68 Available online at www.ijiere.com
International Journal of Innovative and Emerging
Research in Engineering
e-ISSN: 2394 - 3343 p-ISSN: 2394 - 5494
A Survey on Sub Pixel Classification of High Resolution
Satellite Imagery
Amruta Talreja, Prof. Vipul Dalal
Vidyalankar Institute of Technology, Wadala, Mumbai, India
ABSTRACT:
Earth observation satellites have gained importance in many applications like Land Cover Detection, Environment Monitoring, Disaster Management, etc. The information is extracted from satellite images through different classification methods. But frequent occurrence of mixed pixels become the limitation as it causes uncertainty in the process of classification of satellite images. Thus, Soft computing techniques are becoming popular in classifying the remotely sensed data. So, Sub-pixel classification is a process designed to divide pixels into sub-pixels to represent the different class fractions within a pixel. This paper presents the advantages, issues and limitations of existing methods of Sub Pixel Classification of High Resolution Satellite Images.
Keywords: Remote Sensing, Sub Pixel Classification, Soft Computing, Sub Pixel Mapping, Support Vector Machines, Genetic Algorithms
I. INTRODUCTION
Remote Sensing generally refers to the use of satellite sensor technologies to detect and classify objects on Earth, including on the surface and in the atmosphere and oceans, based on electromagnetic radiation. Earth surface data is captured by the sensors at different wavelengths and at different resolutions like spectral, spatial, polarization and temporal to discriminate between various land covers and objects on the earth. Remote sensing data are widely used in areas like agriculture, forestry, water resources, land use, urban sprawl, geology, environment, coastal zone, etc.
Satellite images are rich and plays a vital role in providing geographical information. Satellite and remote sensing images provides quantitative and qualitative information that reduces complexity of field work and study time. Satellite image classification is a process of grouping pixels into meaningful classes. It is a multi-step workflow. Satellite image classification can also be referred as extracting information from satellite images.
Remote-sensing research focusing on image classification has long attracted the attention of the remote-sensing community because classification results are the basis for many environmental and socioeconomic applications. However, classifying remotely sensed data into a thematic map remains a challenge because many factors, such as the complexity of the landscape in a study area, selected remotely sensed data, and image processing and classification approaches, may affect the success of a classification.
Thus, the most important technique in remote sensing is classification of different land cover information. Accurate land cover information is very important in determining the various characteristics of different land forms and other issues like environment monitoring, disaster management, etc. Therefore it is important to reduce the uncertainty which occurs during the classification process due to mixed pixels. Mixed pixels are those which consists of more than one feature like a single pixel containing features of urban sprawl, vegetation, water resources, etc. This affects the accuracy of the classification process of satellite images. However, the biggest disadvantage for most satellite images is the relatively low spatial resolution which limits the accuracy of estimation of coverage and positioning of boundaries. Sub-pixel mapping technique may be a solution to relieve this limitation. Generally, to realize the classification at sub-pixel level based on the original pixel-level images, two main steps are implemented: soft classification which predicts the percentage of each class inside a pixel and the sub-pixel mapping which determines the distribution of sub pixel labels.
69 important factors influencing the selection of remotely sensed data, the design of the classification procedure, and the quality of the classification results.
This paper is a survey on Sub pixel satellite image classification methods and techniques. It describes and provide details on various sub pixel classification methods. Emphasis is given on the advantages, issues and limitations of existing methods of Sub Pixel Classification of High Resolution Satellite Images.
II. SUB PIXEL CLASSIFICATION PROCESS
A. L. Choodarathnakara [15] describes in general the process of sub pixel classification in detail.
Soft Computing is a complex process that may be affected by many factors. Remote-sensing research focusing on image classification has long attracted the attention of the remote-sensing community because classification results are the basis for many environmental and socioeconomic applications. Scientists and practitioners have made great efforts in developing advanced classification approaches and techniques for improving classification accuracy.
Sub Pixel Classification is a complex process and requires consideration of many factors. The major steps of image classification may include determination of a suitable classification system, selection of training samples, image pre-processing, and feature extraction, selection of suitable classification approaches, post-classification processing, and accuracy assessment. This section focuses on the description of the major steps that may be involved in image classification.
