Satellite Image Classification and Analysis using Machine Learning with ISRO LISS IV

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 05 | May 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 1479
Satellite Image Classification and Analysis using Machine Learning with
ISRO LISS IV
Tanya Jain1, MOHD Jawed Khan2, Shubham Pandey3, Shivam Mishra4, Shubham Yadav5
1,3,4,5 Department of Computer Science & Engineering (1813210167)
2Assistant Professor Department of Computer Science & Engineering, GNIOT( Engineering Institute)
Greater Noida Institute Of Technology, Greater Noida
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Abstract - For several years, detecting objects in satellite imagery has been a difficult task. Higher accuracy in the
identification of different artifacts from very high-resolution satellite images has been achieved thanks to the creation of
successful machine learning algorithms and advancements in hardware systems. Satellite imaging has been successfully used
for weather forecasts and spatial and geological purposes over the last few decades. For these types of applications, low-
resolution satellite images are adequate. In our project, high-resolution images will be used where the prediction is done by
processing their images and producing respective valid data for the same. The high-resolution satellite images are being
provided by RRSC- C ISRO,Nagpur. For autonomous systems to interact with their environment intelligently, they must be given
the ability to adapt and learn incrementally and deliberately. Thisproject focuses on extracting and identification of objects
from the high-resolution satellite images which will be provided as aninput from the satellite to extract the features of the
image which are then converted into machine valid data. This valid data is then fed to the neural network model. The neural
network modelanalyzes each image and classifies it with the feature label. This will generate the output screen which displays
the extracted feature from the original image which is given as input to the system.
Keywords: Image Classification, Machine Learning, CNN, Supervised Technique, Support Vector Machine.
I. Introduction
Image classification is an important part of remote sensing, image analysis, and pattern recognition. In some instances, the
classification itself may be the object of the analysis. For example, the classification of land use from remotely sensed data
produces a map-like image as the final product. Image classification, therefore, forms an important tool for the examination
of digital images. At present, there are different image classification procedures used for different purposes by various
researchers. Over the last decade, remote sensing technologies are available easily and in abundance. Recently remote
sensing images are being used widely for urban area classification and change detection. Classification is used for various
applications like Crops, Cars, Roads, Trees, Struct, Buildings, Healthy water, Wastewater, Railway routes, and Trucks. The
selection of a suitable classifier to process the satellite images has an important role in the efficient and accurate
classification results. In this work, we are creating a system to classify satellite images to extract information using image
processing techniques. Classification of satellite images into used and unused areas and also sub-classing of each of the
classes into four different classes has been carried out. Used satellite images are further classified into residential, industries,
highways, croplands, and unused images. Manual classification by using image interpretation techniques requires more time
and field experts. So in our work, we focused on efficient automatic satellite image classification. Convolutional neural
networks are used for feature extraction and classification of satellite images.
CNN is a deep neural network that is most suitable when we deal with images. CNN will help to provide higher classification
accuracy. A confusion matrix is used to estimate the overall classification accuracy. Deep learning is a class of machine
learning models that represent data at different levels of abstraction utilizing multiple processing layers. It has achieved
astonishing success in object detection and classification by combining large neural network models, called CNN with
powerful GPU.
CNN-based algorithms have dominated the annual ImageNet Large Scale Visual Recognition Challenge for detecting and
classifying objects in photographs. This success has caused a revolution in image understanding, and the major technology
companies, including Google, Microsoft, and Facebook, have already deployed CNN-based products and services. Satellite
image classification is the most significant technique used in remote sensing for the computerized study and pattern
recognition of satellite information, which is based on diverse structures of the image that involve rigorous validation of the
training samples depending on the used classification algorithm. It is an extreme part of remote sensing that depends
originally on the image resolution, which is the most important quality factor in images. Image Classification or segmentation
is a partitioning of an image into sections or regions.

