close

Вход

Забыли?

вход по аккаунту

?

01431161.2017.1390275

код для вставкиСкачать
International Journal of Remote Sensing
ISSN: 0143-1161 (Print) 1366-5901 (Online) Journal homepage: http://www.tandfonline.com/loi/tres20
A modified mean filter for improving the
classification performance of very high-resolution
remote-sensing imagery
Lv ZhiYong, WenZhong Shi, Jón Atli Benediktsson & LiPeng Gao
To cite this article: Lv ZhiYong, WenZhong Shi, Jón Atli Benediktsson & LiPeng Gao (2018)
A modified mean filter for improving the classification performance of very high-resolution
remote-sensing imagery, International Journal of Remote Sensing, 39:3, 770-785, DOI:
10.1080/01431161.2017.1390275
To link to this article: http://dx.doi.org/10.1080/01431161.2017.1390275
Published online: 23 Oct 2017.
Submit your article to this journal
Article views: 5
View related articles
View Crossmark data
Full Terms & Conditions of access and use can be found at
http://www.tandfonline.com/action/journalInformation?journalCode=tres20
Download by: [University of Florida]
Date: 25 October 2017, At: 02:38
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2017
VOL. 39, NO. 3, 770–785
https://doi.org/10.1080/01431161.2017.1390275
A modified mean filter for improving the classification
performance of very high-resolution remote-sensing imagery
Lv ZhiYonga, WenZhong Shib, Jón Atli Benediktssonc and LiPeng Gao
d
Downloaded by [University of Florida] at 02:38 25 October 2017
a
School of Computer Science and Engineering, Xi’An University of Technology, Xi’An, China; bLand
Surveying & Geo-Informatics Hung Hom, The Hong Kong Polytechnic University, Kowloon, Hong Kong;
c
Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland; dSchool of
Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
ABSTRACT
ARTICLE HISTORY
Very high resolution (VHR) remote-sensing imagery can reveal
ground objects in great detail, depicting the colour, shape, size
and structure of the objects. However, VHR also leads to a large
amount of noise in the spectra, which may reduce the reliability of
the classification result. This article presents an extension of the
mean filter (MF), which is named ‘modified mean filter (MMF)’, for
smoothing the noise of VHR imagery. First, the MMF is a shapeadaptive filter that is constructed by gradually detecting the spectral similarity between a kernel-anchored pixel and its contextual
pixels through an extension detector with eight neighbouring
pixels. Then, because pixels of an objective are usually homogeneous with spatial continuity, the pixels located at the hollow area
within an extended region are rectified to enhance the smoothing
effect. Finally, the spectral value of the kernel-anchored pixel is
determined by the mean of the group of pixels within the adaptive region. Despite the proposed filter is a simple extension of MF,
it has an advantage in preserving the edge between different
classes, and smoothing the noise of intra-class. The MMF approach
is investigated through comparing with the classification of VHR
images based on filter processing, including the traditional mean
filter (MF), the median filter (MedF) and the recursive filter (RF)
which has been proposed for image classification in Kang, Li, and
Benediktsson (2014). The experimental results obtained by considering two VHR images show the effectiveness of the proposed
of MMF, which improves the performance of the classification and
implies more potential applications.
Received 9 May 2017
Accepted 3 October 2017
1. Introduction
At present, a large number of remote-sensing images with very high resolution (VHR) are
available in urban areas. Compared with low or moderate resolution images, VHR images
can depict ground object in more detail, such as shape, structure, size and texture.
Therefore, these VHR images open new perspectives for remote-sensing applications
CONTACT Lv ZhiYong
Lvzhiyong_fly@hotmail.com
School of Computer Science and Engineering, Xi’An
lswzshi@polyu.edu.hk
Land Surveying & Geo-Informatics
University of Technology, Xi’An, China; WenZhong Shi
Hung Hom, The Hong Kong Polytechnic University, Kowloon HK 999077, Hong Kong
© 2017 Informa UK Limited, trading as Taylor & Francis Group
Downloaded by [University of Florida] at 02:38 25 October 2017
INTERNATIONAL JOURNAL OF REMOTE SENSING
771
such as in urban monitoring, environment assessment, and decision-making (Moser,
Serpico, and Benediktsson 2013; Li, Zhang, and Zhang 2014). Among these applications,
as is well known, many applications depend on the results of land cover classification.