A. Selection of Data
There are mainly two types of remote sensed data airborne and space borne data. For Satellite Image classification it is important to understand the strengths and weakness of remotely sensed data. All the remotely sensed data vary in different resolutions like spatial, spectral, temporal and radiometric resolutions. The first and most important step in satellite image classification is selecting proper remotely sensed data. The factors required for the selection of sensor data are user’s need, characteristics of study area, availability of sensed data, cost and time factor, image resolution, etc.
The frequent cloudy conditions in the moist tropical regions are often an obstacle for capturing high-quality optical sensor data. Thus, atmospheric conditionsbecome another important factor to consider while selecting the remotely sensed data. There are many sources of sensor data readily available so the analyst have many options to choose from for specific study.
B. Selection of suitable Region of Interest(ROI)
Training samples or Region of Interest (ROI) are selected from Aerial Photographs or Satellite Images. There are different collection strategies of training samples, such as single pixel, seed, and polygon, may be used, but they would influence classification results, especially for classifications with high resolution image data .When the landscape of a study area is complex and heterogeneous, selecting sufficient training samples becomes difficult. This problem would be complicated if coarse resolution data are used for classification, because a large volume of mixed pixels may occur. Therefore, selection of training samples must consider the spatial resolution of the remote-sensing data being used, availability of ground reference data, and the complexity of landscapes in the study area.
C. Data Pre-processing
Image pre-processing may include the detection and restoration of bad lines, geometric rectification, radiometric calibration and atmospheric correction, and topographic correction. Accurate geometric rectification of remotely sensed data is a prerequisite for the classification process.
D. Selection of a Suitable Classification Method
Many factors, such as spatial resolution of the remotely sensed data, different sources of data, a classification system, and availability of classification software must be taken into account when selecting a classification method for use. Different classification methods have their own merits and demerits.
E. Post Classification Processing
Per-pixel classification may sometimes lead to salt and pepper effect making the satellite image unclear and blur. The reason behind this unclear image with salt and pepper effect is biophysical and atmospheric environment. Thus, post classification is required to remove the noises from the image. Thus, to improve the quality of image ancillary data are used based on expert rules.
70 Evaluation of classification results is an important process in the classification procedure. There are a few strategies to evaluate the performance of classification method: accuracy, reproducibility, ability to fully use the information content of the data, uniform applicability, objectiveness,classification accuracy, computational resources, stability of the algorithm, and robustness to noise
III.LITERATURE SURVEY
Some of the frequent researches on different sub pixel classification methods for satellite images are discussed in the survey.
K. C. Mertens [1] et al used Genetic Algorithm for the sub pixel mapping of remotely sensed data. Genetic Algorithm combined with the assumption of spatial dependence assign a location to every sub pixel. The algorithm was tested on synthetic and real imagery and obtained accuracy measures which were higher as compared to conventional hard classifications.
Gidudu Anthony [2] et al performed multiple classification tasks using Support Vector Machines. The approaches used were One-Against-One (1A1) and One-Against-All (1AA) techniques for classification of multiple land covers present in remotely sensed data. The authors conclude that 1AA approach to multiclass classification has exhibited higher propensity for mixed pixels than the 1A1 approach. The two approaches were compared with four different SVM classifiers like Linear, Quadratic, Polynomial and RBF using Kappa Coefficients. Thus, classification accuracy reduced for the linear and RBF classifiers and stayed the same for the polynomial and increased for the quadratic classifier. It can therefore be concluded that whereas one can be certain of high classification results with the 1A1 approach, the 1AA yields approximately as good classification accuracies. The choice therefore of which approach to adopt henceforth becomes a matter of preference.
C. Palaniswami [14] et al used Spectral Mixture Analysis (SMA) for the classification process. SMA was performed and evaluated based on Landsat-7 ETM+ (Enhanced Thematic Mapper Plus) data. The procedure used in this study was based on a linear mixture model to derive continuous fields of coconut, road, laterite outcrops, construction, areca nut and cloud. SMA was done on DN values and corresponding radiance values of the satellite imagery. The accuracy of end member fraction was estimated as the mean of the percentage absolute difference between actual and modelled estimates. The sub pixel accuracy achieved for the coconut land-cover was 87% using SMA of DN values, while it was 93% for SMA of radiance values.
