IRJET- Remote Sensing Image Retrieval using Convolutional Neural Network with Weighted Distance and Result Re-Ranking

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 5331
Remote Sensing Image Retrieval using Convolutional Neural Network
with Weighted Distance and Result Re-Ranking
Arjun N.K
M.Tech Student, Dept. of CSE Engineering, AWH Engineering College, Kerala, India
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Abstract - Remote sensing image retrieval (RSIR) is a
fundamental task in remote sensing. This letter proposes a
content based image retrieval method based on weighted
distance and basic features of Convolutional network. in this
method there are two stages. First, in offline stage, the pre-
trained CNN is fine-tuned by set of labeled images and extract
CNN features to label retrieval data set. Second, in online
stage, we use the fine-tuned CNN model to extract the CNN
feature of the query image , and calculate the weight of each
image class and apply them to calculate the distance between
the query image and the retrieved images. HOG and LBP
techniques are used with CNN to extract features. a feedback
based reranking method is introduced to optimize the
retrieved images Experiments areconductedontwoRSIR data
sets. Compared with the state-of the- art methods, the
proposed method is simplified but efficient, significantly
improving retrieval performance.
Key Words: Convolutional neural network (CNN), remote
sensing image retrieval (RSIR), weighted distance.
1. INTRODUCTION
Remote sensing images becomeveryimportantinthefieldof
geological analysis, urban planning, natural disaster
monitoring and assessment, weather prediction, resource
investigation, and so on, with the development of remote
sensing technology, both quality and quantity of remote
sensing (RS) images have been growing quickly. How to
automatically and efficiently retrieve the remote sensing
images from large image databases becomes one of the
challenging and emerging research topics in the field of
remote sensing. Content based image retrieval (CBIR) is a
useful method to solve the hard problem, which is based on
the features of image, such as intensity, shape, texture, and
structure.
Over the years, a rich literature has been developed on high
resolution remote sensing (HRRS) image retrieval problem.
Researchers have applied different forms of descriptors on
RS retrieval purpose, and these descriptors can be classified
into two main categories by default. There includes local
features extracted at interest point locations, such as
intensity features, spectral features, shape features,
structural features , texture features, etc. and also global
features, obtained by encoding local features (i.e., the scale-
invariant feature transform (SIFT)) into a single vector
representation using bag-of-words (BoW), vector locally
aggregated descriptors (VLAD), or their variants.
Nonetheless, the common traditional image representations
have very limited capability in coping with the complexity of
HRRS scenes and multi formats of HRRS categories. To
overcome the weakness of existing HRRS image retrieval
methods, we employ deep features derived from
Convolutional Neural Networks (CNNs) to displace
conventional representations. CNNs have attained amazing
achievements in computer vision research area during
recent years, except for original image classification task,
CNNs proved to have transferabilityasverypowerful feature
extractor. Deep features are prominent in solving various
visual problems, such as image classification, object
detection, fine-grained recognition, and image instance
retrieval.
This letter uses a weighted distance and the simple CNN
features to retrieve image. We fine-tune the pretrained CNN
model to calculate the weight of each class in the retrieved
data set for query image. We give more preference to the
retrieved images in more similar classes with the query
image. We also use HOG and LBP features for feature
extraction and feedback oriented reranking method to filter
and sort the result the framework of our method is shownin
Fig. 1.
Experiments show that the retrieval method with weighted
distance can retrieve more desiredimages.The remainderof
this letter is organized as follows. In Section II, our remote
sensing image retrieval method based on CNN features and
weighted distance is described. In Section III, experimental
results and analyses are presented. We conclude this letter
in Section IV.
2. METHODOLOGY
In this section, we first briefly introduce two CNNs that we
use and the fine-tuning with the two pretrained CNNmodels,
then describe the CNN features used to retrieve image. Next,
the weighted distance is presented. Finally,theprocessofthe
proposed image retrieval method is described.
2.1. CNN Models
CNN architecture is the most effective deep learning
approach in various fields such as image classification,object
recognition, and image scene analysis, CNN consist of many