However, the use of higher resolution images does not necessarily mean that higher
classification accuracies are obtained (Huang and Zhang 2013; Wilkinson 2005). The
reason can be concluded briefly as following: Despite an advantage is that VHR images
can increase the correlation among pixels, which results in the VHR image having many
spatial features that can be utilized for visual interpretation and classification (Wang
et al. 2012), due to the limitation of the remote-sensing technique, images with a high
spatial resolution are typically limited to three or four spectral bands (worldview-2 has a
0.41 m resolution and eight spectral bands) (Gianinetto et al. 2014). Thus, the high
spatial resolution and low spectral properties result in an increased intra-class variance
and a decreased inter-class variance, which introduces spectral noise into the classifying
thematic map and enhances the difficulty of separation (Blaschke 2010). For example,
Ouyang et al. showed that as the spatial resolution of remote-sensing data increases, the
spectral noise of the pixel-wise approach becomes more serious (Ouyang et al. 2011).
Many factors, such as sensor and spatial uncertainty, also bring noise into the VHR
image, and many pixel-based approaches are very sensitive to this noise (Huang and
Zhang 2008; Duro, Franklin, and Dubé 2012).
To reduce the noise and improve the classification performance of VHR image,
spectral-spatial method usually proposed for VHR image classification. That is because
spatial information in a remote-sensing image is inherited from the real world, especially
in VHR images (Li et al. 2014). Therefore, spatial information can be employed to couple
with the spectra to remedy for the spectral information’s insufficiencies, resulting in the
smoothing of the noise and improved the accuracy of VHR image classification. For
example, Tarabalka, Benediktsson, and Chanussot (2009) proposed a new spectral-spatial
classification scheme for hyperspectral images. Huang et al. proposed a multi-feature
model including spectra, structure and semantic features for VHR image classification
(Huang and Zhang 2013). Xia et al. proposed a new spatial-spectral classification method
to enhance the performance of hyperspectral images by integrating rotation forests and
Markov random fields (Xia et al. 2015). In addition, spatial-spectral kernels have been
investigated for the classification of VHR images in many studies, such as morphological
kernels and sparse kernels (Liu et al. 2013). To explore the spatial information and
improve the performance of the VHR image classification, mathematical morphology
was applied and extended by increasing the size of the structuring element
(Benediktsson, Pesaresi, and Amason 2003) and stacking different shapes of structuring
elements (Lv et al. 2014). Several advances of the spectral-spatial approach for classification of high resolution images were summarized in previous research (Fauvel et al.
2013). However, the spatial-spectral feature-based approach relies on the performance
of the spatial feature extraction technique. To obtain accurate spatial features, advanced
feature extraction algorithms are usually required, but those processes may be timeconsuming and experience-dependent.
Different from spatial-spectral feature-based classification approach described previously, image filtering is also adopted to smooth the noise of the hyperspectral imagery
with high resolution. For example, Angelos et al. developed an image classification
framework that was integrated with a nonlinear scale-space filter (Tzotsos, Karantzalos,
Downloaded by [University of Florida] at 02:38 25 October 2017
772
Z. LV ET AL.
and Argialas 2011). Recently, image filter has become a very hot topic in hyperspectral
image processing and applied successfully in many applications (Kang, Li, and
Benediktsson 2014). Although a filter is a powerful tool for denoising an image, the
practical application of filters poses two problems: (1) most of the filters have been
investigated on hyperspectral images, while VHR images have been more or less ignored
and (2) many filters, such as the mean filter (MF) and median filter (MedF), are related to
a regular window as the filter’s convolution. However, a single-size regular window may
be unable to cover the multifarious spatial information of the various ground targets in
an entire image. Therefore, it should be noted that the traditional MF is related to the
spectral and regular domains alone, especially for VHR remote-sensing image.
In this article, a modified mean filter (MMF) is proposed for reducing the noise and
improving the performance of the VHR image classification. Compared with MF, MedF
and RF (Kang, Shutao, and Benediktsson 2014), the proposed filter runs through the
whole VHR image through convolution in an adaptive manner. To verify the performance of the proposed approach, experiments were designed on the basis of a VHR
false colour image of Pavia University and an IKONOS panchromatic image obtained
from Reykjavik, Iceland. In these experiments, to demonstrate the advantage of the
proposed MMF for VHR image classification, the results of classification based on the
proposed MMF are compared with that of the unfiltered process, the MF, MedF and RF.
2. New image denoising approach of an MMF for VHR images
In this section, an image filter, called MMF, is proposed for reducing the noise of the VHR
image. The MMF adds an extension to the traditional mean filter. However, unlike the
single size and regular window of the mean filter, the shape and size of the convolution
region of the MMF are extracted in a pixel-by-pixel manner, wherein the region of each
pixel has a higher homogeneity. The shape of an adaptive region represents the
contextual features surrounding a kernel-anchored pixel, and the size of the adaptive
region is constrained by two thresholds in the spectral and spatial domains. For each
single region extension, it seems like region growing algorithm. However, to our knowledge, it is the first time that region growing is introduced for noise reduction of VHR
image classification.
The proposed MMF contains three blocks, and they will be detailed as following:
(1) extension of the MMF’s region;
(2) rectification-operation within the MMF’s region; and
(3) calculation of the mean value of the labelled pixels.
