Hierarchical Terrain Classification Based on Multilayer Bayesian Network and Conditional Random Field

He, Chu; Fan, Haosen; Feng, Da‐Zheng; Shi, Bo; Luo, Bin; Liao, Mingsheng

doi:10.3390/rs9010096

Cited by 9 publications

(8 citation statements)

References 34 publications

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“…For example, Ronny Hansch [8] used complex neural network to realize PolSAR data learning and classification. He [9] achieved hierarchical terrain classification based on bayesian network and conditional random field in 2017. However, there are essential differences between the imaging mechanism of optical image and SAR imagery.…”

Section: Sar Imagery Feature Extractionmentioning

confidence: 99%

“…On the other hand, attribute information in PolSAR imagery is quite precise and complex, manual features can not automatically adapt to data itself. Lots of work have confirmed that feature learning is superior to traditional methods for PolSAR image interpretation [9,24]. Feature learning methods complete underlying feature abstraction through network layers iteratively, so as to learn data's essential information.…”

Section: Problems and Motivationmentioning

confidence: 99%

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Nonlinear Manifold Learning Integrated with Fully Convolutional Networks for PolSAR Image Classification

Xiong

et al. 2020

Remote Sensing

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Synthetic Aperture Rradar (SAR) provides rich ground information for remote sensing survey and can be used all time and in all weather conditions. Polarimetric SAR (PolSAR) can further reveal surface scattering difference and improve radar’s application ability. Most existing classification methods for PolSAR imagery are based on manual features, such methods with fixed pattern has poor data adaptability and low feature utilization, if directly input to the classifier. Therefore, combining PolSAR data characteristics and deep network with auto-feature learning ability forms a new breakthrough direction. In fact, feature learning of deep network is to realize function approximation from data to label, through multi-layer accumulation, but finite layers limit the network’s mapping ability. According to manifold hypothesis, high-dimensional data exists in potential low-dimensional manifold and different types of data locates in different manifolds. Manifold learning can model core variables of the target, and separate different data’s manifold as much as possible, so as to complete data classification better. Therefore, taking manifold hypothesis as a starting point, nonlinear manifold learning integrated with fully convolutional networks for PolSAR image classification method is proposed in this paper. Firstly, high-dimensional polarized features are extracted based on scattering matrix and coherence matrix of original PolSAR data, whose compact representation is mined by manifold learning. Meanwhile, drawing on transfer learning, pre-trained Fully Convolutional Networks (FCN) model is utilized to learn deep spatial features of PolSAR imagery. Considering complementary advantages, weighted strategy is adopted to embed manifold representation into deep spatial features, which are input into support vector machine (SVM) classifier for final classification. A series of experiments on three PolSAR datasets have verified effectiveness and superiority of the proposed classification algorithm.

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Section: Sar Imagery Feature Extractionmentioning

confidence: 99%

Section: Problems and Motivationmentioning

confidence: 99%

Nonlinear Manifold Learning Integrated with Fully Convolutional Networks for PolSAR Image Classification

Xiong

et al. 2020

Remote Sensing

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“…should be concerned, hence an mount of region-based methods have been developed. A common way is to use the Markov random field and conditional random field to model the spatial interactions [9][10][11], or segment data into homogeneous objects [12]. In addition, some researchers also undertake to combine the neural network models with superpixel segmentation to remedy this matter [13,14].…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Fully Convolutional Network Integrated with Sparse and Low-Rank Subspace Representations for PolSAR Imagery Classification

Wang

Fan

et al. 2018

Remote Sensing

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Inspired by enormous success of fully convolutional network (FCN) in semantic segmentation, as well as the similarity between semantic segmentation and pixel-by-pixel polarimetric synthetic aperture radar (PolSAR) image classification, exploring how to effectively combine the unique polarimetric properties with FCN is a promising attempt at PolSAR image classification. Moreover, recent research shows that sparse and low-rank representations can convey valuable information for classification purposes. Therefore, this paper presents an effective PolSAR image classification scheme, which integrates deep spatial patterns learned automatically by FCN with sparse and low-rank subspace features: (1) a shallow subspace learning based on sparse and low-rank graph embedding is firstly introduced to capture the local and global structures of high-dimensional polarimetric data; (2) a pre-trained deep FCN-8s model is transferred to extract the nonlinear deep multi-scale spatial information of PolSAR image; and (3) the shallow sparse and low-rank subspace features are integrated to boost the discrimination of deep spatial features. Then, the integrated hierarchical subspace features are used for subsequent classification combined with a discriminative model. Extensive experiments on three pieces of real PolSAR data indicate that the proposed method can achieve competitive performance, particularly in the case where the available training samples are limited.

