given for several representative models such as fully convolutional network (FCN) and U-Net, following up with a comparison and discussion of the results.

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1 Citations


Open accessJournal ArticleDOI: 10.1109/ACCESS.2021.3122162
Liyuan Li1, Xiaoyan Li1, Xin Liu1, Wenwen Huang1  +2 moreInstitutions (1)
22 Oct 2021-IEEE Access
Abstract: Semantic segmentation (SS) has been widely applied for cloud detection (CD) in remote sensing images (RSIs) with high spatial and spectral resolution because of its effective pixel-level feature extraction structure. However, the typical model of lightweight SS, namely the fully convolutional network (FCN) with only seven layers, has difficulty in extracting high-level features, and the heavy pyramid scene parsing network (PSPNet) with complicated calculations is not practical in real-time CD, let alone on-orbit CD. So, in view of the problems above, we propose a compact attention mechanism cloud detection network (AM-CDN) based on the modified FCN to refine and fuse the multi-scale features for on-orbit CD. Specifically, taking the FCN as the baseline, our model increases the numbers of hidden layers and adds the residual connections between the input and output to eliminate the network degradation and extract the advanced context feature maps effectively. To expand the receptive field without losing the spatial information, the ordinary convolutions in FCN are replaced by the dilated convolution in AM-CDN. And inspired by the selective kernels of human vision, we introduce the convolutional attention mechanism (AM) into the encoder to adaptively adjust the receptive field to highlight the key texture features. According to experimental results using Landsat-8 infrared RSIs, the accuracy of the proposed CD method is 95.31%, which is 10.17% higher than that of FCN. And the calculation complexity of AM-CDN is only 7.63% of that of PSPNet.

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References
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34 results found


Journal ArticleDOI: 10.1038/NATURE14539
Yann LeCun1, Yann LeCun2, Yoshua Bengio3, Geoffrey E. Hinton4  +1 moreInstitutions (5)
28 May 2015-Nature
Abstract: Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

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33,931 Citations


Open accessBook ChapterDOI: 10.1007/978-3-319-24574-4_28
05 Oct 2015-
Abstract: There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net .

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Topics: Brain segmentation (57%), Deep learning (54%), Segmentation (50%)

28,273 Citations


Open accessBook
18 Nov 2016-
Abstract: Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

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Topics: Feature learning (61%), Deep learning (59%), Approximate inference (51%) ... show more

26,972 Citations


Open accessPosted Content
Abstract: There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at this http URL .

... read more

Topics: Segmentation (50%)

19,534 Citations


Open accessProceedings ArticleDOI: 10.1109/CVPR.2015.7298965
07 Jun 2015-
Abstract: Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segmentation. Our key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet [20], the VGG net [31], and GoogLeNet [32]) into fully convolutional networks and transfer their learned representations by fine-tuning [3] to the segmentation task. We then define a skip architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional network achieves state-of-the-art segmentation of PASCAL VOC (20% relative improvement to 62.2% mean IU on 2012), NYUDv2, and SIFT Flow, while inference takes less than one fifth of a second for a typical image.

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Topics: Scale-space segmentation (55%)

18,335 Citations


Performance
Metrics
No. of citations received by the Paper in previous years
YearCitations
20221
20217