Dual Attention Feature Fusion and Adaptive Context for Accurate Segmentation of Very High-Resolution Remote Sensing Images

Land cover classification of high-resolution remote sensing images aims to obtain pixel-level land cover understanding, which is often modeled as semantic segmentation of remote sensing images. In recent years, convolutional network (CNN)-based land cover classification methods have achieved great advancement. However, previous methods fail to generate fine segmentation results, especially for the object boundary pixels. In order to obtain boundary-preserving predictions, we first propose to incorporate spatially adapting contextual cues. In this way, objects with similar appearance can be effectively distinguished with the extracted global contextual cues, which are very helpful to identify pixels near object boundaries. On this basis, low-level spatial details and high-level semantic cues are effectively fused with the help of our proposed dual attention mechanism. Concretely, when fusing multi-level features, we utilize the dual attention feature fusion module based on both spatial and channel attention mechanisms to relieve the influence of the large gap, and further improve the segmentation accuracy of pixels near object boundaries. Extensive experiments were carried out on the ISPRS 2D Semantic Labeling Vaihingen data and GaoFen-2 data to demonstrate the effectiveness of our proposed method. Our method achieves better performance compared with other state-of-the-art methods.

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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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Urban Land Cover Classification of High-Resolution Aerial Imagery Using a Relation-Enhanced Multiscale Convolutional Network

Remote Sensing ◽

10.3390/rs12020311 ◽

2020 ◽

Vol 12 (2) ◽

pp. 311 ◽

Cited By ~ 6

Author(s):

Chun Liu ◽

Doudou Zeng ◽

Hangbin Wu ◽

Yin Wang ◽

Shoujun Jia ◽

...

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Land Cover ◽

Land Cover Classification ◽

Urban Land ◽

Small Sample ◽

Connectivity Pattern ◽

Feature Maps ◽

Convolutional Network ◽

Urban Land Cover

Urban land cover classification for high-resolution images is a fundamental yet challenging task in remote sensing image analysis. Recently, deep learning techniques have achieved outstanding performance in high-resolution image classification, especially the methods based on deep convolutional neural networks (DCNNs). However, the traditional CNNs using convolution operations with local receptive fields are not sufficient to model global contextual relations between objects. In addition, multiscale objects and the relatively small sample size in remote sensing have also limited classification accuracy. In this paper, a relation-enhanced multiscale convolutional network (REMSNet) method is proposed to overcome these weaknesses. A dense connectivity pattern and parallel multi-kernel convolution are combined to build a lightweight and varied receptive field sizes model. Then, the spatial relation-enhanced block and the channel relation-enhanced block are introduced into the network. They can adaptively learn global contextual relations between any two positions or feature maps to enhance feature representations. Moreover, we design a parallel multi-kernel deconvolution module and spatial path to further aggregate different scales information. The proposed network is used for urban land cover classification against two datasets: the ISPRS 2D semantic labelling contest of Vaihingen and an area of Shanghai of about 143 km2. The results demonstrate that the proposed method can effectively capture long-range dependencies and improve the accuracy of land cover classification. Our model obtains an overall accuracy (OA) of 90.46% and a mean intersection-over-union (mIoU) of 0.8073 for Vaihingen and an OA of 88.55% and a mIoU of 0.7394 for Shanghai.

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A multi-level context-guided classification method with object-based convolutional neural network for land cover classification using very high resolution remote sensing images

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2020.102086 ◽

2020 ◽

Vol 88 ◽

pp. 102086 ◽

Cited By ~ 8

Author(s):

Chenxiao Zhang ◽

Peng Yue ◽

Deodato Tapete ◽

Boyi Shangguan ◽

Mi Wang ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

High Resolution ◽

Land Cover ◽

Convolutional Neural Network ◽

Land Cover Classification ◽

Remote Sensing Images ◽

Object Based ◽

Multi Level ◽

Very High

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Coastal Land Cover Classification of High-Resolution Remote Sensing Images Using Attention-Driven Context Encoding Network

Sensors ◽

10.3390/s20247032 ◽

2020 ◽

Vol 20 (24) ◽

pp. 7032

Author(s):

Jifa Chen ◽

Gang Chen ◽

Lizhe Wang ◽

Bo Fang ◽

Ping Zhou ◽

...

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Land Cover ◽

Long Range ◽

Land Cover Classification ◽

Local Context ◽

Remote Sensing Images ◽

Evaluation Indexes ◽

Additional Information ◽

Coastal Land

Low inter-class variance and complex spatial details exist in ground objects of the coastal zone, which leads to a challenging task for coastal land cover classification (CLCC) from high-resolution remote sensing images. Recently, fully convolutional neural networks have been widely used in CLCC. However, the inherent structure of the convolutional operator limits the receptive field, resulting in capturing the local context. Additionally, complex decoders bring additional information redundancy and computational burden. Therefore, this paper proposes a novel attention-driven context encoding network to solve these problems. Among them, lightweight global feature attention modules are employed to aggregate multi-scale spatial details in the decoding stage. Meanwhile, position and channel attention modules with long-range dependencies are embedded to enhance feature representations of specific categories by capturing the multi-dimensional global context. Additionally, multiple objective functions are introduced to supervise and optimize feature information at specific scales. We apply the proposed method in CLCC tasks of two study areas and compare it with other state-of-the-art approaches. Experimental results indicate that the proposed method achieves the optimal performances in encoding long-range context and recognizing spatial details and obtains the optimum representations in evaluation indexes.

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