2.1. Algorithm of region-extension for MMF
An extension is used to detect adaptively the contextual features surrounding a pixel, an
example of extension is shown in Figure 1. First, a kernel-anchored pixel (KAPi;j ) is a pixel
of a VHR image at location: ði; jÞ. Then, in one chosen KAPi;j , the spectral difference
between the KAPi;j and its eight neighbouring pixels is measured to decide whether the
neighbouring pixel belongs to the homogeneous area around KAPi;j . The homogeneity
of the pixel is defined using the formula (1):
Downloaded by [University of Florida] at 02:38 25 October 2017
INTERNATIONAL JOURNAL OF REMOTE SENSING
773
Figure 1. A filtering example based on an MMF with an eight neighbouring extension detector. (a)
An eight neighbouring extension detector, and (b) is an m n VHR remote-sensing image with R–
G–B bands. (c) A sample extended region, where ‘X-X’ represents the detecting and extending order,
with the first number a KAP or a CAP and the second number the detecting order surrounding the
current anchored pixel.
Δs ¼ ðKAPÞi;j Psur ;
(1)
where Δs represents the spectral similarity between a KAPi;j and its surrounding pixels
Psur . The greater Δs is, the greater difference between the KAPi;j and its surrounding
pixels (Psur ) is, and the lower homogeneity between KAPi;j and Psur . And Psur is one of the
eight neighbouring pixels, sur 2 ½0; 7.
The shape and size of the region around a KAP is extended gradually if the following
conditions are met.
(1) Δs is less than a predefined threshold T1 ;
(2) The total number of labelled pixels that comprise the adaptive region is less than
another predefined threshold T2 .
As shown in Figure 1, if the surrounding pixel Psur and KAPi;j met condition-1, Psur is
labelled as a homogeneous pixel and stack it into the set of candidate-anchored pixels
(CAP). A pixel in the set of CAPk is prepared as another level of anchored pixel for the
region extension in the spatial domain, as the eight neighbouring pixels surrounding a
CAP is compared with KAPi;j in the spectral domain. Therefore, this ensures that all of the
labelled pixels are spectrally homogeneous, and the extension of the MMF region is
adaptive in the spatial domain. In other words, CAP is used to extend the adaptive
region in a recursive manner.
Based on the aforementioned algorithm, the extension of the homogeneous region for
an MMF will cease if either of the two conditions is not met. In this case, the extension of
the KAP will be terminated, the value of the KAP is replaced with the mean of the pixels
within the adaptive region and the algorithm will skip to the ith + 1 KAP. The whole image
is scanned in this manner, and each pixel will be taken as once KAP.
774
Z. LV ET AL.
Downloaded by [University of Florida] at 02:38 25 October 2017
2.2. Rectification-operation within the MMF region
It is worthy noting that when processing a VHR image using MMF, due to the spatial
complexity of pixels or the spectral heterogeneity of the ground objects, a hollow contour
of the region may be generated in the processing of an adaptive region-extension. For
examples, two different extensions are demonstrated in Figure 2. It can be found that the
appendages of the buildings (such the pump-house and chimney on the top of building)
or the sun’s height will all lead to noise in the VHR remote-sensing image. Moreover, this
noise significantly affects the extension of the MMF region. If the current KAP is an
objective pixel, the fitted parameters (T1 and T2 ) may result in a hollow region of MMF,
such as the second column in Figure 2. An object in an image scene, such as a building, a
road, is described by a group pixels, which are similar in spectra and continuous in spatial
domain. Thus, ideally, when a pixel of an object is taken as a KAP, the extension surrounds
the KAP should be a solid region, besides the peculiar shape object (such as a circle shape
meadow). However, the value of pixels for an objective object is usually heterogeneous for
VHR remote-sensing image. The heterogeneity may cause a hollow region for an MMF’s
extension, such as the extension demonstration in Figure 2.
To enhance an objective KAP’s filtering effect and weaken the influence of the noise’s
KAP, a rectification strategy is proposed here. The strategy used here is to remove the
noise pixels from inside a homogeneous objective area, which is defined by (2):
OðPi Þ ¼
l¼n
1X
vl :
n l¼0
(2)
The above formula represents the operation to replace the hollow pixel within an
adaptive region of MMF, where OðPi Þ is the rectified value of a noise pixel. A ‘noise pixel’
is the pixel which is within the adaptive region in spatial domain. Above, vl is the value
of a pixel that is labelled as being in the extension of the MMF region, n is the total
Figure 2. Examples of extension for an MMF and different classes.
INTERNATIONAL JOURNAL OF REMOTE SENSING
775
number of labelled pixels, and OðPi Þ is the rectified mean value of labelled pixels. The
hollow pixels were rectified by the mean of the labelled pixels, which ensures that the
pixels of the MMF region have a higher homogeneity. In other words, when the hollow
pixels within the region of an MMF are filled, the adaptive region becomes a solid
region, and all of the labelled pixels consisting of the adaptive solid-region are similar in
terms of their spectral values.