layers such as convolution, pooling, and fully connected
layers. The network architecture used is the Convolutional
neural network model called VGG-16. Fig.1. shows the VGG-
16 architecture. This architecture has got 16 layers which
include convolution layers followed by pooling layers and
finally the fully connected layers. Weight sharing is
performed in Convolutional layer and in pooling layers the
sub sampling of outputs of the convolutionlayerforlowering
the data ratefrom the previousconvolutionlayerisdone.The
main feature of the VGG-16 network is the fixed size of input
is 224 x 224..
2.2. Fine-Tuning the CNN Models
CNNs have been dominant in feature extraction, and have
gradually replaced traditional methods in the field of
computer vision. The achievement of CNNs is mainly due to
the fact that deep network structures bringalargenumberof
nonlinear functions, and weight parameters can be
automatically learned from the training data. However,
remote sensing image datasets cannot provide a large
amount of data for CNNs training from scratch. CNNs pre-
trained on massive datasetshavebeenusedtoextractfeature
embedding, which has been proven to be effective and
efficient, even when the training data set has a lot of
difference with the remote sensing image. It is difficult to
train a novel model from the beginning because of the lack of
data and hard adjustment of parameters.
Fig. 1. Framework of our proposed approach.
So fine-tuning is the best way to get a better effect with few
iterations by adjusting the pretrained parameters to better
suit the target data set. In this letter, the pretrained CNN
models, VGG and ResNetpretrainedonImageNetdataset,are
fine-tuned using the target retrieval data set. The two
pretrained CNNs are implemented and fine-tuned using
MatConvNet, which is a MATLAB application of CNNs for
computervisionapplications.MatConvNetgetsadvantagesin
CNN building blocks under MATLAB which is a friendly
development environment.
2.3 Feature Extraction
The outputs from the higher level layers of CNN (e.g.,Fc6and
Fc7 from VGG Model) have been shown to be very effective
generic features which achieve state-of-the-art performance
in image retrieval . Here, we consider the outputs of high-
level layer (Fc6 and Fc7) from the fine-tuned VGG16 model
and the last convolutional layer (Pool5) from the fine-tuned
ResNet50 model as the features for remote sensing image
retrieval.
There are two methodsareintroducedwithCNNarchitecture
to extract image features are
2.3.1 Local Binary Pattern (LBP):
LBP feature extractor has been applied in many areas
including text identification and face recognition Classical
LBP feature extractor finds the intensity values of points in a
circular neighborhood by considering the values which have
small circular neighborhoods around the central pixel .
2.3.2. Histogram of Oriented Gradients (HOG):
HOG captures featuresbycountingtheoccurrenceofgradient
orientation. Traditional HOG divides the image into different
cells and computes a histogram of gradient orientationsover
them. HOG is being applied extensively in object recognition
areas as facial recognition.
2.4. Labeling Image Retrieval Data Set
Each image in the retrieval data set needstobelabeledwhich
class it belongs to. We feed each image into the fine tuned
model and apply the softmax function to the output of the
fine-tuned model forconverting the output totheprobability
for each class.
where a is a K vector of the inputs to the output layer, and
i indexes the output units, i = 1, 2, . . . , K. Theclass of image I is
predicted as the class with the max probability.
2.5. Weighted Distance
CNN is very effective in distinguishing image class, so we use
the ability of CNN to improveremotesensingimageretrieval.
The motivation of weighted distance is to give more
preference to the retrieved images in more similar classes
with the query image. The more the class probability of the
query image, the less is thedistancebetweenthequeryimage
and the retrieved images of that class. When a query image q
is fed into the fine-tuned model, we get a probability pq for
each class and use it to calculate the weight of a retrieved
image r in the retrieval data set according

