Geo-localization using Ridgeline Features Extracted from

360-degree Images of Sand Dunes

Shogo Fukuda

, Shintaro Nakatani

, Masashi Nishiyama

and Yoshio Iwai

Graduate School of Sustainability Science, Tottori University, Tottori, Japan

Graduate School of Engineering, Tottori University, Tottori, Japan

Keywords:

Geo-localization, Ridgelines, 360-degree Images, Sand Dune.

Abstract:

We propose a method to extract the features of sand-dune ridgelines using a 360-degree camera to improve

the accuracy of estimating geo-locations. It is difﬁcult to estimate geo-locations in an outdoor environment

with almost no texture such as in sand dunes. We focus on the feature of the ridgeline, which is the boundary

between the ground region and the sky region. A 360-degree camera can quickly detect the ridgeline signal

in all directions in a sand dune. Our method determines the current location by searching for the nearest

ridgeline signal from target signals and pairing with their geo-locations. We evaluated the accuracy of this

geo-localization method using synthesized images generated from a digital elevation model. We also eval-

uated it using real 360-degree images collected in sand dunes. We conﬁrmed that our method signiﬁcantly

outperformed the existing geo-localization method on both synthesized and real images.

1 INTRODUCTION

Geo-localization systems have attracted attention to

increase the use of tourism resources. A geo-

localization system can guide users to speciﬁc loca-

tions and provide digital content suitable for each lo-

cation. To guide users accurately, there is a need for

a technique to estimate the geo-location of a user. In

this paper, we discuss how to estimate geo-locations

in an outdoor environment that is nearly textureless.

We consider sand dunes, which are a noted tourist re-

source of Tottori prefecture in Japan. In sand dunes,

simple and similar textures are widely spread. Thus,

we have to carefully consider how to accurately esti-

mate the geo-locations.

Many existing methods for estimating geo-

locations have been proposed. Here, we classify

the existing methods into three types. In the ﬁrst

type (Li et al., 2015; Hofmann-Wellenhof et al., 2001;

Groves, 2013), the location is acquired using a GNSS

(Global Navigation Satellite System) such as GPS or

GLONASS. The existing methods have a problem in

that the accuracy of the geo-location estimation de-

pends on the arrangement and number of satellites

that are observed from the location of the user. In

the second type (Kim et al., 2017; Brejcha and

Cad

ık,

2017; Lowry et al., 2016), the geo-location is esti-

mated using texture patterns in the images acquired

from a camera. We must consider that outliers fre-

quently occur when simple and similar textures repeat

in a sand dune environment. In the third type (Piasco

et al., 2018; Chen et al., 2018; Dusha and Mejias,

2012), the accuracy of estimating the geo-location is

improved using both GNSS and the camera. These

existing methods set the initial location using GNSS

and determine the current location using the image

acquired from the camera. These methods should im-

prove the accuracy compared with the methods that

use only GNSS or a camera. However, the difﬁcult

problem of little texture still remains in sand dune en-

vironments.

Our aim is to improve the accuracy of geo-location

estimation in a nearly textureless outdoor environ-

ment. We investigate robust features that can be de-

tected from the images of sand dunes. The images in-

clude two regions, the ground and the sky. The ground

region is covered with sand, so it is unsuitable for fea-

ture extraction because there is little texture. The sky

region contains many objects, such as clouds and the

sun, that disturb the feature extraction because these

always move over time. Hence, it is difﬁcult to use ei-

ther region to improve the accuracy of estimating geo-

location. In this paper, we focus on the feature of the

ridgeline, which is the boundary between the ground

region and the sky region. Existing methods (Saurer

et al., 2016; Baatz et al., 2012; Nicolle et al., 2017;

Fukuda, S., Nakatani, S., Nishiyama, M. and Iwai, Y.

Geo-localization using Ridgeline Features Extracted from 360-degree Images of Sand Dunes.

DOI: 10.5220/0008914306210627

In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP, pages

621-627

ISBN: 978-989-758-402-2; ISSN: 2184-4321

621

Porzi et al., 2016) have introduced ridgelines that are

robust to changes in vegetation, illumination, and sea-

son in the outdoor environment. We assume that the

ridgelines work effectively even in a nearly texture-

less environment such as sand dunes. Note that the

existing methods are designed using a general cam-

era with a limited angle of view to extract the fea-

tures. We need to consider that ridgelines spread to

all 360-degree directions in sand dunes. It cannot be

said that the existing methods are sufﬁcient to repre-

sent the features of the ridgelines.