2.3. Calculation of the mean value of the labelled pixels
After determining the adaptive region of an MMF, the MMF is defined as
Downloaded by [University of Florida] at 02:38 25 October 2017
MMFðxi Þ ¼
l¼n
1X
vl ;
L l¼0
(3)
where xi is took as a KAP, MMFðxi Þ is the filtering process of the xi , L is the total number
of the pixels within the adaptive region, and vl is the value of a pixel. The value obtained
with KAP filtering by its MMF is equal to the average mean of the labelled pixels within
the adaptive region.
It is worthy to note that the MMF differs from the traditional MF in the following
aspects:
(1) MF simply replaces each pixel value in an image with the mean (‘average’) value
of its neighbours. This has the effect of eliminating pixel values that are unrepresentative of their surroundings. The mean filter is usually thought of as a regular
filter, such as a 3 3 window or a 5 5 window. However, the MMF filters an
image by replacing each pixel value with the mean of its adaptive region. The aim
of the MMF is to describe the overall contextual features and let all pixels of an
object have a higher similarity in the spectra.
(2) MF yields to linear filters, and it can not only remove noise, but it also smooths
the edges and boundaries and may ‘erase’ details whose size is not equal to the
window size. As a result, an image filtered by MF becomes ‘blurred’. However, the
proposed MMF is adaptive in the spatial domain, and the filtered value of a pixel
depends on the spectral difference between a kernel-anchored pixel and its
contextual pixels. Thus, MMF joints the spectral and spatial feature together.
The extension of an MMF region is self-adaptive and constrained by two parameters: T1 and T2 . Hence, MMF not only smooths the intra-class noise, but it also
retains the boundary between inter-classes.
To illustrate the advantage of the proposed MMF, a VHR images is, respectively,
filtered with the MMF and the traditional MF, and the results are compared in
Figure 3. The local variance of each band is compared using the same window for the
filtered image, ‘RGB-Var’ is referenced the variance value for Red, Green and Blue band
respectively. A lower variance shows a higher homogeneity of the ground object. For
example, as shown in Figure 3(a), the variance of the pixels which are within the red
window for band-1, band-2, and band-3 is 98, 45, and 61, respectively. It can be found
that the proposed MMF has an advantage in improving the homogeneity of the ground
Downloaded by [University of Florida] at 02:38 25 October 2017
776
Z. LV ET AL.
Figure 3. Filtering result comparison between the MF and MMF using a VHR image. (a) A raw VHR
image with a 1.0 m resolution and three R–G–B bands; (b), (c) and (d) are the results processed by
an MF with different window sizes; (e), (f), and (g) are the results processed by the MMF with
different constraint parameters,T1 and T2 .
object, and the MMF can also smooth the building lawns and retain the edges between
them and their surroundings, as shown by the yellow arrow in Figure 3.
3. Experimental
Two VHR images acquired by ROSIS-03 sensor and IKONOS-2 satellite were utilized to
validate the feasibility and effectiveness of the proposed MMF approach through
classification. Three parts were designed to achieve the aims. First, the images were
described for each experiment. Second, experimental setup and parameter settings were
presented. Finally, the compared results and discussion were given.
3.1. Data sets
The first data set is the ROSIS-03 Pavia University image scene with a 1.0 m spatial
resolution. The original data set is 610 × 340 pixels, and a total of 12 bands were
removed because of noise. A false colour composite of the image using channels 10, 27
and 46 for red, green, blue, respectively, is shown in Figure 4(a). The ground truth is
shown in Figure 4(b). Nine classes of interest were considered in this article: trees,
asphalt, bitumen, gravel, painted metal sheets, shadows, bricks, meadows, and soil.
The second data set is an IKONOS-2 image from Reykjavik city, Iceland, which was
used in the experiment to assess the proposed MMF. The IKONOS image is a highresolution panchromatic image with spectral coverage from 0.45 to 0.9 µm. The size of
the image is 957 × 639 pixels, with a 1.0 m spatial resolution. Six classes were considered
in this case: small buildings, open areas, shadows, large buildings, street and residential
lawns. The original data and the available ground reference truth are shown in Figure 5.
Downloaded by [University of Florida] at 02:38 25 October 2017
INTERNATIONAL JOURNAL OF REMOTE SENSING
777
Figure 4. False colour original image (a) and the ground reference data (b) of Pavia University.
Figure 5. IKONOS-02 panchromatic VHR image from Reykjavik, Iceland and the available ground
reference samples: (a) original image; (b) the ground reference map.