The power of such algorithms depends on the way of extracting the information from the huge amount of data found in
images.
Machine learning is the scientific study of algorithms and statistical models that computer systems use to perform a task
without using explicit instructions, relying on patterns and inference. In our program, machine learning algorithms are
applied to achieve image processing. In ISRO, to perform image processing the scientists have to first convert the image into data
frames and feed this information into a system application where manually they have to change the bands depending on the
image and identify objects in the image. This process is time-consuming and sometimes can lead to errors as the data is not
precise which can lead to inaccurate processing of the data. Ourproject builds a system that can identify the number of bandsof
the image and create precise data that can be fed to the system this data is then processed using the concept of a
convolutional neural network to obtain feature extraction and object recognition without any manual effort. The primary
concept of the project is to focus on the information which willbe obtained by processing the images or datasets that are made
available from the sensor. The sensor that is used for our project is LINEAR IMAGE SELF SCANNING SENSOR – IV, also known as
LISS-IV. The images on the satelliteare captured by the sensor which takes high-resolution images. The high-resolution satellite
images are being provided by RRSC- C ISRO, Nagpur.
Problem Statement:
To segment and detect the images received from satellites by using a deep learning system and image processing
which classifies objects and facilities in high-resolution multispectral satellite imagery.
II. Review Of Literature
A literature review is an evaluative report of information found in the literature related to your selected area of study. The
review is written so that it should give a theoretical base for the research and help you to determine the nature of your
research. When conducting research, a literature review is an essential part of the project because it covers all previous
research done on the relative problem statements and sets the platform on which the current research is based. No new
research can be taken seriously without first reviewing the previous research done on the topic. The current research workis a
literature review on satellite image processing methods and techniques based on the given problem statement. It describes
and provides details on various satellite image classification methods to the analyst. The current literature review
emphasizes automated satellite image classification and algorithmic methods and techniques used for processing. Our
research focuses on finding and surveying different ways to extract features of objects which can be used as fixed parameters
to classify objects with the help of C- 2S and Liss - IV irrespective of the color of the image being given as input, & in our
survey, we found several papers which gave details about different methods which are being used already for this purpose. In
our survey, we found that the best way to classify objects in an image is by converting the images into different bands and
using them to identify the objects to achieve our goals. The conversion of bands can be achieved by using python scripts and
then providing the converted image as input to the trained machine which will be able to process the image received from
the satellite and complete the task assigned to us based on image processing with the help of Liss - IV.
Literature Survey
Remote Sensing [1] is a technique introduced in the early 1960s for data analysis and interpretation. Remote sensing collects a
hugeamount of satellite data. Satellite remote sensing imagery covers a large geographical area with high temporal frequency as
compared to other imagery. Interpretation of these satellite images helps in a variety of applications such as environmental
conservation and management, water resource research, soil quality studies, environmental study after natural disasters,
meteorology simulations, deriving land use and land cover information, and preventing natural disasters, studying climatic
change evolution. Different techniques are used for data extraction from remote sensing images. The classification techniqueis
the most useful technique for image interpretation and information extraction [2]. Satellite image classification groups
together the pixels of the image into several different defined classes [3]. The pixels are grouped based onthe digital values
extracted from the satellite images. The pixelvalues extracted from the satellite images could be a single value as in the case of
grayscale images or a multivariate value for the multispectral, temporal or multi-modal image. The classification helps in
extracting the information contained in different bands [4] of the satellite sensor and the information isextracted in terms of
digital numbers which are then converted to a category. S. Muhammad et al., [5] proposed a supervised satellite image
classification method using the decision tree technique. This method extracts features from satellite imagesbased on pixel
color and intensity. Extracted features assist todetermine objects that reside in the satellite images. The method classifies
satellite images using adecision tree with the support ofidentified objects. In the paper, "Very High-Resolution Satellite Image
Classification Using Fuzzy Rule-Based Systems”, J. Shabnam et al. [6] introduced a supervised satellite image classification
method to classify very high-resolution satellite images into specific classes using fuzzy logic. This method classifies satellite
images into five major classes: shadow, vegetation, road, building, and bare land. This method uses image segmentation and

