scholarly journals Open Set Semantic Segmentation of Remote Sensing Images

2020 ◽  
Author(s):  
Caio Cesar Viana Da Silva ◽  
Jefersson Alex Dos Santos
Keyword(s):  
Remote Sensing ◽  
Semantic Segmentation ◽  
Remote Sensing Images ◽  
Computational Vision ◽  
Closed Set ◽  
Sensing Applications ◽  
Open Set ◽  
The World ◽  
Remote Sensing Applications ◽  
Novel Approaches

The development of computational vision approaches that exploit satellite imagery is relatively recent, mainly due to the limited availability of this type of image. In the area of remote sensing, applications that employ computational vision techniques are modeled for classification in closed set scenarios. However, the world is not purely closed set, many scenarios present classes that are not previously known by the algorithm, an open set scenario. Thus, the main objective of this paper is the study and development of semantic segmentation techniques considering the open set scenario applied to remote sensing images. Focusing on this problem, this is the first work to study and develop semantic segmentation techniques for open set scenarios applied to remote sensing images. The main contributions of this paper are: 1) a discussion of related works in open set semantic segmentation, showing evidence that these techniques can be adapted for open set remote sensing tasks; 2) the development and evaluation of four novel approaches for open set semantic segmentation. Our methods yielded competitive results when compared to closed set methods for the same dataset

scholarly journals TOWARDS OPEN-SET SEMANTIC SEGMENTATION OF AERIAL IMAGES

2020 ◽  
Vol IV-3/W2-2020 ◽  
pp. 19-24
Author(s):  
C. C. V. da Silva ◽  
K. Nogueira ◽  
H. N. Oliveira ◽  
J. A. dos Santos
Keyword(s):  
Remote Sensing ◽  
Computer Vision ◽  
Low Cost ◽  
Visible Spectrum ◽  
Semantic Segmentation ◽  
Aerial Images ◽  
Closed Set ◽  
Sensing Applications ◽  
Open Set ◽  
Public Datasets

Abstract. Classical and more recently deep computer vision methods are optimized for visible spectrum images, commonly encoded in grayscale or RGB colorspaces acquired from smartphones or cameras. A more uncommon source of images exploited in the remote sensing field are satellite and aerial images. However the development of pattern recognition approaches for these data is relatively recent, mainly due to the limited availability of this type of images, as until recently they were used exclusively for military purposes. Access to aerial imagery, including spectral information, has been increasing mainly due to the low cost of drones, cheapening of imaging satellite launch costs, and novel public datasets. Usually remote sensing applications employ computer vision techniques strictly modeled for classification tasks in closed set scenarios. However, real-world tasks rarely fit into closed set contexts, frequently presenting previously unknown classes, characterizing them as open set scenarios. Focusing on this problem, this is the first paper to study and develop semantic segmentation techniques for open set scenarios applied to remote sensing images. The main contributions of this paper are: 1) a discussion of related works in open set semantic segmentation, showing evidence that these techniques can be adapted for open set remote sensing tasks; 2) the development and evaluation of a novel approach for open set semantic segmentation. Our method yielded competitive results when compared to closed set methods for the same dataset.

scholarly journals Spatiotemporal Fusion in Remote Sensing

2020 ◽  
Author(s):  
Hessah Albanwan ◽  
Rongjun Qin
Keyword(s):  
Remote Sensing ◽  
Quality Improvement ◽  
Data Quality ◽  
Information Extraction ◽  
Remote Sensing Images ◽  
Sensing Applications ◽  
Surface Data ◽  
Remote Sensing Applications ◽  
Basic Concepts ◽  
Free Set

Remote sensing images and techniques are powerful tools to investigate earth’s surface. Data quality is the key to enhance remote sensing applications and obtaining clear and noise-free set of data is very difficult in most situations due to the varying acquisition (e.g., atmosphere and season), sensor and platform (e.g., satellite angles and sensor characteristics) conditions. With the increasing development of satellites, nowadays Terabytes of remote sensing images can be acquired every day. Therefore, information and data fusion can be particularly important in the remote sensing community. The fusion integrates data from various sources acquired asynchronously for information extraction, analysis, and quality improvement. In this chapter, we aim to discuss the theory of spatiotemporal fusion by investigating previous works, in addition to describing the basic concepts and some of its applications by summarizing our prior and ongoing works.

Enhancing the Binary Watermark-Based Data Hiding Scheme Using an Interpolation-Based Approach for Optical Remote Sensing Images

2018 ◽  
Vol 9 (2) ◽  
pp. 53-71 ◽  
Author(s):  
Mohammad Reza Khosravi ◽  
Habib Rostami ◽  
Sadegh Samadi
Keyword(s):  
Remote Sensing ◽  
Multimedia Data ◽  
Computer Networking ◽  
Optical Remote Sensing ◽  
Embedding Capacity ◽  
Remote Sensing Images ◽  
Sensing Applications ◽  
Original Scheme ◽  
Remote Sensing Applications ◽  
Watermarking Scheme

Computer networking and internet developments create new challenges in information security and copyright protection. In order to protect the multimedia data and also in some cases, for information management, the authors can use watermarking schemes to achieve more security. In this article, the authors firstly review a watermarking scheme for remote sensing applications, represented by Zhu et al.; They also explain two problems in Zhu et al.'s scheme and in addition, introduce two solutions for these problems. Generally, Zhu et al.'s scheme is a non-blind scheme that also does not have any attention to watermarked image quality, thus they try to represent ways in order to achieve the blind and quality-aware watermarking. Experimental results confirm that both of their modifications have suitable effectiveness than the original scheme whereas in practice, their modifications create an output with embedding capacity like the original scheme but it is high quality and also blind.

scholarly journals A Soft Shadow Detection Method Based on MRF for Remote Sensing Images

2015 ◽  
Vol 2015 ◽  
pp. 1-11
Author(s):  
Pengwei Li ◽  
Wenying Ge
Keyword(s):  
Remote Sensing ◽  
Spatial Information ◽  
Detection Method ◽  
Spectral Characteristics ◽  
Shadow Detection ◽  
Detection Methods ◽  
Remote Sensing Images ◽  
Sensing Applications ◽  
Soft Shadow ◽  
Remote Sensing Applications

Shadows limit many remote sensing applications such as classification, target detection, and change detection. Most current shadow detection methods utilize the histogram threshold of spectral characteristics to distinguish the shadows and nonshadows directly, called “hard binary shadow.” Obviously, the performance of threshold-based methods heavily rely on the selected threshold. Simultaneously, these threshold-based methods do not take any spatial information into account. To overcome these shortcomings, a soft shadow description method is developed by introducing the concept of opacity into shadow detection, and MRF-based shadow detection method is proposed in order to make use of neighborhood information. Experiments on remote sensing images have shown that the proposed method can obtain more accurate detection results.

scholarly journals Semantic Segmentation of Remote Sensing Images With Sparse Annotations

2021 ◽  
pp. 1-5
Author(s):  
Yuansheng Hua ◽  
Diego Marcos ◽  
Lichao Mou ◽  
Xiao Xiang Zhu ◽  
Devis Tuia
Keyword(s):  
Remote Sensing ◽  
Semantic Segmentation ◽  
Remote Sensing Images

scholarly journals Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images

2021 ◽  
Vol 26 (1) ◽  
pp. 200-215
Author(s):  
Muhammad Alam ◽  
Jian-Feng Wang ◽  
Cong Guangpei ◽  
LV Yunrong ◽  
Yuanfang Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Natural Scene ◽  
Remote Sensing Images ◽  
Advantages And Disadvantages ◽  
Target Segmentation

AbstractIn recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

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