W. Wei [3] et al tried to combine sub pixel analysis of remotely sensed data with decision tree classification to improve the wetland mapping in the delta area. A two-step classification technique was proposed trying to make use of a priori knowledge of wetland compositions. First, a sub pixel analysis technique was applied to remotely-derived reflectance data using in-site spectra to extract abundance images for each component material of the wetlands. A suitable set of end members were selected for the unmixing analysis. A classification scheme of wetlands was then created and used to classify the abundance images into various wetlands and non-wetlands. The approach combines quantitative analysis (sub pixel unmixing) with qualitative decision (classification), and incorporates with human knowledge during the whole process of analysis. An ASTER image of surface reflectance was acquired for the study area and used to evaluate the proposed approach. The approach was implemented in RSI ENVI/IDL environment. Twelve wetland and non-wetland covers were identified using the technique and the result was then assessed with field verification and the land use data that was acquired by other method. The overall accuracy of the wetland mapping is about 88.7% and the Kappa coefficient is 0.86.
M. Niroumand J., A. R. Safdarinezhad, M. R. Sahebi, M. Mokhtarzade [4] proposed a new method using SVM and SMACC. A brief survey is conducted on spatial optimisation based techniques of SRM. SVM and SMACC are used cooperatively to produce fractional maps as an input of SRM algorithm. The initial allocation of the sub-pixels is performed nonrandomised based on the highest amounts of attractiveness. An optimisation procedure is proposed to transfer the multiple allocated sub-pixels to the non-allocated ones. This procedure usually stops with minimal iterations and is time effective. The proposed method is tested on multispectral imagery (Landsat ETM+ and Quickbird) and has demonstrated precise results particularly in boundary pixels.
71 been compared to the training samples and was assigned to its closest class. The procedure has been implemented using MATLAB software. This procedure significantly reduces the mixed pixel problem which was suffered by most pixel based classification methods.
C.Heltin Genitha and K.Vani [6] implemented fuzzy c-means and fuzzy weighted c-means algorithms for sub pixel classification to estimate the proportion of class components within a pixel. Experiments are carried out with the QuickBird multispectral image and the classification accuracy is estimated by the confusion matrices. The performance of these sub-pixel classification algorithms are evaluated and compared. The results compared indicate that the accuracy of fuzzy weighted c-means algorithm is more when compared to fuzzy c-means algorithm.
Most traditional sub pixel mapping algorithms treat all mixed pixels as an identical type, either boundary-mixed pixel or linear sub pixel, leading to incomplete and inaccurate results. To improve the sub pixel mapping accuracy, Xiong Xu [8] et al proposed an adaptive sub pixel mapping framework based on a multiagent system for remote sensing imagery. In the proposed multiagent sub pixel mapping framework, three kinds of agents, namely, feature detection agents, sub pixel mapping agents and decision agents, are designed to solve the sub pixel mapping problem. Experiments with artificial images and synthetic remote-sensing images were performed to evaluate the performance of the proposed sub pixel mapping algorithm in comparison with the hard classification method and other sub pixel mapping algorithms like back-propagation neural network and the spatial attraction model. The experimental results using two types of images like synthetic and artificial images indicate that the proposed algorithm outperforms the other two sub pixel mapping algorithms with a higher sub pixel mapping accuracy.
Qnming Wang [12] et al proposed a new sub pixel mapping (SPM) method based on radial basis function (RBF) interpolation for land cover mapping at sub pixel level. The proposed method consists of sub pixel soft class value estimation and subsequent class allocation for each sub pixel. The sub pixel soft class values are calculated by RBF interpolation. Taking the coarse proportion images as input, an interpolation model is built for each visited coarse pixel. Results show that the proposed RBF interpolation-based SPM is more accurate. Hence, the proposed method provides an effective new option for SPM.
Suresh Merugu and Dr Kamal Jain [7] presented a new CIELAB Euclidean distance based super-resolution mapping method implemented and tested using colorimetric classical model in Matlab. The experiment have been conducted using sample imagery of QuickBird dataset. The performance of the proposed technique measured in terms of classification accuracy and CPU time for the datasets have been found satisfactory and encouraging. Though the technique produces good results, one of the limitations of the proposed technique lies in the use of a linear Euclidean distance as a measure of attractiveness.
