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

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%.

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

2020 ◽  
Vol 13 (1) ◽  
pp. 71
Author(s):  
Zhiyong Xu ◽  
Weicun Zhang ◽  
Tianxiang Zhang ◽  
Jiangyun Li
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
High Resolution ◽  
Spatial Information ◽  
Semantic Segmentation ◽  
Context Information ◽  
Remote Sensing Images ◽  
Global Context ◽  
Boundary Information ◽  
Extraction Stage

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.

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.

scholarly journals Dual Path Attention Net for Remote Sensing Semantic Image Segmentation

2020 ◽  
Vol 9 (10) ◽  
pp. 571
Author(s):  
Jinglun Li ◽  
Jiapeng Xiu ◽  
Zhengqiu Yang ◽  
Chen Liu
Keyword(s):  
Remote Sensing ◽  
Spatial Information ◽  
Semantic Segmentation ◽  
Remote Sensing Images ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Augmentation Strategies ◽  
Rich Information ◽  
The Mean ◽  
The Rich

Semantic segmentation plays an important role in being able to understand the content of remote sensing images. In recent years, deep learning methods based on Fully Convolutional Networks (FCNs) have proved to be effective for the sematic segmentation of remote sensing images. However, the rich information and complex content makes the training of networks for segmentation challenging, and the datasets are necessarily constrained. In this paper, we propose a Convolutional Neural Network (CNN) model called Dual Path Attention Network (DPA-Net) that has a simple modular structure and can be added to any segmentation model to enhance its ability to learn features. Two types of attention module are appended to the segmentation model, one focusing on spatial information the other focusing upon the channel. Then, the outputs of these two attention modules are fused to further improve the network’s ability to extract features, thus contributing to more precise segmentation results. Finally, data pre-processing and augmentation strategies are used to compensate for the small number of datasets and uneven distribution. The proposed network was tested on the Gaofen Image Dataset (GID). The results show that the network outperformed U-Net, PSP-Net, and DeepLab V3+ in terms of the mean IoU by 0.84%, 2.54%, and 1.32%, respectively.

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

2020 ◽  
Vol 9 (4) ◽  
pp. 256 ◽  
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.

scholarly journals Cascaded Residual Attention Enhanced Road Extraction from Remote Sensing Images

2021 ◽  
Vol 11 (1) ◽  
pp. 9
Author(s):  
Shengfu Li ◽  
Cheng Liao ◽  
Yulin Ding ◽  
Han Hu ◽  
Yang Jia ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Information ◽  
Semantic Segmentation ◽  
Road Extraction ◽  
Remote Sensing Images ◽  
Long Distance ◽  
Features Fusion ◽  
Multi Scale ◽  
Boundary Recognition ◽  
Benchmark Datasets

Efficient and accurate road extraction from remote sensing imagery is important for applications related to navigation and Geographic Information System updating. Existing data-driven methods based on semantic segmentation recognize roads from images pixel by pixel, which generally uses only local spatial information and causes issues of discontinuous extraction and jagged boundary recognition. To address these problems, we propose a cascaded attention-enhanced architecture to extract boundary-refined roads from remote sensing images. Our proposed architecture uses spatial attention residual blocks on multi-scale features to capture long-distance relations and introduce channel attention layers to optimize the multi-scale features fusion. Furthermore, a lightweight encoder-decoder network is connected to adaptively optimize the boundaries of the extracted roads. Our experiments showed that the proposed method outperformed existing methods and achieved state-of-the-art results on the Massachusetts dataset. In addition, our method achieved competitive results on more recent benchmark datasets, e.g., the DeepGlobe and the Huawei Cloud road extraction challenge.

scholarly journals Multi-scale Adaptive Feature Fusion Network for Semantic Segmentation in Remote Sensing Images

2020 ◽  
Vol 12 (5) ◽  
pp. 872 ◽  
Author(s):  
Ronghua Shang ◽  
Jiyu Zhang ◽  
Licheng Jiao ◽  
Yangyang Li ◽  
Naresh Marturi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Multiple Scales ◽  
Feature Fusion ◽  
Semantic Segmentation ◽  
Semantic Features ◽  
Remote Sensing Images ◽  
Global Features ◽  
Global Average ◽  
Multi Scale ◽  
Context Extraction

