Fully Convolutional Network With Task Partitioning for Inshore Ship Detection in Optical Remote Sensing Images

Lin, Haoning; Shi, Zhenwei; Zou, Zhengxia

doi:10.1109/lgrs.2017.2727515

Cited by 120 publications

(59 citation statements)

References 18 publications

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“…More recently, with the idea of learning theory, Refs. [21][22][23][24][25] proposed to learn high-level features automatically for classification tasks.…”

Section: Related Workmentioning

confidence: 99%

“…Here, the semi-supervised method is used for combining the labeled and unlabeled pixels for a better classification result in the light network. With excellent classification performance, the method proposed for the imbalanced PolSAR images is used as shown in our preliminary work [24]. The cost-sensitive latent space learning network is built based on the parametric feature and classifier learning framework for imbalanced PolSAR images, where classifier and latent space learning are defined as optimizing the posterior and likelihood function for labeled pixels, respectively.…”

Section: Completion Of Label Matrix By Matrix Completionmentioning

confidence: 99%

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Structure Label Matrix Completion for PolSAR Image Classification

Hou

Wen

et al. 2020

Remote Sensing

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Terrain classification is a hot topic in polarimetric synthetic aperture radar (PolSAR) image interpretation that aims at assigning a label to every pixel and forms a label matrix for a PolSAR image. From the perspective of human interpretation, classification is not in terms of pixels, but decomposed perceptual groups and structures. Therefore, a new perspective of label matrix completion is proposed to treat the classification task as a label matrix inversion process. Firstly, a matrix completion framework is built to avoid processing the large-scale PolSAR image data for traditional feature and classifier learning. Secondly, a light network is used to obtain the known labels for the complete task by uniform down-sampling of the entire image that aims to keep the shape of regions in a PolSAR image and reduce the computational complexity. Finally, the zeroth-and first-order label information is proposed as the prior distribution for label matrix completion to keep the structure and texture. The experiments are tested on real PolSAR images, which demonstrates that the proposed method can realize excellent classification results with less computation time and labeled pixels. Classification results with different down-sampling rates in the light network also prove the robustness of this method.

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“…More recently, with the idea of learning theory, Refs. [21][22][23][24][25] proposed to learn high-level features automatically for classification tasks.…”

Section: Related Workmentioning

confidence: 99%

Section: Completion Of Label Matrix By Matrix Completionmentioning

confidence: 99%

Structure Label Matrix Completion for PolSAR Image Classification

Hou

Wen

et al. 2020

Remote Sensing

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“…FCN has greatly increased the processing flexibility and computational efficiency, and the image-to-image mapping process is naturally suitable for the pixel-wise image labeling tasks. In remote sensing image interpretation fields, such tasks include land structure segmentation, sea-land segmentation and others [21][22][23]. However, raft labeling is different from the above labeling problems, where there is a huge difference between the semantic scales between them.…”

Section: Introductionmentioning

confidence: 99%

Automatic Raft Labeling for Remote Sensing Images via Dual-Scale Homogeneous Convolutional Neural Network

Shi

Zou

et al. 2018

Remote Sensing

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Raft-culture is a way of utilizing water for farming aquatic product. Automatic raft-culture monitoring by remote sensing technique is an important way to control the crop's growth and implement effective management. This paper presents an automatic pixel-wise raft labeling method based on fully convolutional network (FCN). As rafts are always tiny and neatly arranged in images, traditional FCN method fails to extract the clear boundary and other detailed information. Therefore, a homogeneous convolutional neural network (HCN) is designed, which only consists of convolutions and activations to retain all details. We further design a dual-scale structure (DS-HCN) to integrate higher-level contextual information for accomplishing sea-land segmentation and raft labeling at the same time in a uniform framework. A dataset with Gaofen-1 satellite images was collected to verify the effectiveness of our method. DS-HCN shows a satisfactory performance with a better interpretability and a more accurate labeling result.

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“…Similar to the related works [8]- [12], the RSIs used in this paper were collected from Google Earth with the spatial resolution ranging from 0.5m to 2m. Compared with the objects in nature scene images, the objects in RSIs (such as airports, buildings, and ships) usually have many different orientations, scales, and types since the RSIs are taken overhead [13], [14].…”

mentioning

confidence: 99%

Nested Network With Two-Stream Pyramid for Salient Object Detection in Optical Remote Sensing Images

Cong

Hou

et al. 2019

IEEE Trans. Geosci. Remote Sensing

232

123

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Arising from the various object types and scales, diverse imaging orientations, and cluttered backgrounds in optical remote sensing image (RSI), it is difficult to directly extend the success of salient object detection for nature scene image to the optical RSI. In this paper, we propose an end-to-end deep network called LV-Net based on the shape of network architecture, which detects salient objects from optical RSIs in a purely data-driven fashion. The proposed LV-Net consists of two key modules, i.e., a two-stream pyramid module (L-shaped module) and an encoder-decoder module with nested connections (V-shaped module). Specifically, the L-shaped module extracts a set of complementary information hierarchically by using a twostream pyramid structure, which is beneficial to perceiving the diverse scales and local details of salient objects. The V-shaped module gradually integrates encoder detail features with decoder semantic features through nested connections, which aims at suppressing the cluttered backgrounds and highlighting the salient objects. In addition, we construct the first publicly available optical RSI dataset for salient object detection, including 800 images with varying spatial resolutions, diverse saliency types, and pixel-wise ground truth. Experiments on this benchmark dataset demonstrate that the proposed method outperforms the state-of-the-art salient object detection methods both qualitatively and quantitatively.

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Fully Convolutional Network With Task Partitioning for Inshore Ship Detection in Optical Remote Sensing Images

Cited by 120 publications

References 18 publications

Structure Label Matrix Completion for PolSAR Image Classification

Structure Label Matrix Completion for PolSAR Image Classification

Automatic Raft Labeling for Remote Sensing Images via Dual-Scale Homogeneous Convolutional Neural Network

Nested Network With Two-Stream Pyramid for Salient Object Detection in Optical Remote Sensing Images

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