HRCNet: High-Resolution Context Extraction Network for Semantic Segmentation of Remote Sensing Images

Semantic segmentation is a significant method in remote sensing image (RSIs) processing and has been widely used in various applications. Conventional convolutional neural network (CNN)-based semantic segmentation methods are likely to lose the spatial information in the feature extraction stage and usually pay little attention to global context information. Moreover, the imbalance of category scale and uncertain boundary information meanwhile exists in RSIs, which also brings a challenging problem to the semantic segmentation task. To overcome these problems, a high-resolution context extraction network (HRCNet) based on a high-resolution network (HRNet) is proposed in this paper. In this approach, the HRNet structure is adopted to keep the spatial information. Moreover, the light-weight dual attention (LDA) module is designed to obtain global context information in the feature extraction stage and the feature enhancement feature pyramid (FEFP) structure is promoted and employed to fuse the contextual information of different scales. In addition, to achieve the boundary information, we design the boundary aware (BA) module combined with the boundary aware loss (BAloss) function. The experimental results evaluated on Potsdam and Vaihingen datasets show that the proposed approach can significantly improve the boundary and segmentation performance up to 92.0% and 92.3% on overall accuracy scores, respectively. As a consequence, it is envisaged that the proposed HRCNet model will be an advantage in remote sensing images segmentation.

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DFFAN: Dual Function Feature Aggregation Network for Semantic Segmentation of Land Cover

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030125 ◽

2021 ◽

Vol 10 (3) ◽

pp. 125

Author(s):

Junqing Huang ◽

Liguo Weng ◽

Bingyu Chen ◽

Min Xia

Keyword(s):

Remote Sensing ◽

Land Cover ◽

Spatial Information ◽

Feature Fusion ◽

Semantic Segmentation ◽

Dual Function ◽

Context Information ◽

Remote Sensing Images ◽

Feature Aggregation ◽

Image Context

Analyzing land cover using remote sensing images has broad prospects, the precise segmentation of land cover is the key to the application of this technology. Nowadays, the Convolution Neural Network (CNN) is widely used in many image semantic segmentation tasks. However, existing CNN models often exhibit poor generalization ability and low segmentation accuracy when dealing with land cover segmentation tasks. To solve this problem, this paper proposes Dual Function Feature Aggregation Network (DFFAN). This method combines image context information, gathers image spatial information, and extracts and fuses features. DFFAN uses residual neural networks as backbone to obtain different dimensional feature information of remote sensing images through multiple downsamplings. This work designs Affinity Matrix Module (AMM) to obtain the context of each feature map and proposes Boundary Feature Fusion Module (BFF) to fuse the context information and spatial information of an image to determine the location distribution of each image’s category. Compared with existing methods, the proposed method is significantly improved in accuracy. Its mean intersection over union (MIoU) on the LandCover dataset reaches 84.81%.

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Water Areas Segmentation from Remote Sensing Images Using a Separable Residual SegNet Network

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9040256 ◽

2020 ◽

Vol 9 (4) ◽

pp. 256 ◽

Cited By ~ 1

Author(s):

Liguo Weng ◽

Yiming Xu ◽

Min Xia ◽

Yonghong Zhang ◽

Jia Liu ◽

...

Keyword(s):

Remote Sensing ◽

Feature Extraction ◽

Spatial Information ◽

Global Climate ◽

Semantic Segmentation ◽

Water Area ◽

Remote Sensing Images ◽

The One ◽

High Level ◽

Almost All

Changes on lakes and rivers are of great significance for the study of global climate change. Accurate segmentation of lakes and rivers is critical to the study of their changes. However, traditional water area segmentation methods almost all share the following deficiencies: high computational requirements, poor generalization performance, and low extraction accuracy. In recent years, semantic segmentation algorithms based on deep learning have been emerging. Addressing problems associated to a very large number of parameters, low accuracy, and network degradation during training process, this paper proposes a separable residual SegNet (SR-SegNet) to perform the water area segmentation using remote sensing images. On the one hand, without compromising the ability of feature extraction, the problem of network degradation is alleviated by adding modified residual blocks into the encoder, the number of parameters is limited by introducing depthwise separable convolutions, and the ability of feature extraction is improved by using dilated convolutions to expand the receptive field. On the other hand, SR-SegNet removes the convolution layers with relatively more convolution kernels in the encoding stage, and uses the cascading method to fuse the low-level and high-level features of the image. As a result, the whole network can obtain more spatial information. Experimental results show that the proposed method exhibits significant improvements over several traditional methods, including FCN, DeconvNet, and SegNet.