3.2. Experimental setup and parameter settings
The first experiment has two purposes, i.e. to test the effectiveness of the proposed MMF
in the classification of a VHR remote-sensing satellite image and to explore the relationship between T1 , T2 ; and the accuracy of the classification. The proposed MMF-based
778
Z. LV ET AL.
Table 1. Training and test samples for the ROSIS-03 Pavia university image.
Name
Downloaded by [University of Florida] at 02:38 25 October 2017
Asphalt
Meadows
Gravel
Trees
Painted metal sheet
Bare soil
Bitumen
Self-blocking
Shadows
No. of training pixels
No. of ground reference pixels
603
412
182
382
46
512
189
414
88
6631
18,649
2099
3064
1345
5029
1330
3682
947
VHR image classification was compared with that of the raw image and with other
similar filters, such as MF, MedF, and RF. The training and test samples for each class are
detailed in Table 1.
The parameters for each approach are detailed as follows:
Original image: Three bands, 10, 27, and 46, were taken from the original data as the
false-colour image according to the guidance of ROSIS-03 sensor, and these bands were
placed into the SVM classifier as spectral features for classification. The RBF kernel
function was used, and the parameters were set by cross-validation. The obtained
gamma parameter was 0.33, and the penalty parameter was 100.0.
Original image filtered by MF: the original image was smoothed using MF with a 5 5 window size. The processed spectral feature was used for classification based on the
SVM with the RBF, and the parameters (gamma 0.33, penalty parameter 100.0) of the
SVM were acquired through cross validation (CV).
Original image filtered by MedF: As in MF, a window size of 5 5 was used to filter
the original image based on MedF. Then, the filtered bands were used for classification
using the SVM with the RBF. The gamma and penalty parameters of the SVM that were
obtained through CV were 0.33 and 100.0, respectively.
Original image filtered by RF: the classification map based on the original image using
SVM is filtered by RF which has been proposed in (Kang, Shutao, and Benediktsson
2014). The relative optimal parameters were δs ¼ 200; δr ¼ 20, and the number of iteration is 3.
Original image filtered by the proposed MMF: the proposed MMF was used to filter
the original spectral bands. The parameters of the proposed MMF were set as follows:
T1 ¼ 50 and T2 ¼ 100. After the original spectral bands were smoothed by the proposed
MMF with the preset parameters T1 and T2 , the filtered spectral bands were put into the
SVM for classification. The SVM classifier with RBF was adopted, and the parameters
were obtained through CV. The gamma parameter was 0.33, and the penalty parameter
was 100.0.
In the second experiment, an IKONOS-02 image with a very high spatial resolution
from the city of Reykjavik, Iceland, was used in the experiment to demonstrate the
proposed MMF is suitable for processing panchromatic image with high spatial resolution. The training samples and test pixels are detailed in Table 2. The training samples
were selected randomly, and the available reference samples are shown in Figure 5. In
addition, the parameters used in this experiment are detailed as follows: the window
size adopted in MF and MedF is 5 5 pixels, and T1 and T2 are set, respectively, to 35
INTERNATIONAL JOURNAL OF REMOTE SENSING
779
Table 2. Training and test samples for the IKONOS Reykjavik panchromatic images.
Name
Small buildings
Open areas
Shadows
Large buildings
Streets
Residential laws
No. of training pixels
No. of ground reference pixels
1526
7536
1286
2797
3336
5616
34,155
25,806
43,867
39,202
30,916
35,147
Downloaded by [University of Florida] at 02:38 25 October 2017
and 200 for the extension of the MMF region. δs ¼ 300 and δr ¼ 40 are adopted here for
the filter RF. Finally, the parameters of the SVM with the RBF kernel were optimized
through CV.
3.3. Experimental results
In the first experiment, the proposed MMF was evaluated and compared with other
approaches, including the traditional MF, MedF, and RF. The classification maps are
shown in Figure 6, and the class-specific accuracies for different approaches are compared in Table 3. From this table, it can be seen that the raw image filtered by the
proposed MMF exhibited an overall accuracy (OA) of 68.8% and a kappa coefficient (κ) of
0.611. Compared with the raw image without any filter processing, the proposed MMF
achieved a higher classified accuracy. Compared with the raw image processed using
MF, MedF, and RF, the classification accuracy of the proposed MMF is competitive in
terms of OA and κ.
The class-specific accuracies of the second experiment are shown in Table 4, and the
visual classification maps are shown in Figure 7. From the table and its corresponding
classification map, it can be found that (1) the VHR panchromatic image processed by a
filter, such as MF, MedF, RF or the proposed MMF, can produce a better classification
map and higher classifying accuracies than that without any filtering process and (2)
comparing the proposed MMF-based classification with those of MF, MedF and RF, the
proposed MMF-based classification achieved a higher classification accuracy. Therefore,
the proposed MMF can smooth the noise in the VHR panchromatic image, which is
suitable for improving the performance of classification.
