A Novel Sea-Land Segmentation Algorithm Based on Local Binary Patterns for Ship Detection

Xia, Yu; Wan, Shouhong; Jin, Peiquan; Yue, Lihua

doi:10.14257/ijsip.2014.7.3.19

Cited by 23 publications

(30 citation statements)

References 15 publications

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“…For instance, Ma et al [4] proposed an algorithm by combining a modified Otsu's method with homogeneous textures and intensity features. Xia et al [3] proposed a model, in which the original gray-level hyperspectral image is combined with the texture features extracted from local binary patterns to generate the combined feature map. Liu and Jezek [19] and Li et al [20] presented thresholding algorithms to separate water areas from land areas.…”

Section: Related Workmentioning

confidence: 99%

“…Continuous efforts have been made in the field. For instance, contrast to traditional thresholding segmentation models, Xia et al [3] presented a model in which gray intensity features and local binary pattern features are combined. Ma et al [4] and Liu et al [33] presented a sea-land segmentation hierarchical model which reduced the computational costs.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Deep Structure U-Net for Sea-Land Segmentation in Remote Sensing Images

Shamsolmoali

Zareapoor

Wang

et al. 2019

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

100

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Sea-land segmentation is an important process for many key applications in remote sensing. Proper operative sealand segmentation for remote sensing images remains a challenging issue due to complex and diverse transition between sea and lands. Although several Convolutional Neural Networks (CNNs) have been developed for sea-land segmentation, the performance of these CNNs is far from the expected target. This paper presents a novel deep neural network structure for pixelwise sea-land segmentation, a Residual Dense U-Net (RDU-Net), in complex and high-density remote sensing images. RDU-Net is a combination of both downsampling and upsampling paths to achieve satisfactory results. In each down-and up-sampling path, in addition to the convolution layers, several densely connected residual network blocks are proposed to systematically aggregate multi-scale contextual information. Each dense network block contains multilevel convolution layers, short-range connections and an identity mapping connection which facilitates features reuse in the network and makes full use of the hierarchical features from the original images. These proposed blocks have a certain number of connections that are designed with shorter distance backpropagation between the layers and can significantly improve segmentation results whilst minimizing computational costs. We have performed extensive experiments on two real datasets Google-Earth and ISPRS and compare the proposed RDU-Net against several variations of Dense Networks. The experimental results show that RDU-Net outperforms the other state-of-the-art approaches on the sea-land segmentation tasks.Index Term-Deep neural network (DNN), dense network (DenseNet), remote sensing images, sea-land segmentation, U-Net.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Deep Structure U-Net for Sea-Land Segmentation in Remote Sensing Images

Shamsolmoali

Zareapoor

Wang

et al. 2019

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

100

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show abstract

“…This section discussed the experiments to test our results under different complex background conditions and analyze our method by comparing the results with those of four approaches: ME [1], T [6], LBP [11] based algorithms, and the method in [13], which is referred to as ATI for short in this paper. The former three methods are state-of-the-art methods designed using different perspectives.…”

Section: Comparison With State Of the Artmentioning

confidence: 99%

“…Dai et al [10] introduced a multilevel local pattern histogram (MLPH) to classify water area class from TerraSAR-X images. Xia et al [11] used local binary patterns (LBP) features to obtain the integrated feature map for sealand segmentation.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Sea‐Land Segmentation for Panchromatic Remote Sensing Imagery

Soomro

Shen

et al. 2017

Mathematical Problems in Engineering

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Automatic sea-land segmentation is an essential and challenging field for the practical use of panchromatic satellite imagery. Owing to the temporal variations as well as the complex and inconsistent intensity contrast in both land and sea areas, it is difficult to generate an accurate segmentation result by using the conventional thresholding methods. Additionally, the freely available digital elevation model (DEM) also difficultly meets the requirements of high-resolution data for practical usage, because of the low precision and high memory storage costs for the processing systems. In this case, we proposed a fully automatic sea-land segmentation approach for practical use with a hierarchical coarse-to-fine procedure. We compared our method with other state-ofthe-art methods with real images under complex backgrounds and conducted quantitative comparisons. The experimental results show that our method outperforms all other methods and proved being computationally efficient.

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“…Therefore, we follow object segmentation methods to study refined water body extraction from panchromatic remote sensing images. In object segmentation-based methods [30][31][32][33][34][35][36][37][38][39], thresholding is widely used for water body extraction based on gray level histograms. Figure 1 provides two kinds of panchromatic water-land scenes and their gray level histograms.…”

Section: Introductionmentioning

confidence: 99%

Fusion Feature Multi-Scale Pooling for Water Body Extraction from Optical Panchromatic Images

Zhuang

Chen

et al. 2019

Remote Sensing

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Water body extraction is a hot research topic in remote sensing applications. Using panchromatic optical remote sensing images to extract water bodies is a challenging task, because these images have one level of gray information, variable imaging conditions, and complex scene information. Refined water body extraction from optical panchromatic images often experiences serious under- or over- segmentation problems. In this paper, for producing refined water body extraction results from optical panchromatic images, we propose a fusion feature multi-scale pooling for Markov modeling method. Markov modeling includes two aspects: label field initialization and feature field establishment. These two aspects are jointly created by the fusion feature multi-scale pooling process, and this process is proposed to enhance the feature difference between water bodies and land cover. Then, the greedy algorithm in the iteration conditional method is used to extract refined water bodies according to the rebuilt Markov initial label and feature fields. Finally, to prove the effectiveness of proposed method, extensive experiments were used with collected 2.5m SPOT 5 and 1m GF-2 optical panchromatic images and evaluation indexes (precision, recall, overall accuracy, kappa coefficient and boundary detection ratios) to demonstrate that our proposed method can produce more refined water body extraction results than the state-of-the-art methods. The global and local refined indexes are improved by about 7% and 10%, respectively.

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A Novel Sea-Land Segmentation Algorithm Based on Local Binary Patterns for Ship Detection

Abstract: Ship detection is an important application of optical remote sensing image processing. Sea-land

Cited by 23 publications

References 15 publications

A Novel Deep Structure U-Net for Sea-Land Segmentation in Remote Sensing Images

A Novel Deep Structure U-Net for Sea-Land Segmentation in Remote Sensing Images

Hierarchical Sea‐Land Segmentation for Panchromatic Remote Sensing Imagery

Fusion Feature Multi-Scale Pooling for Water Body Extraction from Optical Panchromatic Images

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