where k is the class of the image r . The weighted distance
between the query image q and the retrieved image r is
reformulated as
dw(q, r ) = w × d(q, r )
where d(q, r ) is calculated using the conventional distance
chosen suitably. The Euclidean distance is used to evaluate
the performance of the proposed method, the top row is the
retrieval results using the distance d(q, r ); there are five
irrelevant images in the top nine results. While the bottom
row is the results using the weighted distance dw(q, r ), the
top nine results are all correct. The method with the distance
d(q, r ) returns six irrelevant images, while the method using
the weighted distance dw(q, r ) only returns three irrelevant
images. It can be seen thattheweighteddistancecanimprove
the retrieval result
Fig. 2. Comparison of image retrieval results between
different distances. (a) and (b) are two retrieval examples.
In both (a) and (b), (Top row) Results with the distance
d(q, r), (Bottom row) Results of the weighted distance
dw(q, r), (First column) Query images.
2.6. Process of Image Retrieval
The framework of the proposed method is shown in Fig. 1. It
consists of two parts: the offline part and the online part. In
the offline part, we fine-tune the pretrained CNN model with
some labeled images. Then, we label and extract the CNN
features forthe images in the retrieved data set withthefine-
tuned CNN model. Finally, we build a data set with feature
vectors and class labels. In the online part of our method, a
query image is fed into fine-tuned CNN modeltocalculatethe
CNN features and the class probability. Then, the weighted
distances between the query image and the retrievedimages
are computed. After that, we sort the retrieved images in
ascending order according tothe weighteddistances.Finally,
we get the retrieval results.
2.7. IMPROVED K -MEANS CLUSTERING (lKC) ALGORITHM
Clustering is widely studied in data mining community. It is
used to partition data set into clusters so that intra-cluster
data are similarandinter-clusterdataaredissimilar..Kmeans
is one of the most widely used clustering algorithms. Its
essence is to reach the purpose of stepwise refinement
through iteration. It has the following steps:
I) Pick K data points randomly to represent the initial
centers of clusters;
2) Repeat steps (3) and (4), until the clusters are
unchanged or criterion function has converged;
3) For each data point, find the cluster center that is
closest to the data point. Assign the data point to the cluster
whose center is closest to it, update K clusters;
4) Recalculate the center of each cluster.
This algorithm is based on the squared-error criterion .In
traditional K-means algorithm, the choice of initial centers is
random, so the clustering results are usually only locally
optimal and fluctuant. If we choose some more appropriate
initialcenters, clustering resultswillbemorereasonable,and
the speed of clustering will also be faster. To remedy this
problem, we take the following steps:
1) Calculatethe distances of the data pointsbetweeneach
other in the data set U;
2) Select two nearest data points to find a subset AI, and
remove them from the data set U;
3) Calculate the distances between the subset AI and the
remaining data points in data set U, and put the data that is
nearest to subset AI into AI. Repeat this processuntilthedata
points in Al reach a certain number. The distances between
the subset Al and a data point can be calculated by the
following formula:
d ( x , AI) = mine d ( x , y), y E A 1 )
4) Repeat steps (2) and (3) until K subsets AI, A 2 , ... , A k
are formed;
5) Calculatethe average values of the K subsets: ml, m2,...
mk . We choose ml, m2, ... mk as initial centers. Calculate the
distances of the data points between each other in the data
set U;
2.8. RELEVANCE FEEDBACK
Through applying IKC, the image search result can be
partitioned into K clusters and K cluster centers are formed.
The image that is nearest to the center is deformed as
representative image of the cluster. There are two steps in
relevance feedback: In the first step, the user is required to
label the relevant representativeimagesoftheKclusters.The
clusters whose representativeimagesarelabeledcanforman
image set G. For the image set G, we calculate the standard
deviation of the ith dimension of the feature vectors, and its
inverse is assigned to update the corresponding weight

formula. In the second step, we recalculate the distance
between the query image q and the image p in search result
using the following formula:
dis(Hq,Hp) = aDw(Hq,Hp)+ f/Dw(Hq,mp)
where mp is the center of the cluster Cp which the image p
belongs to, and Dw(H q, mp) is the weighted Euclidean
distance between the query image and the cluster Cp • a and
f/ are weighting factors to adjust the importance of
Dw(Hq,Hp) and Dw(Hq,mp) The distance between thequery
and the images in the nearer cluster is adjusted to a smaller
value, otherwise the distance is modified to a larger value by
formula . According to formula, we re-order the ranks of the
images in search result in decreasing order.
3. EXPERIMENTS AND ANALYSIS
Here, we evaluate the performance of the proposed method
for CBRSIR on two remote sensing data sets of urban area
which are cropped from the Google kerala Map. Each class
has 100 images with 256×256 pixels. The other one is rural
area dataset cropped from the Google kerala Map. The data
set contains 38 classes, each of which has 800 images.
3.1 Per Class mAP
We evaluate per class mean average precision (mAP) values
for the CNN features on both data sets average values of the
methods using the pretrained feature without the weighted
distance are poor performance and methods with the
weighted distance achieve noticeablybetter results.Average
values of the methods using the pretrained feature are
approximately 70%, while our methods are approximately
92%. It can conclude that our methods can get better results
than the pretrained features. Moreover, it can be filter and
sorted out the result using feedback based re ranking.
4. CONCLUSION
In this letter, we presented a remote sensing image retrieval
method based on weighted distance and CNN. In the offline
stage, we used the fine-tuned CNN models to extract image
features and label the class of image in the retrieval data set.
In the online stage, we calculated the weight of each class
according to the class probability of the query image and
used it to adjust the distance between the query image and
the retrieved images. K-means clustering and relevance
feedback to re-rank the search result. The experimental
results demonstrate that the proposed method achieves
better performance compared with existing methods
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BIOGRAPHIES
ARJUN N.K, M.Tech studentofAWH
Engineering college, APJ Abdul
Kalam Technological University,
Kerala India.

IRJET- Remote Sensing Image Retrieval using Convolutional Neural Network with Weighted Distance and Result Re-Ranking

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IRJET- Remote Sensing Image Retrieval using Convolutional Neural Network with Weighted Distance and Result Re-Ranking