4. Discussion
To enhance the applicability of the proposed approach, the sensitivity between the
parameters (T1 and T2 ) and the classification accuracy was investigated. Parameter T1
indicates the spectral similarity between the kernel-anchored pixel (KAP) and its contextual pixels. Selecting a suitable T1 is the key to obtain a higher accuracy of classification. If T1 is too small that will not smooth enough noise pixels, leading to the cessation
of the extension of the region, whereas a T1 that is too large will remove inter-class noise
and details, also leading to inaccurate inter-class information. Figure 8(a) shows that
when the value of T1 ranges from 15 to 60, the accuracy of the proposed MMF-based
classification initially increases and then stabilizes. When T1 is large enough to extend
the region and obtain the optimum accuracy, T2 will control the size of the extension.
From Figure 8(b), it can be observed that when T1 is fixed at 50, the value of T2 ranges
Z. LV ET AL.
Downloaded by [University of Florida] at 02:38 25 October 2017
780
Figure 6. Comparisons of classification maps based on different approaches for Pavia University VHR
false colour image: (a) classification map based on the raw image without any image processing; (b),
(c), (d), and (e) are classification maps based on the filtered image using MF, MedF, RF, and the
proposed MMF, respectively. (f) Legend of the map.
from 15 to 700, but the accuracy, OA and κ, only range from 64.7% to 68.9% and 0.564
to 0.611, respectively. The reason for this is that a suitable value of T1 ¼ 50 has been set
for constraining the extension. Therefore, when T1 is large enough, T2 will constrain the
size of the extension and the classification accuracy based on the proposed MMF will
move towards a stable trend. Thus, T1 and T2 complement each other, in a practical
application, T1 and T2 can be adjusted and determined in accordance with the different
image.
INTERNATIONAL JOURNAL OF REMOTE SENSING
781
Table 3. Class-specific accuracy (%) for different filtering approaches in the SVM classification of the
Pavia University image.
Information class
Downloaded by [University of Florida] at 02:38 25 October 2017
Asphalt
Meadows
Gravel
Trees
Painted metal sheets
Bare soil
Bitumen
Self-blocking bricks
Shadows
OA
κ
Raw image
MF
MedF
RF [1]
MMF
72.6
45.0
38.1
61.4
99.3
69.2
76.5
78.7
65.0
59.0
0.498
81.8
50.5
52.7
62.2
92.7
67.2
79.1
79.9
93.3
63.9
0.555
88.5
50.1
59.5
65.1
99.6
65.3
90.8
84.4
92.7
66.1
0.581
89.3
85.3
62.7
98.4
89.9
27.4
55.8
71.4
98.5
65.1
0.572
88.0
86.4
69.1
92.9
90.4
31.0
49.4
78.0
98.3
68.8
0.611
Table 4. Class-specific accuracy (%) for different filtering approaches in the SVM classification of the
IKONOS Reykjavik panchromatic images.
Information class
Small buildings
Open areas
Shadows
Large buildings
Streets
Residential lawns
OA
κ
Raw image
20.2
67.14
89.0
43.1
14.6
66.0
48.6
0.377
MF
31.9
60.3
84.2
35.6
41.4
61.1
51.6
0.412
MedF
22.0
60.4
90.8
43.4
51.3
57.0
53.1
0.431
RF [1]
52.3
53.8
93.1
40.1
55.3
41.8
54.7
0.452
MMF
21.3
59.6
93.0
48.7
66.4
56.6
56.6
0.475
Overall, by comparing the result of the two experiments, it is clear that the proposed
MMF-based approach is superior to the classification of the raw images (without any
image filter). Compared with the traditional MF, MedF, and RF, the proposed MMF is
competitive for improving the classification performance of VHR image.
The discussion above reveals that the proposed MMF is an extension of the traditional MF, it is more effective than the traditional MF for smoothing the noise of VHR
images. Furthermore, it is clear that MMF has an advantage over the MF in preserving
edge information. This is not only helpful for improving the accuracies of VHR image
classification but also has the ability to improve the visual performance of raw VHR
images. In comparison to the traditional mean filter, the proposed MMF is competitive in
terms of classification accuracies when applied on VHR images. Currently, VHR images
are used widely for land-cover classification. As a novel and simple spatial filter, MMF
may imply more potential applications in image processing.
5. Conclusion
In this article, we propose a modified mean filter (MMF) for noise reduction of VHR
remote-sensing image classification. The proposed MMF progressively and adaptively
extends the filter’s contextual shape from a kernel-anchored pixel (KAP) to a labelled
pixel group whose members are spectral similar and spatial contiguously. The effect of
the proposed MMF is investigated by the classification of VHR images in two experiments. The main contributions of this study can be summarized briefly as follows:
Downloaded by [University of Florida] at 02:38 25 October 2017
782
Z. LV ET AL.
Figure 7. Classification maps obtained based on different image filters for the IKONOS-02-Reykjavik
city image: (a), (b), (c), (d), and (e) are classification map based on the raw image without any image
processing, MF, MedF, RF, and MMF, respectively.