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“…The final result will emphasize the color that most represents the terrain type (eg, blue for water). Additionally, frequency domain [7,8], segmentation [6,9,10], bayesian network [11], and Hyperspectal Images [12] can also be used in terrain classification.Other types of sensors such as LiDAR [13][14][15][16] can complement the classification decision. Algorithms that use laser scanners proved to be qualified to accurately distinguish between water and non-water terrains [13][14][15][16].…”

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confidence: 99%

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confidence: 99%

Static and Dynamic Algorithms for Terrain Classification in UAV Aerial Imagery

et al. 2019

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The ability to precisely classify different types of terrain is extremely important for Unmanned Aerial Vehicles (UAVs). There are multiple situations in which terrain classification is fundamental for achieving a UAV's mission success, such as emergency landing, aerial mapping, decision making, and cooperation between UAVs in autonomous navigation. Previous research works describe different terrain classification approaches mainly using static features from RGB images taken onboard UAVs. In these works, the terrain is classified from each image taken as a whole, not divided into blocks; this approach has an obvious drawback when applied to images with multiple terrain types. This paper proposes a robust computer vision system to classify terrain types using three main algorithms, which extract features from UAV's downwash effect: Static textures-Gray-Level Co-Occurrence Matrix (GLCM), Gray-Level Run Length Matrix (GLRLM) and Dynamic textures-Optical Flow method. This system has been fully implemented using the OpenCV library, and the GLCM algorithm has also been partially specified in a Hardware Description Language (VHDL) and implemented in a Field Programmable Gate Array (FPGA)-based platform. In addition to these feature extraction algorithms, a neural network was designed with the aim of classifying the terrain into one of four classes. Lastly, in order to store and access all the classified terrain information, a dynamic map, with this information was generated. The system was validated using videos acquired onboard a UAV with an RGB camera.Several methods for terrain classification have been proposed in different works. Texture algorithms, such as those proposed in [2][3][4][5], have been widely recommended to emphasize the high and low frequencies of the images, supporting image classification. Other algorithms use color information to classify terrains, such as presented in [6], which is able to distinguish four different terrain types within an image. During this process, each channel's pixel is divided by the square root of its own three channels intensity. The final result will emphasize the color that most represents the terrain type (eg, blue for water). Additionally, frequency domain [7,8], segmentation [6,9,10], bayesian network [11], and Hyperspectal Images [12] can also be used in terrain classification.Other types of sensors such as LiDAR [13][14][15][16] can complement the classification decision. Algorithms that use laser scanners proved to be qualified to accurately distinguish between water and non-water terrains [13][14][15][16]. However, shallow water terrains increase the decision error due to laser reflection, which leads to a misclassification as non-water terrain.Although prior research work has proposed many good solutions for terrain classification, there is still a gap regarding the study of dynamic terrain. The previously mentioned algorithms suffer from a high sensitivity to changes in the environment, mainly due to changes in brightness, color and texture. Recent works [17,18] ...

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Hierarchical Terrain Classification Based on Multilayer Bayesian Network and Conditional Random Field

Cited by 9 publications

References 34 publications

Nonlinear Manifold Learning Integrated with Fully Convolutional Networks for PolSAR Image Classification

Nonlinear Manifold Learning Integrated with Fully Convolutional Networks for PolSAR Image Classification

A Hierarchical Fully Convolutional Network Integrated with Sparse and Low-Rank Subspace Representations for PolSAR Imagery Classification

Static and Dynamic Algorithms for Terrain Classification in UAV Aerial Imagery

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