Semantic segmentation of high-resolution remote sensing images is highly challenging due to the presence of a complicated background, irregular target shapes, and similarities in the appearance of multiple target categories. Most of the existing segmentation methods that rely only on simple fusion of the extracted multi-scale features often fail to provide satisfactory results when there is a large difference in the target sizes. Handling this problem through multi-scale context extraction and efficient fusion of multi-scale features, in this paper we present an end-to-end multi-scale adaptive feature fusion network (MANet) for semantic segmentation in remote sensing images. It is a coding and decoding structure that includes a multi-scale context extraction module (MCM) and an adaptive fusion module (AFM). The MCM employs two layers of atrous convolutions with different dilatation rates and global average pooling to extract context information at multiple scales in parallel. MANet embeds the channel attention mechanism to fuse semantic features. The high- and low-level semantic information are concatenated to generate global features via global average pooling. These global features are used as channel weights to acquire adaptive weight information of each channel by the fully connected layer. To accomplish an efficient fusion, these tuned weights are applied to the fused features. Performance of the proposed method has been evaluated by comparing it with six other state-of-the-art networks: fully convolutional networks (FCN), U-net, UZ1, Light-weight RefineNet, DeepLabv3+, and APPD. Experiments performed using the publicly available Potsdam and Vaihingen datasets show that the proposed MANet significantly outperforms the other existing networks, with overall accuracy reaching 89.4% and 88.2%, respectively and with average of F1 reaching 90.4% and 86.7% respectively.

scholarly journals HA-MPPNet: Height Aware-Multi Path Parallel Network for High Spatial Resolution Remote Sensing Image Semantic Seg-Mentation

2021 ◽  
Vol 10 (10) ◽  
pp. 672
Author(s):  
Suting Chen ◽  
Chaoqun Wu ◽  
Mithun Mukherjee ◽  
Yujie Zheng
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Spatial Information ◽  
Feature Fusion ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Surface Model ◽  
Semantic Features ◽  
Low Level ◽  
Parallel Network

Semantic segmentation of remote sensing images (RSI) plays a significant role in urban management and land cover classification. Due to the richer spatial information in the RSI, existing convolutional neural network (CNN)-based methods cannot segment images accurately and lose some edge information of objects. In addition, recent studies have shown that leveraging additional 3D geometric data with 2D appearance is beneficial to distinguish the pixels’ category. However, most of them require height maps as additional inputs, which severely limits their applications. To alleviate the above issues, we propose a height aware-multi path parallel network (HA-MPPNet). Our proposed MPPNet first obtains multi-level semantic features while maintaining the spatial resolution in each path for preserving detailed image information. Afterward, gated high-low level feature fusion is utilized to complement the lack of low-level semantics. Then, we designed the height feature decode branch to learn the height features under the supervision of digital surface model (DSM) images and used the learned embeddings to improve semantic context by height feature guide propagation. Note that our module does not need a DSM image as additional input after training and is end-to-end. Our method outperformed other state-of-the-art methods for semantic segmentation on publicly available remote sensing image datasets.

scholarly journals Multi-Scale Object Histogram Distance for LCCD Using Bi-Temporal Very-High-Resolution Remote Sensing Images

2018 ◽  
Vol 10 (11) ◽  
pp. 1809 ◽  
Author(s):  
ZhiYong Lv ◽  
TongFei Liu ◽  
Jón Atli Benediktsson ◽  
Tao Lei ◽  
YiLiang Wan
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Change Detection ◽  
Land Cover Change ◽  
Spatial Information ◽  
Remote Sensing Images ◽  
Blue Band ◽  
Multi Scale ◽  
Evaluation Approach ◽  
Event Image

To improve the performance of land-cover change detection (LCCD) using remote sensing images, this study utilises spatial information in an adaptive and multi-scale manner. It proposes a novel multi-scale object histogram distance (MOHD) to measure the change magnitude between bi-temporal remote sensing images. Three major steps are related to the proposed MOHD. Firstly, multi-scale objects for the post-event image are extracted through a widely used algorithm called the fractional net evaluation approach. The pixels within a segmental object are taken to construct the pairwise frequency distribution histograms. An arithmetic frequency-mean feature is then defined from the red, green and blue band histogram. Secondly, bin-to-bin distance is adapted to measure the change magnitude between the pairwise objects of bi-temporal images. The change magnitude image (CMI) of the bi-temporal images can be generated through object-by-object. Finally, the classical binary method Otsu is used to divide the CMI to a binary change detection map. Experimental results based on two real datasets with different land-cover change scenes demonstrate the effectiveness of the proposed MOHD approach in detecting land-cover change compared with three widely used existing approaches.

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