(1) It is the first time that region growing algorithm integrated with mean filter for
noise reduction of VHR image classification. Due to the benefit of this integration,
the proposed MMF is adaptive according to the shape, size and spectral difference
of a considered ground object. Thus, the proposed MMF not only can reduce the
noise of intra-class, but also can preserve the boundary of inter-classes.
(2) The proposed MMF-based classification system provides competitive classification
accuracies when compared with the MF, MedF and RF for VHR image classification. In
addition, the proposed MMF based classification system performs better than the MF,
MedF, RF in an experiment on the high spatial resolution panchromatic image. This
indicates that the proposed MMF is not only effective for a VHR false colour image,
but also demonstrates robustness for a panchromatic image. Thus, the proposed
MMF may imply more potential applications than the MF, MedF and RF.
Downloaded by [University of Florida] at 02:38 25 October 2017
INTERNATIONAL JOURNAL OF REMOTE SENSING
783
Figure 8. Relation between the overall accuracy (OA) and the Kappa coefficient (κ) against the
spectral similarity (T1) and Region size (T2) for an MMF-based classification of the Pavia University
image: (a) T1 vs. OA and κ; (b) T2 vs. OA and κ.
In theory, more adaptive region features can be explored than that of a single
feature. Therefore, in future research, such additional features will be explored and
investigated. In addition, the automation of parameters (T1 and T2) can be considered. If T1 and T2 are acquired in an automated manner by considering the spectral
difference of the contextual pixels, then the result will be an image filter with a
higher degree of automation. The advantage of such an approach may be more
practicable than that of the proposed approach for VHR image classification. In
addition, with the fast development of remote-sensing technology, many VHR
images are available conveniently, including the unmanned aerial vehicle (UAV)
image, the proposed MMF may play an important role in image processing, such
as the UAV images with very high spatial resolution.
Acknowledgements
The authors would like to thank the editor-in-chief, the anonymous associate editor, and the
reviewers for their insightful comments and suggestions. This work was supported by National
Natural Science Foundation of China (41331175 and 61701396), National Administration of
Surveying, Mapping and Geoinformation, P.R. China (Technical Leading Talents) and the Hong
Kong Polytechnic University (1-ZE24, 1-ZVF2), Shaanxi Natural Science Foundation (2017JQ4006)
and the project from the China Postdoctoral Science Foundation (2015M572658XB).
Disclosure statement
No potential conflict of interest was reported by the authors.
784
Z. LV ET AL.
Funding
This work was supported by the National Natural Science Foundation of China
[41331175,61701396]; National Administration of Surveying, Mapping and Geoinformation, P.R.
China (Technical Leading Talents) and the Hong Kong Polytechnic University [1-ZE24, 1-ZVF2]; the
China Postdoctoral Science Foundation [2015M572658XB]; and Shaanxi Natural Science
Foundation [2017JQ4006].
ORCID
LiPeng Gao
http://orcid.org/0000-0003-0026-3719
Downloaded by [University of Florida] at 02:38 25 October 2017
References
Benediktsson, J. A., M. Pesaresi, and K. Amason. 2003. “Classification and Feature Extraction for
Remote Sensing Images from Urban Areas Based on Morphological Transformations.” IEEE
Transactions on Geoscience and Remote Sensing 41 (9): 1940–1949. doi:10.1109/
TGRS.2003.814625.
Blaschke, T. 2010. “Object Based Image Analysis for Remote Sensing.” ISPRS Journal of
Photogrammetry and Remote Sensing 65 (1): 2–16. doi:10.1016/j.isprsjprs.2009.06.004.
Duro, D. C., S. E. Franklin, and M. G. Dubé. 2012. “A Comparison of Pixel-Based and Object-Based
Image Analysis with Selected Machine Learning Algorithms for the Classification of Agricultural
Landscapes Using SPOT-5 HRG Imagery.” Remote Sensing of Environment 118: 259–272.
doi:10.1016/j.rse.2011.11.020.
Fauvel, M., Y. Tarabalka, J. A. Benediktsson, J. Chanussot, and J. C. Tilton. 2013. “Advances in
Spectral-Spatial Classification of Hyperspectral Images.” Proceedings of the IEEE 101 (3): 652–675.
doi:10.1109/JPROC.2012.2197589.
Gianinetto, M., M. Rusmini, G. Candiani, G. D. Via, F. Frassy, P. Maianti, A. Marchesi, F. R. Nodari, and
L. Dini. 2014. “Hierarchical Classification of Complex Landscape with VHR Pan-Sharpened
Satellite Data and OBIA Techniques.” European Journal Remote Sens 47: 229–250. doi:10.5721/
EuJRS20144715.