In this paper, we propose a method to extract the

features of the ridgelines using a 360-degree cam-

era. Our aim is to improve the accuracy of geo-

localization in sand dunes. The 360-degree camera

can quickly acquire an image containing all angles of

the view and can easily detect the ridgelines of all di-

rections. We regard the ridgelines as continuous sig-

nals and extract their amplitude components as fea-

tures. Our method sets the search range of the geo-

location using the initial location obtained by GNSS

and determines the current location by searching for

the nearest ridgeline feature. To conﬁrm the effec-

tiveness of our method, we evaluated the accuracy of

the geo-location estimation using synthesized images

generated from a digital elevation model. Further-

more, we collected the 360-degree images of a sand

dune environment and conﬁrmed that our method im-

proves accuracy.

This paper is organized as follows. Section 2 de-

scribes the details of our method, Section 3 presents

the results of a preliminary experiment using GNSS,

Section 4 describes the results of geo-localization us-

ing the synthesized images, and Section 5 describes

the result of the evaluation using real 360-degree im-

ages. Our concluding remarks are given in Section 6.

2 ESTIMATION OF

GEO-LOCATION USING

RIDGELINE SIGNALS

2.1 Ridgelines in a Sand Dune

Environment

A ridgeline generally refers to a narrow area of high

land along the top of a line of hills. In this pa-

per, the ridgeline refers to the boundary between the

ground region and the sky region in the image ac-

quired from a 360-degree camera. Figure 1(a) shows

an example of a ridgeline. We represent a ridgeline as

a one-dimensional waveform signal. When the user

moves, parallax occurs between the ridgeline signals

Ridgeline

(a)

(b)

(c)

Figure 1: Example of a ridgeline detected from a 360-

degree image of a sand dune environment (a). Two 360-

degree images acquired under different weather conditions

(b) and (c).

observed before and after movement. Therefore, we

can use the ridgeline signals as features when esti-

mating geo-location. We discuss the characteristics

of the ridgelines below. A sand dune does not change

its shape rapidly over time because of coastal con-

servation activities. There are no obstacles such as

buildings at most locations on the sand dunes. Fur-

thermore, it is difﬁcult to see the buildings that stand

several kilometers away because of the height differ-

ence in the sand dunes. We thus can stably observe

the parallax included in the ridgeline signals in all di-

rections. There is also an advantage in that the ridge-

lines can be easily detected from 360-degree images

because the textures of the ground and sky regions are

very different. Figures 1(b) and (c) show examples

of 360-degree images acquired from the sand dunes.

Given the above characteristics, we believe that geo-

localization using ridgeline signals works effectively

in sand dune environments.

2.2 Overview of Our Method

Figure 2 illustrates an overview of our method. In

step S1, we generate a target database to store the

pairs of ridgeline signals and their geo-locations. The

system collects a 360-degree image at each location

in the sand dune area and detects the ridgeline sig-

nal in advance. Steps S2 to S4 are used to estimate

the geo-location of the user. In step S2, the user ac-

quires a query 360-degree image using the camera

VISAPP 2020 - 15th International Conference on Computer Vision Theory and Applications

622

Geo-location

S2: Obtain an initial location and

a query 360-degree image

S1: Store target ridgeline

signal and

geo-locations

S3: Detect the query ridgeline signal

S4: Search for the nearest target ridgeline

signal using the PSD features

Target ridgeline signals

Location N

Location 1

Figure 2: Overview of our method. We set the search range

of the geo-location using the initial location obtained by

GNSS and detect the current position by searching for the

nearest ridgeline signal.

and his or her initial location using GNSS. In step S3,

our method detects the ridgeline signal from the 360-

degree image. We divide the 360-degree image into

the sky and ground regions using color distributions,

and determine the region boundary to detect the ridge-

line signal. In step S4, we determine the geo-location

by searching for the target ridgeline signal that is most

similar to the query ridgeline signal. It is not practi-

cal to search through all ridgeline signals contained in

the target database. Instead, our method searches only

the neighborhood of the initial location acquired from

GNSS by limiting the search range in the database.

Our aim is to prevent outliers and reduce processing

time. Our method determines the search range using

the error of the initial location (see Section 3). The

search for ridgeline signals is described in detail be-

low.

