scholarly journals Bidirectional Grid Fusion Network for Accurate Land Cover Classification of High-Resolution Remote Sensing Images

2020 ◽  
Vol 13 ◽  
pp. 5508-5517
Author(s):  
Yupei Wang ◽  
Hao Shi ◽  
Yin Zhuang ◽  
Qianbo Sang ◽  
Liang Chen
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Land Cover Classification ◽  
Remote Sensing Images

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

Sensors ◽  
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.

scholarly journals Land-cover classification with high-resolution remote sensing images using transferable deep models

2020 ◽  
Vol 237 ◽  
pp. 111322 ◽  
Author(s):  
Xin-Yi Tong ◽  
Gui-Song Xia ◽  
Qikai Lu ◽  
Huanfeng Shen ◽  
Shengyang Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Land Cover Classification ◽  
Remote Sensing Images

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

2021 ◽  
Vol 13 (18) ◽  
pp. 3715
Author(s):  
Hao Shi ◽  
Jiahe Fan ◽  
Yupei Wang ◽  
Liang Chen
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Feature Fusion ◽  
Semantic Segmentation ◽  
Land Cover Classification ◽  
Contextual Cues ◽  
Remote Sensing Images ◽  
Object Boundary ◽  
Convolutional Network

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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