scholarly journals IFRAD: A Fast Feature Descriptor for Remote Sensing Images

2021 ◽  
Vol 13 (18) ◽  
pp. 3774
Author(s):  
Qinping Feng ◽  
Shuping Tao ◽  
Chunyu Liu ◽  
Hongsong Qu ◽  
Wei Xu
Keyword(s):  
Remote Sensing ◽  
Feature Matching ◽  
State Of The Art ◽  
Image Data ◽  
Feature Descriptor ◽  
Feature Description ◽  
Sensing Applications ◽  
Feature Similarity ◽  
Feature Based ◽  
Remote Sensing Applications

Feature description is a necessary process for implementing feature-based remote sensing applications. Due to the limited resources in satellite platforms and the considerable amount of image data, feature description—which is a process before feature matching—has to be fast and reliable. Currently, the state-of-the-art feature description methods are time-consuming as they need to quantitatively describe the detected features according to the surrounding gradients or pixels. Here, we propose a novel feature descriptor called Inter-Feature Relative Azimuth and Distance (IFRAD), which will describe a feature according to its relation to other features in an image. The IFRAD will be utilized after detecting some FAST-alike features: it first selects some stable features according to criteria, then calculates their relationships, such as their relative distances and azimuths, followed by describing the relationships according to some regulations, making them distinguishable while keeping affine-invariance to some extent. Finally, a special feature-similarity evaluator is designed to match features in two images. Compared with other state-of-the-art algorithms, the proposed method has significant improvements in computational efficiency at the expense of reasonable reductions in scale invariance.

scholarly journals An Image Registration Method Using Deep Residual Network Features for Multisource High-Resolution Remote Sensing Images

2021 ◽  
Vol 13 (17) ◽  
pp. 3425
Author(s):  
Xin Zhao ◽  
Hui Li ◽  
Ping Wang ◽  
Linhai Jing
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Registration ◽  
Feature Descriptor ◽  
Remote Sensing Images ◽  
Residual Network ◽  
Scale Invariant ◽  
Registration Method ◽  
Sensing Applications ◽  
Feature Based

Accurate registration for multisource high-resolution remote sensing images is an essential step for various remote sensing applications. Due to the complexity of the feature and texture information of high-resolution remote sensing images, especially for images covering earthquake disasters, feature-based image registration methods need a more helpful feature descriptor to improve the accuracy. However, traditional image registration methods that only use local features at low levels have difficulty representing the features of the matching points. To improve the accuracy of matching features for multisource high-resolution remote sensing images, an image registration method based on a deep residual network (ResNet) and scale-invariant feature transform (SIFT) was proposed. It used the fusion of SIFT features and ResNet features on the basis of the traditional algorithm to achieve image registration. The proposed method consists of two parts: model construction and training and image registration using a combination of SIFT and ResNet34 features. First, a registration sample set constructed from high-resolution satellite remote sensing images was used to fine-tune the network to obtain the ResNet model. Then, for the image to be registered, the Shi_Tomas algorithm and the combination of SIFT and ResNet features were used for feature extraction to complete the image registration. Considering the difference in image sizes and scenes, five pairs of images were used to conduct experiments to verify the effectiveness of the method in different practical applications. The experimental results showed that the proposed method can achieve higher accuracies and more tie points than traditional feature-based methods.

scholarly journals Remote Sensing of Floodpath Lakes and Wetlands: A Challenging Frontier in the Monitoring of Changing Environments

2018 ◽  
Vol 10 (12) ◽  
pp. 1955 ◽  
Author(s):  
Yeqiao Wang ◽  
Hervé Yésou
Keyword(s):  
Remote Sensing ◽  
State Of The Art ◽  
Anthropogenic Impacts ◽  
Management Practice ◽  
Innovation Management ◽  
Special Issue ◽  
Water Surface Area ◽  
Sensing Applications ◽  
Remote Sensing Applications ◽  
Primary Focus

Monitoring of changing lake and wetland environments has long been among the primary focus of scientific investigation, technology innovation, management practice, and decision-making analysis. Floodpath lakes and wetlands are the lakes and associated wetlands affected by seasonal variations of water level and water surface area. Floodpath lakes and wetlands are, in particular, sensitive to natural and anthropogenic impacts, such as climate change, human-induced intervention on hydrological regimes, and land use and land cover change. Rapid developments of remote sensing science and technologies, provide immense opportunities and capacities to improve our understanding of the changing lake and wetland environments. This special issue on Remote Sensing of Floodpath Lakes and Wetlands comprise featured articles reporting the latest innovative research and reflects the advancement in remote sensing applications on the theme topic. In this editorial paper, we review research developments using state-of-the-art remote sensing technologies for monitoring dynamics of floodpath lakes and wetlands; discuss challenges of remote sensing in inventory, monitoring, management, and governance of floodpath lakes and wetlands; and summarize the highlights of the articles published in this special issue.

scholarly journals Benchmarking Anchor-Based and Anchor-Free State-of-the-Art Deep Learning Methods for Individual Tree Detection in RGB High-Resolution Images

