scholarly journals DEVELOPING AN EFFECTIVE MODEL FOR THE SEMANTIC SEGMENTATION OF REMOTE SENSING IMAGERY

IJARCCE ◽  
2021 ◽  
Vol 10 (12) ◽  
Author(s):  
Muazu Aminu Aliyu ◽  
Souley Boukari ◽  
Abdullahi Gamsha Madaki
Keyword(s):  
Remote Sensing ◽  
Semantic Segmentation ◽  
Effective Model ◽  
Remote Sensing Imagery

scholarly journals AI-ForestWatch: semantic segmentation based end-to-end framework for forest estimation and change detection using multi-spectral remote sensing imagery

2021 ◽  
Vol 15 (02) ◽  
Author(s):  
Annus Zulfiqar ◽  
Muhammad M. Ghaffar ◽  
Muhammad Shahzad ◽  
Christian Weis ◽  
Muhammad I. Malik ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
Semantic Segmentation ◽  
Remote Sensing Imagery ◽  
End To End

scholarly journals SEMANTIC SEGMENTATION OF REMOTE SENSING IMAGERY USING OBJECT-BASED MARKOV RANDOM FIELD BASED ON HIERARCHICAL SEGMENTATION TREE WITH AUXILIARY LABELS

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 75-81
Author(s):  
L. He ◽  
Z. Wu ◽  
Y. Zhang ◽  
Z. Hu
Keyword(s):  
Remote Sensing ◽  
Random Field ◽  
Markov Random Field ◽  
Texture Features ◽  
Semantic Segmentation ◽  
Remote Sensing Imagery ◽  
Hierarchical Segmentation ◽  
Art Object ◽  
Markov Random ◽  
Object Based

Abstract. In the remote sensing imagery, spectral and texture features are always complex due to different landscapes, which leads to misclassifications in the results of semantic segmentation. The object-based Markov random field provides an effective solution to this problem. However, the state-of-the-art object-based Markov random field still needs to be improved. In this paper, an object-based Markov Random Field model based on hierarchical segmentation tree with auxiliary labels is proposed. A remote sensing imagery is first segmented and the object-based hierarchical segmentation tree is built based on initial segmentation objects and merging criteria. And then, the object-based Markov random field with auxiliary label fields is established on the hierarchical tree structure. A probabilistic inference is applied to solve this model by iteratively updating label field and auxiliary label fields. In the experiment, this paper utilized a Worldview-3 image to evaluate the performance, and the results show the validity and the accuracy of the presented semantic segmentation approach.

scholarly journals Attentively Learning Edge Distributions for Semantic Segmentation of Remote Sensing Imagery

2021 ◽  
Vol 14 (1) ◽  
pp. 102
Author(s):  
Xin Li ◽  
Tao Li ◽  
Ziqi Chen ◽  
Kaiwen Zhang ◽  
Runliang Xia
Keyword(s):  
Remote Sensing ◽  
Contextual Information ◽  
Semantic Segmentation ◽  
Matrix Analysis ◽  
Natural Image ◽  
Feature Maps ◽  
Remote Sensing Imagery ◽  
Edge Distribution ◽  
Non Local ◽  
Ablation Study

Semantic segmentation has been a fundamental task in interpreting remote sensing imagery (RSI) for various downstream applications. Due to the high intra-class variants and inter-class similarities, inflexibly transferring natural image-specific networks to RSI is inadvisable. To enhance the distinguishability of learnt representations, attention modules were developed and applied to RSI, resulting in satisfactory improvements. However, these designs capture contextual information by equally handling all the pixels regardless of whether they around edges. Therefore, blurry boundaries are generated, rising high uncertainties in classifying vast adjacent pixels. Hereby, we propose an edge distribution attention module (EDA) to highlight the edge distributions of leant feature maps in a self-attentive fashion. In this module, we first formulate and model column-wise and row-wise edge attention maps based on covariance matrix analysis. Furthermore, a hybrid attention module (HAM) that emphasizes the edge distributions and position-wise dependencies is devised combing with non-local block. Consequently, a conceptually end-to-end neural network, termed as EDENet, is proposed to integrate HAM hierarchically for the detailed strengthening of multi-level representations. EDENet implicitly learns representative and discriminative features, providing available and reasonable cues for dense prediction. The experimental results evaluated on ISPRS Vaihingen, Potsdam and DeepGlobe datasets show the efficacy and superiority to the state-of-the-art methods on overall accuracy (OA) and mean intersection over union (mIoU). In addition, the ablation study further validates the effects of EDA.

scholarly journals SEMANTIC SEGMENTATION OF CONVOLUTIONAL NEURAL NETWORK FOR SUPERVISED CLASSIFICATION OF MULTISPECTRAL REMOTE SENSING

2018 ◽  
Vol XLII-3 ◽  
pp. 2035-2039
Author(s):  
L. Xue ◽  
C. Liu ◽  
Y. Wu ◽  
H. Li
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
High Resolution ◽  
Network Architecture ◽  
Semantic Segmentation ◽  
Remote Sensing Imagery ◽  
The Neural Network ◽  
Ground Object ◽  
Remote Sensing Image Processing

Semantic segmentation is a fundamental research in remote sensing image processing. Because of the complex maritime environment, the classification of roads, vegetation, buildings and water from remote Sensing Imagery is a challenging task. Although the neural network has achieved excellent performance in semantic segmentation in the last years, there are a few of works using CNN for ground object segmentation and the results could be further improved. This paper used convolution neural network named U-Net, its structure has a contracting path and an expansive path to get high resolution output. In the network , We added BN layers, which is more conducive to the reverse pass. Moreover, after upsampling convolution , we add dropout layers to prevent overfitting. They are promoted to get more precise segmentation results. To verify this network architecture, we used a Kaggle dataset. Experimental results show that U-Net achieved good performance compared with other architectures, especially in high-resolution remote sensing imagery.

scholarly journals Automated Processing of Remote Sensing Imagery Using Deep Semantic Segmentation: A Building Footprint Extraction Case

2020 ◽  
Vol 9 (8) ◽  
pp. 486 ◽  
Author(s):  
Aleksandar Milosavljević
Keyword(s):  
Remote Sensing ◽  
Semantic Segmentation ◽  
Aerial Image ◽  
Empirical Examination ◽  
Remote Sensing Imagery ◽  
Automated Processing ◽  
Building Footprint ◽  
Segmentation Task ◽  
Segmentation Models ◽  
Selection Of

The proliferation of high-resolution remote sensing sensors and platforms imposes the need for effective analyses and automated processing of high volumes of aerial imagery. The recent advance of artificial intelligence (AI) in the form of deep learning (DL) and convolutional neural networks (CNN) showed remarkable results in several image-related tasks, and naturally, gain the focus of the remote sensing community. In this paper, we focus on specifying the processing pipeline that relies on existing state-of-the-art DL segmentation models to automate building footprint extraction. The proposed pipeline is organized in three stages: image preparation, model implementation and training, and predictions fusion. For the first and third stages, we introduced several techniques that leverage remote sensing imagery specifics, while for the selection of the segmentation model, we relied on empirical examination. In the paper, we presented and discussed several experiments that we conducted on Inria Aerial Image Labeling Dataset. Our findings confirmed that automatic processing of remote sensing imagery using DL semantic segmentation is both possible and can provide applicable results. The proposed pipeline can be potentially transferred to any other remote sensing imagery segmentation task if the corresponding dataset is available.

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