Suresh Merugu [13] et al proposed a method to extract the information from mixed pixels and to perform sub pixel swapping with the neighbouring pixels to get the hidden information from the sub pixels. Here, sub pixel analysis on hypothetical image by using the SVM classifier is used to get the soft classified fractional abundances and sub pixel mapping is done by using Matlab. The accuracy of this new sub pixel mapping technique is influenced by different type of factors such as the accuracy of fractional images from the soft classifier, normalized factor, the number of neighbouring pixels, and the calorimetrically global color transformation algorithm.
Mohammed Arif [9] et al proposed a super resolution mapping technique which provides a super resolved land cover information using the output of soft classification process. Proposed method uses soft classification approaches for generating fractional maps which is provided as input to SRM method. Early allocation of sub pixels is achieved based on amount of attractiveness to neighborhood pixels. The study area of satellite imagery discussed in this paper is categorized into two parts viz., AVIRIS dataset and Aerial image. Thus, after performing proposed method overall accuracy for Aerial image is 94.1256% and Kappa Coefficient is 0.8974. And the overall classification accuracy for AVIRIS is 94.5128% and Kappa Coefficient is 0.9112.
72 Genetic Algorithm using both synthetic and real hyper spectral images. The experimental results demonstrate that the proposed approach outperforms traditional sub pixel mapping algorithms.
The following table gives the complete summary of the above literature survey.
IV. SUMMARY
Sr. No.
Paper Author Methodology Remarks
1. Using Genetic Algorithms in Sub Pixel Mapping
K. C. Mertens, L. P. C. Verbeke, E. I. Ducheyne and R. R. De Wulf
Genetic Algorithm tested on Synthetic and Real Imagery.
Genetic Algorithm is more efficient than existing hard classification
techniques.
2. Classification of Images Using Support Vector Machines
Gidudu Anthony, Hulley Greg, Marwala Tshilidzi
Support Vector Machine used as multiple task classifier using two approaches: One-Against-One (1A1) and One-Against-All (1AA)
1AA yields
approximately as good classification accuracies as compared to 1A1.
3. Spectral Mixture Analysis for Sub Pixel Classification of Coconut
C. Palaniswami, A. K. Upadhyay, H. P. Maheswarappa
Spectral Mixture Analysis (SMA) was performed and evaluated on Landsat-7 ETM+ (Enhanced Thematic Mapper Plus) data.
The sub pixel accuracy achieved was 87% using SMA of DN values, while it was 93% for SMA of radiance values.
4. Wetland Mapping Using Sub Pixel Analysis and Decision Tree Classification in The Yellow River Delta Area
W. Weia, X. Zhangb, X. Chenb, J. Tang, M. Jiang
Sub pixel analysis combined with Decision Tree Classification to improve the wetland mapping in the delta area. To evaluate the proposed approach an ASTER image was used in RSI ENVI/IDL environment.
The overall accuracy of the wetland mapping is about 88.7% and the Kappa coefficient is 0.86.
5. A Novel Approach to Super Resolution
Mapping of
Multispectral Imagery Based on Pixel Swapping Technique
M. Niroumand J., A. R. Safdarinezhad, M. R. Sahebi, M. Mokhtarzade
Spatial optimisation based techniques of SRM like SVM and SMACC are used cooperatively to produce fractional maps as an input of SRM algorithm. The proposed method is tested on multispectral imagery like Landsat ETM+ and Quickbird.
The results indicate that it has a promising proficiency; especially in boundary mixed pixels.
6. Fuzzy-Based Sub-Pixel Classification of Satellite Imagery
Satish Kumar, G. Saravana, R Rout, Ankur Pandit, Snehmani
A Fuzzy-based supervised classification method implemented using MATLAB software and tested on USA’s MODIS TERRA imagery.
This procedure significantly reduces the mixed pixel problem which was suffered by most pixel based classification methods.
7. Sub-pixel
Classification using FCM and FWCM Algorithms
C.Heltin Genitha, K.Vani
The sub-pixel classification algorithms such as fuzzy c-means and fuzzy weighted c-means algorithms are implemented on QuickBird multispectral image.