2021 ◽  
Vol 13 (13) ◽  
pp. 2482
Author(s):  
Pedro Zamboni ◽  
José Marcato Junior ◽  
Jonathan de Andrade Silva ◽  
Gabriela Takahashi Miyoshi ◽  
Edson Takashi Matsubara ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
State Of The Art ◽  
Free State ◽  
Two Stage ◽  
Learning Methods ◽  
Single Tree ◽  
Sensing Applications ◽  
Remote Sensing Applications

Urban forests contribute to maintaining livability and increase the resilience of cities in the face of population growth and climate change. Information about the geographical distribution of individual trees is essential for the proper management of these systems. RGB high-resolution aerial images have emerged as a cheap and efficient source of data, although detecting and mapping single trees in an urban environment is a challenging task. Thus, we propose the evaluation of novel methods for single tree crown detection, as most of these methods have not been investigated in remote sensing applications. A total of 21 methods were investigated, including anchor-based (one and two-stage) and anchor-free state-of-the-art deep-learning methods. We used two orthoimages divided into 220 non-overlapping patches of 512 × 512 pixels with a ground sample distance (GSD) of 10 cm. The orthoimages were manually annotated, and 3382 single tree crowns were identified as the ground-truth. Our findings show that the anchor-free detectors achieved the best average performance with an AP50 of 0.686. We observed that the two-stage anchor-based and anchor-free methods showed better performance for this task, emphasizing the FSAF, Double Heads, CARAFE, ATSS, and FoveaBox models. RetinaNet, which is currently commonly applied in remote sensing, did not show satisfactory performance, and Faster R-CNN had lower results than the best methods but with no statistically significant difference. Our findings contribute to a better understanding of the performance of novel deep-learning methods in remote sensing applications and could be used as an indicator of the most suitable methods in such applications.

New Computational Models for Image Remote Sensing and Big Data

2019 ◽  
pp. 232-248
Author(s):  
Dhanasekaran K. Pillai
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Computational Models ◽  
Image Data ◽  
Wireless Sensor ◽  
Data Handling ◽  
Sensing Applications ◽  
Emerging Trends ◽  
Remote Sensing Applications ◽  
Handling Method

This chapter focuses on the development of new computational models for remote sensing applications with big data handling method using image data. Furthermore, this chapter presents an overview of the process of developing systems for remote sensing and monitoring. The issues and challenges are presented to discuss various problems related to the handling of image big data in wireless sensor networks that have various real-world applications. Moreover, the possible solutions and future recommendations to address the challenges have been presented and also this chapter includes discussion of emerging trends and a conclusion.

New Computational Models for Image Remote Sensing and Big Data

2018 ◽  
pp. 1-21
Author(s):  
Dhanasekaran K. Pillai
Keyword(s):  
Remote Sensing ◽  
Big Data ◽  
Computational Models ◽  
Image Data ◽  
Wireless Sensor ◽  
Data Handling ◽  
Sensing Applications ◽  
Emerging Trends ◽  
Remote Sensing Applications ◽  
Handling Method

This chapter focuses on the development of new computational models for remote sensing applications with big data handling method using image data. Furthermore, this chapter presents an overview of the process of developing systems for remote sensing and monitoring. The issues and challenges are presented to discuss various problems related to the handling of image big data in wireless sensor networks that have various real-world applications. Moreover, the possible solutions and future recommendations to address the challenges have been presented and also this chapter includes discussion of emerging trends and a conclusion.

Accurate Detection of Built-Up Areas from High-Resolution Remote Sensing Imagery Using a Fully Convolutional Network

2019 ◽  
Vol 85 (10) ◽  
pp. 737-752
Author(s):  
Yihua Tan ◽  
Shengzhou Xiong ◽  
Zhi Li ◽  
Jinwen Tian ◽  
Yansheng Li
Keyword(s):  
Remote Sensing ◽  
State Of The Art ◽  
Automatic Extraction ◽  
Feature Maps ◽  
Convolutional Network ◽  
Data Set ◽  
Fully Convolutional Network ◽  
Sensing Applications ◽  
Wide Range ◽  
Remote Sensing Applications

The analysis of built-up areas has always been a popular research topic for remote sensing applications. However, automatic extraction of built-up areas from a wide range of regions remains challenging. In this article, a fully convolutional network (FCN)–based strategy is proposed to address built-up area extraction. The proposed algorithm can be divided into two main steps. First, divide the remote sensing image into blocks and extract their deep features by a lightweight multi-branch convolutional neural network (LMB-CNN). Second, rearrange the deep features into feature maps that are fed into a well-designed FCN for image segmentation. Our FCN is integrated with multi-branch blocks and outputs multi-channel segmentation masks that are utilized to balance the false alarm and missing alarm. Experiments demonstrate that the overall classification accuracy of the proposed algorithm can achieve 98.75% in the test data set and that it has a faster processing compared with the existing state-of-the-art algorithms.

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