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Conditional Generative Adversarial Network-Based Training Sample Set Improvement Model for the Semantic Segmentation of High-Resolution Remote Sensing Images

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2020.3033816 ◽

2020 ◽

pp. 1-17

Author(s):

Xin Pan ◽

Jian Zhao ◽

Jun Xu

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Semantic Segmentation ◽

Training Sample ◽

Remote Sensing Images ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Sample Set

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Semantic Segmentation of High Resolution Remote Sensing Images with Extra Context Attention Mechanism

2020 IEEE 20th International Conference on Communication Technology (ICCT) ◽

10.1109/icct50939.2020.9295814 ◽

2020 ◽

Author(s):

Weifu Fu ◽

Qing Peng ◽

Yanxiang Gong ◽

Mei Xie ◽

Shicheng Wang ◽

...

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Semantic Segmentation ◽

Attention Mechanism ◽

Remote Sensing Images

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Efficient Patch-Wise Semantic Segmentation for Large-Scale Remote Sensing Images

Sensors ◽

10.3390/s18103232 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3232 ◽

Cited By ~ 17

Author(s):

Yan Liu ◽

Qirui Ren ◽

Jiahui Geng ◽

Meng Ding ◽

Jiangyun Li

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Large Scale ◽

Semantic Segmentation ◽

Remote Sensing Image ◽

Training Data ◽

Land Resources ◽

Remote Sensing Images ◽

Training Strategy ◽

The Impact

Efficient and accurate semantic segmentation is the key technique for automatic remote sensing image analysis. While there have been many segmentation methods based on traditional hand-craft feature extractors, it is still challenging to process high-resolution and large-scale remote sensing images. In this work, a novel patch-wise semantic segmentation method with a new training strategy based on fully convolutional networks is presented to segment common land resources. First, to handle the high-resolution image, the images are split as local patches and then a patch-wise network is built. Second, training data is preprocessed in several ways to meet the specific characteristics of remote sensing images, i.e., color imbalance, object rotation variations and lens distortion. Third, a multi-scale training strategy is developed to solve the severe scale variation problem. In addition, the impact of conditional random field (CRF) is studied to improve the precision. The proposed method was evaluated on a dataset collected from a capital city in West China with the Gaofen-2 satellite. The dataset contains ten common land resources (Grassland, Road, etc.). The experimental results show that the proposed algorithm achieves 54.96% in terms of mean intersection over union (MIoU) and outperforms other state-of-the-art methods in remote sensing image segmentation.

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ERN: Edge Loss Reinforced Semantic Segmentation Network for Remote Sensing Images

Remote Sensing ◽

10.3390/rs10091339 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1339 ◽

Cited By ~ 16

Author(s):

Shuo Liu ◽

Wenrui Ding ◽

Chunhui Liu ◽

Yu Liu ◽

Yufeng Wang ◽

...

Keyword(s):

Remote Sensing ◽

Edge Detection ◽

Qualitative Evaluation ◽

Semantic Segmentation ◽

Model Complexity ◽

Semantic Ambiguity ◽

Remote Sensing Images ◽

Future Networks ◽

Spatial Boundary ◽

Boundary Information

The semantic segmentation of remote sensing images faces two major challenges: high inter-class similarity and interference from ubiquitous shadows. In order to address these issues, we develop a novel edge loss reinforced semantic segmentation network (ERN) that leverages the spatial boundary context to reduce the semantic ambiguity. The main contributions of this paper are as follows: (1) we propose a novel end-to-end semantic segmentation network for remote sensing, which involves multiple weighted edge supervisions to retain spatial boundary information; (2) the main representations of the network are shared between the edge loss reinforced structures and semantic segmentation, which means that the ERN simultaneously achieves semantic segmentation and edge detection without significantly increasing the model complexity; and (3) we explore and discuss different ERN schemes to guide the design of future networks. Extensive experimental results on two remote sensing datasets demonstrate the effectiveness of our approach both in quantitative and qualitative evaluation. Specifically, the semantic segmentation performance in shadow-affected regions is significantly improved.

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