scholarly journals Remote Sensing Image Semantic Segmentation Algorithm Based on Improved ENet Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yiqin Wang
Keyword(s):  
Remote Sensing ◽  
Receptive Field ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Activation Function ◽  
Segmentation Algorithm ◽  
Segmentation Result ◽  
Image Information ◽  
Dilated Convolution

A remote sensing image semantic segmentation algorithm based on improved ENet network is proposed to improve the accuracy of segmentation. First, dilated convolution and decomposition convolution are introduced in the coding stage. They are used in conjunction with ordinary convolution to increase the receptive field of the model. Each convolution output contains a larger range of image information. Second, in the decoding stage, the image information of different scales is obtained through the upsampling operation and then through the compression, excitation, and reweighting operations of the Squeeze and Excitation (SE) module. The weight of each feature channel is recalibrated to improve the accuracy of the network. Finally, the Softmax activation function and the Argmax function are used to obtain the final segmentation result. Experiments show that our algorithm can significantly improve the accuracy of remote sensing image semantic segmentation.

scholarly journals Remote Sensing Image Semantic Segmentation Based on Edge Information Guidance

2020 ◽  
Vol 12 (9) ◽  
pp. 1501
Author(s):  
Chu He ◽  
Shenglin Li ◽  
Dehui Xiong ◽  
Peizhang Fang ◽  
Mingsheng Liao
Keyword(s):  
Remote Sensing ◽  
Prior Knowledge ◽  
Image Data ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Segmentation Result ◽  
Rapid Progress ◽  
Remote Sensing Images ◽  
Edge Information ◽  
Important Field

Semantic segmentation is an important field for automatic processing of remote sensing image data. Existing algorithms based on Convolution Neural Network (CNN) have made rapid progress, especially the Fully Convolution Network (FCN). However, problems still exist when directly inputting remote sensing images to FCN because the segmentation result of FCN is not fine enough, and it lacks guidance for prior knowledge. To obtain more accurate segmentation results, this paper introduces edge information as prior knowledge into FCN to revise the segmentation results. Specifically, the Edge-FCN network is proposed in this paper, which uses the edge information detected by Holistically Nested Edge Detection (HED) network to correct the FCN segmentation results. The experiment results on ESAR dataset and GID dataset demonstrate the validity of Edge-FCN.

scholarly journals Fully Convolutional Neural Network with Augmented Atrous Spatial Pyramid Pool and Fully Connected Fusion Path for High Resolution Remote Sensing Image Segmentation

2019 ◽  
Vol 9 (9) ◽  
pp. 1816 ◽  
Author(s):  
Guangsheng Chen ◽  
Chao Li ◽  
Wei Wei ◽  
Weipeng Jing ◽  
Marcin Woźniak ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Image Segmentation ◽  
High Resolution ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Dilated Convolution ◽  
Segmentation Task ◽  
Fully Connected ◽  
Spatial Pyramid

Recent developments in Convolutional Neural Networks (CNNs) have allowed for the achievement of solid advances in semantic segmentation of high-resolution remote sensing (HRRS) images. Nevertheless, the problems of poor classification of small objects and unclear boundaries caused by the characteristics of the HRRS image data have not been fully considered by previous works. To tackle these challenging problems, we propose an improved semantic segmentation neural network, which adopts dilated convolution, a fully connected (FC) fusion path and pre-trained encoder for the semantic segmentation task of HRRS imagery. The network is built with the computationally-efficient DeepLabv3 architecture, with added Augmented Atrous Spatial Pyramid Pool and FC Fusion Path layers. Dilated convolution enlarges the receptive field of feature points without decreasing the feature map resolution. The improved neural network architecture enhances HRRS image segmentation, reaching the classification accuracy of 91%, and the precision of recognition of small objects is improved. The applicability of the improved model to the remote sensing image segmentation task is verified.

Adaptive Effective Receptive Field Convolution for Semantic Segmentation of VHR Remote Sensing Images

2020 ◽  
pp. 1-15
Author(s):  
Xi Chen ◽  
Zhiqiang Li ◽  
Jie Jiang ◽  
Zhen Han ◽  
Shiyi Deng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Receptive Field ◽  
Semantic Segmentation ◽  
Remote Sensing Images

scholarly journals Mathematical models for information classification and recognition of multi-target optical remote sensing images

Open Physics ◽  
2020 ◽  
Vol 18 (1) ◽  
pp. 951-960
Author(s):  
Haiqing Zhang ◽  
Jun Han
Keyword(s):  
Remote Sensing ◽  
Recognition Rate ◽  
Three Dimensional ◽  
Remote Sensing Image ◽  
Optical Remote Sensing ◽  
Specific Class ◽  
Three Dimensional Model ◽  
Image Information ◽  
Information Classification ◽  
Optical Remote Sensing Image

Abstract Traditionally, three-dimensional model is used to classify and recognize multi-target optical remote sensing image information, which can only identify a specific class of targets, and has certain limitations. A mathematical model of multi-target optical remote sensing image information classification and recognition is designed, and a local adaptive threshold segmentation algorithm is used to segment multi-target optical remote sensing image to reduce the gray level between images and improve the accuracy of feature extraction. Remote sensing image information is multi-feature, and multi-target optical remote sensing image information is identified by chaotic time series analysis method. The experimental results show that the proposed model can effectively classify and recognize multi-target optical remote sensing image information. The average recognition rate is more than 95%, the maximum robustness is 0.45, the recognition speed is 98%, and the maximum time-consuming average is only 14.30 s. It has high recognition rate, robustness, and recognition efficiency.

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

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3232 ◽  
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.

scholarly journals Performance Evaluation of Single-Label and Multi-Label Remote Sensing Image Retrieval Using a Dense Labeling Dataset

2018 ◽  
Vol 10 (6) ◽  
pp. 964 ◽  
Author(s):  
Zhenfeng Shao ◽  
Ke Yang ◽  
Weixun Zhou
Keyword(s):  
Remote Sensing ◽  
Performance Evaluation ◽  
Deep Learning ◽  
Image Retrieval ◽  
Semantic Segmentation ◽  
Semantic Content ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Benchmark Datasets ◽  
Feature Based

Benchmark datasets are essential for developing and evaluating remote sensing image retrieval (RSIR) approaches. However, most of the existing datasets are single-labeled, with each image in these datasets being annotated by a single label representing the most significant semantic content of the image. This is sufficient for simple problems, such as distinguishing between a building and a beach, but multiple labels and sometimes even dense (pixel) labels are required for more complex problems, such as RSIR and semantic segmentation.We therefore extended the existing multi-labeled dataset collected for multi-label RSIR and presented a dense labeling remote sensing dataset termed "DLRSD". DLRSD contained a total of 17 classes, and the pixels of each image were assigned with 17 pre-defined labels. We used DLRSD to evaluate the performance of RSIR methods ranging from traditional handcrafted feature-based methods to deep learning-based ones. More specifically, we evaluated the performances of RSIR methods from both single-label and multi-label perspectives. These results demonstrated the advantages of multiple labels over single labels for interpreting complex remote sensing images. DLRSD provided the literature a benchmark for RSIR and other pixel-based problems such as semantic segmentation.

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