2.3 Searching for the Nearest Target

Ridgeline Signals using Power

Spectrum Density (PSD) Features

Our method searches for the target ridgeline signal

that has the waveform that is most similar to the given

query ridgeline signal. To accurately search for the

ridgeline signal, it is necessary to consider the varia-

tion of camera poses when acquiring the 360-degree

images. Recently available 360-degree cameras have

various tilt correction functions, which suppresses the

variation in attitude. To more stably extract fea-

tures from ridgeline signals, our method uses the PSD

(Power Spectrum Density). The PSD is a frequency-

based analysis and is represented by the power spec-

trum components for each frequency band. The fre-

quency analysis assumes that the query signal is a

(a)

(b)

(c )

(d)

Figure 3: Examples of 360-degree images acquired from a

real camera (a) and (c). Synthesized images generated from

the digital elevation model (b) and (d).

continuous waveform, so it has a high afﬁnity with the

ridgeline signal acquired from a 360-degree image.

The PSD also has the advantage of not being affected

by phase shift. Note that we remove the components

of the frequency band above f

max

because high fre-

quency components sometimes contain noise when

detecting ridgeline signals. Our method determines

the target ridgeline signal corresponding to the query

ridgeline signal when the L1 norm between the two

PSD features is the smallest. Our method outputs the

ﬁnal geo-location linked to the target ridgeline signal.

3 PRELIMINARY EXPERIMENT:

THE ERROR OF THE INITIAL

LOCATION OBTAINED BY

GNSS

Our method uses GNSS to set the search range

for the target ridgeline signals from the initial lo-

cation. To determine this range, we investigated

how much error occurs in commonly available GNSS

terminals. We used a mobile terminal (GARMIN:

GPSMAP64SJ) that records the current position using

GPS, GLONASS, and QZSS. We measured known

Geo-localization using Ridgeline Features Extracted from 360-degree Images of Sand Dunes

623

location points managed by the Geospatial Informa-

tion Authority of Japan, which are generally used

for map making and various topographical surveys.

We acquired the current values of the latitude and

longitude using the mobile terminal at each known

point and compared them with the correct latitude and

longitude values of the known point. We computed

the error in distance between the current estimated

point and the known point using these values. Three

known points (L010000001070, TR35334212403,

and TR45334214401) were used. We performed one-

minute measurements three times on sunny days. The

results of the evaluation show that the estimation er-

ror of the initial location using GNSS is 2.5 ± 1.3 m.

In the following experiments, we set the search range

of the target ridgeline signals to a radius of 5 m from

the initial location, which is a margin of 2σ of the es-

timation error for the search range.

In recent years, RTK-GPS (Langley, 1998; Odijk

et al., 2017) has attracted attention because it is a

highly accurate GNSS and a low-cost device. The

RTK-GPS system uses a reference station on the

ground and corrects the estimated locations using po-

sitioning satellites. We set the reference station less

than 5 km of the known points. Here, we evaluated

the estimation error of the current locations using the

single-band RTK-GPS (NEO-M8T, u-blox). We con-

ﬁrmed that the estimation error was within several

tens of centimeters when the RTK-GPS system ob-

tained ﬁxed solutions. However, it was necessary to

wait for several tens of seconds while standing the

same location to obtain the ﬁxed solution. Further-

more, there were some cases where the ﬁxed solution

could not be obtained even after waiting a long time.

We believe that the use of RTK-GPS is still premature.

We thus used the estimation error computed from the

commonly available GNSS terminal to determine the

search range of the target ridgeline signal.

4 BASIC PERFORMANCE OF

GEO-LOCALIZATION

EVALUATED USING THE

SYNTHESIZED IMAGES

4.1 Dataset Generated from the Digital

Elevation Model

We generated synthesized images of the ridgeline sig-

nals using the digital elevation model (hyo, 2 12)

provided by the Geospatial Information Authority of

Japan. The model contains three-dimensional meshes

Digital Elevation Model (5 m

mesh)

25 m

Evaluation point

Region

Figure 4: Evaluation points on the digital elevation model

of sand dunes.

5 m

Initial location (!

, #

)

1 m

Location of the target ridgeline signal

Range of the query ridgeline signal

Target:

Radius of 5 m in intervals of 1 m from

initial location$%!

Test:

Location !(& #( ) %!