fuzzy techniques for satellite image classification. It applies two levels of segmentation, first-level segmentation identifies and
classifies shadow, vegetation, and road. Second level regions. Fuzzy techniques are used to improve the classification
accuracy at the borders of objects. “Land Cover Classification of Satellite Images using Contextual Information” by Bjorn
Frohlich [7] Presents a method for the classification of satellite images into multiple predefined land cover classes. This
method is automated and uses segment-level classification with the support of a training set. The classification methods
include contextual properties of predefined multiple classes to improve classification accuracy. In the paper "Spatial
technique for Image Classification" [8] Selim proposed a classification method using the Bayesian technique. The method
uses spatial information for the classification of high-resolution satellite images. The method performs classification in two
phases. Phase 1: spectral and textural features are extracted for each pixel to train Bayesian classifiers with discrete
nonparametric density models. Phase 2: the iterative split-and-merge algorithm is used to convert the pixel-level
classification maps into contiguous regions. "Comparison of Four Classification Methods to Extract Land Use and Land Cover
from Raw Satellite Images for Some Remote Arid Areas, Kingdom of Saudi Arabia" [9] on ISODATA technique is the most
common unsupervised satellite classification method. It creates a predefined number of unlabeled clusters/classes in a
satellite image. Later meaningful labels are assigned to the clusters. ISODATA parameters need several parameters that
control the number of clusters and iterations to be run. In a few cases, clusters may contain pixels of different classes.
Support Vector Machine (SVM) [10] is a non-parametric unsupervised statistical classification method. This method can be
used to extract land-use maps. SVM works on the assumption that there is no information on how to distribute the overall
data. SVM reduces satellite classification cost, increases speed, and improves accuracy Maximum method is a statistical
supervised approach for recognizing the patterns. It allocates pixels to appropriate classes based on the probability values of
the pixels [11]. Maximum likelihood is an efficient method to classify pixels of the satellite image. But it is time-consuming
and insufficient ground truth data produces poor results. The paper, "Fundamental of Remote sensing" by George joseph [12]
also gives a brief idea about the mechanism involved in capturing satellite imagery in various bands and storing it in a
different format. We have also referred Giss website [13] for basic concepts of satellites, sensors, and information on remote
sensing. It gives the basic mechanism which is involved in remote sensing. This website also gives a brief description of
various formats of images.
Architecture Overview:
A CNN consists of a series of processing layers as shown in Fig. Each layer is a family of convolution filters that detect image
features. Near the end of the series, the CNN combines the detector outputs in fully connected “dense” layers, finally
producing a set of predicted probabilities, one for each class. The network itself learns which features to detect, and how to
detect them, as it trains.
Figure. The architecture of Satellite Image classification.
IV. Data Analysis
The train_wkt.csv file comprises three columns. The first one consist of the Image Id which is equivalent to the name of the
Image file. Image Id for all the individual images of the training dataset is unique so while training the model it doesn't get
conflict. The second column name is Class Type, which consists of the type of object which is present in the class whether the

defined body is a water body, building or cropland, etc. The Class Type is denoted by the number between 1 to 10 which are
predefined (ex: 1Water, 2-Buildings). We can add more objects if we want. The last column comprises the labeled area, which
is multipolygon geometry represented in WKT format. The polygons can be more than one depending upon the objects
available in the satellite images. This file consists of labeled data for 25 satellite images respectively.
Fig - CSV file of labeled data
V. Experimental Result
In this part, we will look at a Keras-based implementation of a deep U-Net for satellite image segmentation. The dataset is
made up of 8-and commercial-grade satellite imagery from the Space Net dataset. These files contain data on five distinct
types of objects: Buildings, struct, trucks, roads, cars, plants, crops, Tracks, Slow H2O, and Fast H2O.

As shown in the above figure it gives us various types of graphical representation which describe the result from the object
image.

Visualizing results on random validation results
Accuracy of this project:
We obtained accuracy between 90 to 95 Percentage.

Incorporated Libraries And Packages
A. Pandas is a Python library used for working with data sets. It has functions for analyzing, cleaning, exploring, and
manipulating data. Pandas allow us to analyze big data and make conclusions based on statistical theories. Pandas can clean
messy data sets, and make them readable and relevant.
B. Matplotlib is a low-level graph plotting library in python that serves as a visualization utility. Matplotlib is open source and
we can use it freely.
C. Pillow, the Python Imaging Library adds image processing capabilities to your Python interpreter. This library provides
extensive file format support, an efficient internal representation, and fairly powerful image processing capabilities.
D. EfficientNet PyTorch is a PyTorch re-implementation of EfficientNet.It is consistent with the original TensorFlow
implementation, such that it is easy to load weights from a TensorFlow checkpoint. At the same time, we aim to make our
PyTorch implementation as simple, flexible, and extensible as possible.
VI. Conclusion
The machine learning-based feature extraction and object detection using high-resolution satellite images is being designed
for RRSC-C, ISRO, and Nagpur. The system consists of a classifier that helps in extracting the information contained in
different bands of the satellite sensor. This information is extracted in terms of digital numbers which are then converted to a
category. This system is implemented to detect objects like landmass, waterbody, and forests. The data is in the form of an
image that is captured by a satellite. The image captured by satellite is converted into tiff format while processing is done.
There will be different cameras on the satellite which will be used for image capturing. The captured image will be processed
in different bands needed for identification purposes. This image is fed to the system which contains an NVIDIA GPU
processor installed in the server room. NVIDIA in the system shows the image predicted by the user against the image. The
neural network model is used for the segmentation to get the predicted pixels for each class. The output screen displays a
black image with white pixel-sized polygons which refers to the class in the program. Hence, the output of the program is
obtained. The currently developed system can further be trained to give more efficient results for different band images. The
second thing which can be done is to make the website in which direction the image will be imported and the rest of the
process is executed automatically. Furthermore, the data being used to classify can be made more accurate so accuracy will
increase further.
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