Remote Sensing Image Registration Using Multiple Image Features

Yang, Kun; Pan, Anning; Yang, Yang; Zhang, Su; Ong, Sim Heng; Tang, Haolin

doi:10.3390/rs9060581

Cited by 119 publications

(24 citation statements)

References 33 publications

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“…This issue can be addressed through the use of ground control points (GCPs) during image acquisition [83,109], the location of which is either fixed or measured with specialized equipment more accurate than the camera integrated one (e.g., differential GNSS). Since the latter could prove quite cumbersome and time-consuming for large-scale applications (especially in rice fields which are flooded), automatic registration methods using a reference image can be applied instead, which is an active research topic with many methods having been proposed recently [110][111][112][113]. The latter can reduce the geolocation errors to approximately 1 m or even less, which is sufficient for precision agriculture applications and variable rate fertilization in particular.…”

Section: Discussionmentioning

confidence: 99%

Estimating Rice Agronomic Traits Using Drone-Collected Multispectral Imagery

et al. 2019

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The knowledge of rice nitrogen (N) requirements and uptake capacity are fundamental for the development of improved N management. This paper presents empirical models for predicting agronomic traits that are relevant to yield and N requirements of rice (Oryza sativa L.) through remotely sensed data. Multiple linear regression models were constructed at key growth stages (at tillering and at booting), using as input reflectance values and vegetation indices obtained from a compact multispectral sensor (green, red, red-edge, and near-infrared channels) onboard an unmanned aerial vehicle (UAV). The models were constructed using field data and images from two consecutive years in a number of experimental rice plots in Greece (Thessaloniki Regional Unit), by applying four different N treatments (C0: 0 N kg∙ha−1, C1: 80 N kg∙ha−1, C2: 160 N kg∙ha−1, and C4: 320 N kg∙ha−1). Models for estimating the current crop status (e.g., N uptake at the time of image acquisition) and predicting the future one (e.g., N uptake of grains at maturity) were developed and evaluated. At the tillering stage, high accuracies (R2 ≥ 0.8) were achieved for N uptake and biomass. At the booting stage, similarly high accuracies were achieved for yield, N concentration, N uptake, biomass, and plant height, using inputs from either two or three images. The results of the present study can be useful for providing N recommendations for the two top-dressing fertilizations in rice cultivation, through a cost-efficient workflow.

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Section: Discussionmentioning

confidence: 99%

Estimating Rice Agronomic Traits Using Drone-Collected Multispectral Imagery

et al. 2019

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“…This makes huge difference between the UAV aerial images and the satellite maps. Therefore, without any image pre-processing, it is not realistic to match UAV aerial images and the satellite maps by using SURF [15,18]. Ao used to propose a method which traverses a range of ratios to search the optimal scaling ratio [2].…”

Section: Shichu Chen Zhiqiang Wang and Yan Renmentioning

confidence: 99%

A fast matching algorithm for the images with large scale disparity

Chen¹,

Wang²,

Ren³

2020

Mathematical Foundations of Computing

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With the expansion of application areas of unmanned aerial vehicle (UAV) applications, there is a rising demand to realize UAV navigation by means of computer vision. Speeded-Up Robust Features (SURF) is an ideal image matching algorithm to be applied to solve the location for UAV. However, if there is a large scale difference between two images with the same scene taken by UAV and satellite respectively, it is difficult to apply SURF to complete the accurate image matching directly. In this paper, a fast image matching algorithm which can bridge the huge scale gap is proposed. The fast matching algorithm searches an optimal scaling ratio based on the ground distance represented by pixel. Meanwhile, a validity index for validating the performance of matching is given. The experimental results illustrate that the proposed algorithm performs better performance both on speed and accuracy. What's more, the proposed algorithm can also obtain the correct matching results on the images with rotation. Therefore, the proposed algorithm could be applied to location and navigation for UAV in future.

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“…Feature matching is the basis of various remote sensing processing tasks, such as image retrieval [1,2], object recognition [3,4] and image registration [5][6][7][8]. eature matching can also contribute to calibrate attitude sensor performance [9,10].…”

Section: Introductionmentioning

confidence: 99%

Local Deep Descriptor for Remote Sensing Image Feature Matching

et al. 2019

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Feature matching via local descriptors is one of the most fundamental problems in many computer vision tasks, as well as in the remote sensing image processing community. For example, in terms of remote sensing image registration based on the feature, feature matching is a vital process to determine the quality of transform model. While in the process of feature matching, the quality of feature descriptor determines the matching result directly. At present, the most commonly used descriptor is hand-crafted by the designer’s expertise or intuition. However, it is hard to cover all the different cases, especially for remote sensing images with nonlinear grayscale deformation. Recently, deep learning shows explosive growth and improves the performance of tasks in various fields, especially in the computer vision community. Here, we created remote sensing image training patch samples, named Invar-Dataset in a novel and automatic way, then trained a deep learning convolutional neural network, named DescNet to generate a robust feature descriptor for feature matching. A special experiment was carried out to illustrate that our created training dataset was more helpful to train a network to generate a good feature descriptor. A qualitative experiment was then performed to show that feature descriptor vector learned by the DescNet could be used to register remote sensing images with large gray scale difference successfully. A quantitative experiment was then carried out to illustrate that the feature vector generated by the DescNet could acquire more matched points than those generated by hand-crafted feature Scale Invariant Feature Transform (SIFT) descriptor and other networks. On average, the matched points acquired by DescNet was almost twice those acquired by other methods. Finally, we analyzed the advantages of our created training dataset Invar-Dataset and DescNet and gave the possible development of training deep descriptor network.

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Remote Sensing Image Registration Using Multiple Image Features

Cited by 119 publications

References 33 publications

Estimating Rice Agronomic Traits Using Drone-Collected Multispectral Imagery

Estimating Rice Agronomic Traits Using Drone-Collected Multispectral Imagery

A fast matching algorithm for the images with large scale disparity

Local Deep Descriptor for Remote Sensing Image Feature Matching

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