* +

& #

& +

. /0123& 1234 uniform random noise

Figure 5: Locations used to generate the synthesized images

at certain evaluation points.

at intervals of 5 m. We generated the synthesized im-

ages of the ridgelines using graphics rendering en-

gine (Unity). Figures 3(a) and (c) show real 360-

degree images, and Figs. 3(b) and (d) show synthe-

sized images assuming the same location as the real

images. When generating the synthesized images, we

rendered the ground regions as black and the sky re-

gions as white. We generated a 360-degree image by

connecting the center pixel rows of candidate images.

We made a candidate image of each orientation by ro-

tating a camera with a ﬁeld angle of θ by A. The pa-

rameter settings θ = 100 degrees and A = 0.5 degrees

were adjusted by comparing the synthesized image

with the waveform of the actual 360-degree image.

The resolution of the synthesized image was set to

720 × 180 pixels.

We used an area of a 500× 500 m square to gener-

ate the synthesized images. Figure 4 shows the eval-

uation points on the digital elevation model. We set

20 × 20 = 400 evaluation points on a 25-m grid. At

each evaluation point, synthesized images for the tar-

get and query were generated. Figure 5 shows the

locations used to generate the synthesized images at

a certain evaluation point. In the 5 m radius from an

initial position (x

, y

), which represents the center of

the search range described in Section 2.3, 73 synthe-

sized target images were generated at locations in in-

tervals of 1 m. The 10 synthesized query images were

generated at an initial location ( ˆx, ˆy) with uniform ran-

dom noise (ε

, ε

) in the range [−0.5, 0.5]. The height

of the camera from the ground surface was ﬁxed at a

height of 100 cm.

VISAPP 2020 - 15th International Conference on Computer Vision Theory and Applications

624

4.2 Results for Synthesized Images

We compared the accuracies of several geo-

localization methods to conﬁrm the effectiveness of

our method by evaluating them using the synthesized

images. We extracted features using the following

methods:

• Existing Method. We extracted the histogram

features of the ridgelines for geo-localization us-

ing the existing method (Saurer et al., 2016).

The ridgeline signals were divided into small seg-

ments. The distributions of gradients of contours

in the segments were computed to generate the

contour words, which are similar in spirit to visual

words. We set the number of the contour words to

32.

• Baseline. We used the fast Fourier transform

(FFT) to extract features from the ridgeline sig-

nals. The FFT is a representative approach of

frequency analysis. We used the amplitude com-

ponents as the feature, and set the maximum fre-

quency band of 15 Hz to increase the accuracy.

• Our Method. We used the PSD features of the

ridgeline signals. The maximum frequency band

was f

max

= 15.

We used the 360-degree images of the ridgelines syn-

thesized in Section 4.1. All experimental conditions

except for the features were the same for all three

methods. We computed the Euclidean distance from

the ground-truth location to the estimated location at

each evaluation point. The errors of the geo-locations

were calculated at all evaluation points and their av-

erages are reported.

Table 1 shows the average errors of the geo-

localization evaluated using the synthesized images

of the digital elevation model. We conﬁrmed that

our method obtained higher accuracy than the existing

method and the baseline. We visualized the average

errors of the evaluation points with a heatmap. Fig-

ure 6(a) shows the area including all evaluation points

on the digital elevation model. In Figs. 6(b), (c), and

(d), the heatmaps of the average errors for the exist-

ing method, the baseline, and our method are repre-

sented, respectively. We see that the existing method

(Fig. 6(b)) and the baseline (Fig. 6(c)) yielded large

errors for many evaluation points. In contrast, we

conﬁrmed that our method (Fig. 6(d)) was able to esti-

mate the geo-location at 1 m or less at most evaluation

points, although the average errors were still large at

some points. We discuss the reason for these large

errors below.

(a) (b)

30Average error [m]

Figure 6: Visualization of the average errors at all evalua-

tion points. (a) The three-dimensional shape of the all eval-

uation points when viewed from above. The average errors

of the (b) existing method, (c) baseline, and (d) our method.

Table 1: Average errors of the geo-localization evaluated

using the synthesized images of the digital elevation model.

Average error [m]

Existing method 0.79 ± 0.95

Baseline 0.60 ± 0.75

Our method 0.42 ± 0.60

4.3 Discussion of the Errors of

Geo-localization using Our Method

We investigated the locations at which the accuracy of

the geo-localization decreases. We hypothesized that

the errors are large when the ridgeline signals con-

tain deep depth values and the parallax is very small.

