Journal ArticleDOI
Stacked Sparse Autoencoder in PolSAR Data Classification Using Local Spatial Information
Lu Zhang,Wenping Ma,Dan Zhang +2 more
TLDR
A novel framework is proposed to learn robust features of PolSAR data using the stacked sparse autoencoder (SSAE) to learn the deep spatial sparse features automatically for the first time.Abstract:
Terrain classification is an important topic in polarimetric synthetic aperture radar (PolSAR) image processing. Among various classification techniques, the stacked sparse autoencoder (SSAE) is a kind of deep learning method that can automatically learn useful features layer by layer in an unsupervised manner. However, the scattering measurements of individual pixels in PolSAR images are affected by the speckle; hence, the performance of pixel-based classification approaches would be poor. In this situation, a novel framework is proposed to learn robust features of PolSAR data. The local spatial information is introduced into SSAE to learn the deep spatial sparse features automatically for the first time. Furthermore, the influences of the neighbor pixels on the central pixel are controlled depending on the spatial distances from the neighbor pixels to the central pixel. Experimental results with fully PolSAR data indicate that the proposed method provides a competitive solution.read more
Citations
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Journal ArticleDOI
Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
Xiao Xiang Zhu,Devis Tuia,Lichao Mou,Gui-Song Xia,Liangpei Zhang,Feng Xu,Friedrich Fraundorfer +6 more
TL;DR: The challenges of using deep learning for remote-sensing data analysis are analyzed, recent advances are reviewed, and resources are provided that hope will make deep learning in remote sensing seem ridiculously simple.
Journal ArticleDOI
Deep learning in environmental remote sensing: Achievements and challenges
Qiangqiang Yuan,Huanfeng Shen,Tongwen Li,Zhiwei Li,Shuwen Li,Yun Jiang,Hongzhang Xu,Weiwei Tan,Qianqian Yang,Jiwen Wang,Jianhao Gao,Liangpei Zhang +11 more
TL;DR: The potential of DL in environmental remote sensing, including land cover mapping, environmental parameter retrieval, data fusion and downscaling, and information reconstruction and prediction, will be analyzed and a typical network structure will be introduced.
Journal ArticleDOI
Deep learning in remote sensing: a review
Xiao Xiang Zhu,Devis Tuia,Lichao Mou,Gui-Song Xia,Liangpei Zhang,Feng Xu,Friedrich Fraundorfer +6 more
TL;DR: In this article, the authors analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with.
Journal ArticleDOI
SAR Image segmentation based on convolutional-wavelet neural network and markov random field
TL;DR: In this approach, a wavelet constrained pooling layer is designed to replace the conventional pooling in CNN and the new architecture can suppress the noise and is better at keeping the structures of the learned features, which are crucial to the segmentation tasks.
Journal ArticleDOI
A Review of the Autoencoder and Its Variants: A Comparative Perspective from Target Recognition in Synthetic-Aperture Radar Images
TL;DR: The milestone work done by Hinton and Salakhutdinov proposes to initialize the weights that allow deep autoencoder networks to learn lowdimensional codes, and the encoding trick introduced works much better than principal component analysis (PCA) in terms of dimension reduction.
References
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Book
Learning Deep Architectures for AI
TL;DR: The motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer modelssuch as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks are discussed.
Journal ArticleDOI
Sparse Coding with an Overcomplete Basis Set: A Strategy Employed by V1 ?
TL;DR: These deviations from linearity provide a potential explanation for the weak forms of non-linearity observed in the response properties of cortical simple cells, and they further make predictions about the expected interactions among units in response to naturalistic stimuli.
Journal ArticleDOI
Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution
TL;DR: A maximum likelihood classifier to segment polarimetric SAR data according to terrain types has been developed based on the Wishart distribution, which can be applied to multifrequency multi-look polarimetry SAR data, as well as 10 SAR data containing only intensity information.
Journal ArticleDOI
Region-Based Classification of Polarimetric SAR Images Using Wishart MRF
TL;DR: A novel classification method, taking regions as elements, is proposed using a Markov random field (MRF), using a Wishart-based maximum likelihood, based on regions, to obtain a classification map.
Proceedings ArticleDOI
Support vector machine classification of land cover: application to polarimetric SAR data
S. Fukuda,H. Hirosawa +1 more
TL;DR: This paper addresses a novel SVM-based classification scheme of land cover from polarimetric synthetic aperture radar (SAR) data and discusses some important properties of SVMs, for example the relation between the number of support vectors and classification accuracy.
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