Huang, X., and L. Zhang. 2008. “An Adaptive Mean-Shift Analysis Approach for Object Extraction
and Classification from Urban Hyperspectral Imagery.” IEEE Transactions on Geoscience and
Remote Sensing 46 (12): 4173–4185. doi:10.1109/TGRS.2008.2002577.
Huang, X., and L. Zhang. 2013. “An SVM Ensemble Approach Combining Spectral, Structural, and
Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery.” IEEE
Transactions on Geoscience and Remote Sensing 51 (1): 257–272. doi:10.1109/
TGRS.2012.2202912.
Kang, X., L. Shutao, and J. A. Benediktsson. 2014. “Feature Extraction of Hyperspectral Images with
Image Fusion and Recursive Filtering.” IEEE Transactions on Geoscience and Remote Sensing 52
(6): 3742–3752. doi:10.1109/TGRS.2013.2275613.
Li, J., H. Zhang, and L. Zhang. 2014. “Supervised Segmentation of Very High Resolution Images by
the Use of Extended Morphological Attribute Profiles and a Sparse Transform.” IEEE Geoscience
and Remote Sensing Letters 11 (8): 1409–1413. doi:10.1109/LGRS.2013.2294241.
Li, M., S. Zang, B. Zhang, L. Shanshan, and W. Changshan. 2014. “A Review of Remote Sensing
Image Classification Techniques: The Role of Spatio-Contextual Information.” European Journal
of Remote Sensing 47: 389–411. doi:10.5721/EuJRS20144723.
Liu, J., W. Zebin, Z. Wei, L. Xiao, and L. Sun. 2013. “Spatial-Spectral Kernel Sparse Representation for
Hyperspectral Image Classification.” IEEE Journal of Selected Topics in Applied Earth Observations
and Remote Sensing 6 (6): 2462–2471. doi:10.1109/JSTARS.2013.2252150.
Lv, Z. Y., P. Zhang, J. A. Benediktsson, and W. Z. Shi. 2014. “Morphological Profiles Based on
Differently Shaped Structuring Elements for Classification of Images with Very High Spatial
Downloaded by [University of Florida] at 02:38 25 October 2017
INTERNATIONAL JOURNAL OF REMOTE SENSING
785
Resolution.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7
(12): 4644–4652. doi:10.1109/JSTARS.2014.2328618.
Moser, G., S. B. Serpico, and J. A. Benediktsson. 2013. “Land-Cover Mapping by Markov Modeling of
Spatial–Contextual Information in Very-High-Resolution Remote Sensing Images.” Proceedings of
the IEEE 101 (3): 631–651. doi:10.1109/JPROC.2012.2211551.
Ouyang, Z.-T., M.-Q. Zhang, X. Xie, Q. Shen, H.-Q. Guo, and B. Zhao. 2011. “A Comparison of PixelBased and Object-Oriented Approaches to VHR Imagery for Mapping Saltmarsh Plants.”
Ecological Informatics 6 (2): 136–146. doi:10.1016/j.ecoinf.2011.01.002.
Tarabalka, Y., J. A. Benediktsson, and J. Chanussot. 2009. “Spectral–Spatial Classification of
Hyperspectral Imagery Based on Partitional Clustering Techniques.” IEEE Transactions on
Geoscience and Remote Sensing 47 (8): 2973–2987. doi:10.1109/TGRS.2009.2016214.
Tzotsos, A., K. Karantzalos, and D. Argialas. 2011. “Object-Based Image Analysis through Nonlinear
Scale-Space Filtering.” ISPRS Journal of Photogrammetry and Remote Sensing 66 (1): 2–16.
doi:10.1016/j.isprsjprs.2010.07.001.
Wang, L., Q. Dai, L. Hong, and G. Liu. 2012. “Adaptive Regional Feature Extraction for Very High
Spatial Resolution Image Classification.” Journal of Applied Remote Sensing 6 (1): 063506-1–16.
doi:10.1117/1.JRS.6.063506.
Wilkinson, G. G. 2005. “Results and Implications of a Study of Fifteen Years of Satellite Image
Classification Experiments.” IEEE Transactions on Geoscience and Remote Sensing 43 (3): 433–440.
doi:10.1109/TGRS.2004.837325.
Xia, J., J. Chanussot, D. Peijun, and X. He. 2015. “Spectral–Spatial Classification for Hyperspectral
Data Using Rotation Forests with Local Feature Extraction and Markov Random Fields.” IEEE
Transactions on Geoscience and Remote Sensing 53 (5): 2532–2546. doi:10.1109/
TGRS.2014.2361618.
Документ
Категория
Без категории
Просмотров
0
Размер файла
3 443 Кб
Теги
2017, 01431161, 1390275
1/--страниц
Пожаловаться на содержимое документа