We generated histograms of the depth values of the

rendered ridgeline signals for evaluation points with

good accuracy and poor accuracy and compared them.

Figure 7 shows the distribution of depth values on the

top-10 and bottom-10 ridgelines with respect to evalu-

ation point accuracy. We see that the distributions for

low-accuracy points tend to have deeper depth values

on the ridgeline signals than the distributions for high-

accuracy points. In fact, we found that there are many

low-accuracy evaluation points on the top of a sand

dune. We believe that the top of a sand dune has a

good view and distant mountains are observed, so the

change in parallax of the ridgeline signals decreases.

Geo-localization using Ridgeline Features Extracted from 360-degree Images of Sand Dunes

625

Depth of the ridgeline (m)

800

6,000

3,000

(0, 250) (250, 500) (500, 750) (750,∞]

(a)

(c)

(b)

(d)

(0, 250) (250, 500] (500, 750) (750, ∞]

Depth of the ridgeline (m)

Frequency

Depth (m)

6,000

3,000

Frequency

800

Depth (m)

Figure 7: Distribution of depths on ridgelines and depth

maps. (a) and (c) Depth distribution of the 10 most and

least accurate evaluation points. (b) and (d) Depth maps of

the most and least accurate evaluation points.

Figure 8: Examples of real 360-degree images. The target

and query images were acquired from each area of the sand

dunes.

5 EVALUATION USING REAL

360-DEGREE IMAGES

We evaluated the accuracy of our method using actual

360-degree images collected in the sand dune envi-

ronment. We used piles managed by Tottori prefec-

ture to determine the locations where the 360-degree

0 42 31 5

Frequency

Average error (m)

(c)

0 42 31 5

Frequency

Average error (m)

(b)

0 42 31 5

Frequency

Average error (m)

(a)

Figure 9: Distributions of the errors of the geo-localization

evaluated using the real 360-degree images. Results for the

(a) existing method, (b) baseline, and (c) our method.

Table 2: Average errors of geo-localization evaluated using

real 360-degree images collected in a sand dune environ-

ment.

Average error [m]

Existing method 3.62 ± 1.84

Baseline 2.42 ± 1.72

Our method 1.81 ± 1.44

images were acquired. The piles were struck in a

grid every 100 m. We deﬁned three areas, each cen-

tered on a pile, and collected the 360-degree images

in each area. Figure 8 shows examples of real 360-

degree images, taken using a 360-degree camera (RI-

COH THETA m15) and a tripod. To generate a target

database of ridgeline signals, we collected 19× 3 = 57

target images per meter in the range of ±9 m to the

north starting from each pile. We collected 9× 3 = 27

query images per meter in the range of ±4 m to the

north. The resolution of the 360-degree image was

set to 720 × 180 pixels. We manually detected the

ridgeline signals using a graphics editor (Adobe Pho-

toshop). The search range of the target database was

set to ±5 m. The ridgeline signal feature extraction

process was the same as that described in Section 4.2.

Table 2 shows the average errors of the geo-

localization obtained using real 360-degree images.

The results show that our method obtains a smaller

VISAPP 2020 - 15th International Conference on Computer Vision Theory and Applications

626

average error (less than 2 m) than the existing method

and baseline.

We investigated how much the error was dis-

tributed for each feature. Figure 9 shows the distri-

butions of the errors of the geo-localization using the

real 360-degree images. In the results of the existing

method (Fig. 9(a)) and baseline (Fig. 9(b)), the peaks

of the distributions have an error of 4 m. In contrast,

in our method (Fig. 9(c)), almost all cases had errors

less than 1 m, though there were a few cases with er-

rors of 5 m. The above results conﬁrm that the pro-

posed PSD features extracted from the ridgeline sig-

nals are effective for geo-localization.

6 CONCLUSIONS

We proposed a method for estimating geo-location us-

ing ridgeline features extracted from 360-degree im-

ages. We evaluated the accuracy of the proposed geo-

localization method using synthesized images gener-

ated from a digital elevation model. We conﬁrmed

that our method substantially outperformed the exist-

ing method. Furthermore, we conducted an experi-

ment to evaluate geo-location using real 360-degree

images collected in the sand dunes. We conﬁrmed

that the average error of our method was less than 2

In future work, we will further evaluate our

method on various datasets of with low texture us-

ing 360-degree cameras. We will also explore the use

of synthesized images to create a target database to

reduce the cost of database generation.

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