The results compared indicate that the accuracy of fuzzy weighted c-means algorithm is more when compared to fuzzy c-means algorithm.
8. Sub Pixel
Classification of Remote Sensing Data
using CIE
Chromaticity Values
Suresh Merugu, Kamal Jain
SVM classifier is used to get the soft classified fractional abundances and sub pixel mapping is done by using Matlab.
The accuracy is influenced by factors such as the accuracy of fractional images, normalized factor, the
73 neighbouring pixels,
and the
colorimetrically transformation algorithm.
9. Adaptive Sub pixel Mapping Based on a Multiagent System for Remote-Sensing Imagery
Xiong Xu, Yanfei Zhong, Member, IEEE, and Liangpei Zhang,
An adaptive sub pixel mapping framework based on a multiagent system for remote sensing imagery has been implemented. Agents like feature detection agents, sub pixel mapping agents and decision agents are used.
In comparison with
other soft
classification
algorithms, the proposed algorithm performs more efficiently.
10. Sub pixel mapping of remote sensing images based on radial basis function interpolation
Qunming Wang, Whenzhong Shi, Peter M. Atkinson
A new Radial Basis Function (RBF) interpolation sub pixel mapping (SPM) used for remote sensing images.
RBF interpolation gives accurate results in sub pixel mapping.
11. Sub Pixel Level Classification using Colorimetric Color Space
Suresh Merugu, Dr Kamal Jain
A new CIELAB euclidean distance based super-resolution mapping method has been presented.
The performance of
the proposed
technique measured in terms of classification accuracy and CPU time for both the datasets have been found satisfactory
12. Sub Pixel
Classification of High Resolution Satellite Imagery
Mohammed Arif, Merugu Suresh, Kamal Jain, Sowjanya Dundhigal
SVM is used for classifying dataset. The output of SVM is given as an input to SRM algorithm for sub pixel classification.
The overall accuracy by the proposed method is 94.1256%
and Kappa
Coefficient is 0.8974.
13. A Comparison of Sub Pixel Mapping Methods for Coastal Areas
Xiaohua Tong, Xiong Xu, Antonio Plaza, Huan Xie, Haiyan Pan, Wen Cao, Dong Lv
Soft Classification methods like linear spectral unmixing model, supervised fully-fuzzy classification method, the support vector machine and sub-pixel mapping methods like the sub-pixel/pixel attraction model, pixel swapping and wavelets method are compared.
For sub pixel mapping, overall accuracy is 91.79%
and Kappa
Coefficient is 0.875.
14. A New Genetic Method for Sub Pixel Mapping Using Hyperspectral Images
Qingxiang Liu, John Trinder, Ian Turner
A new genetic algorithm-based sub pixel mapping technique is presented and compared with Spatial Attraction Model and Modified Genetic Algorithm.
The proposed method outperforms the traditional sub pixel mapping algorithms.
Table 1. Summary of the Literature Review
V. CONCLUSIONS
This paper gives a review on different classification approaches and methods of sub pixel classification of satellite images. This paper also gives a summary on the work done till now with the accuracy of the proposed systems. The summary helps researchers to select appropriate sub pixel satellite image classification method or technique based on the requirements.
From the survey, we can state that Artificial Neural Networks show great promise in high dimensional data classification. The advantage is not significant when applied to low-dimensional multispectral data. The Maximum Likelihood yields good results for some classes for which the ANN shows poor result, and vice versa. Likewise, Genetic algorithm can be used in feature classification and feature selection. It is primarily used in optimization. It can handle large, complex, non-differentiable and multimodal spaces. It is good at refining irrelevant and noisy features selected for classification.
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VI.REFERENCES
[1] K. C. Mertens, L. P. C. Verbeke, E. I. Ducheyne and R. R. De WULF (2003) “Using Genetic Algorithms in Sub-Pixel Mapping“ INT. J. Remote Sensing, 10th NOVEMBER, 2003, VOL. 24, NO. 21, 4241–4247. [2] Gidudu Anthony, Hulley Greg and Marwala Tshilidzi (2005) “Classification of Images Using Support Vector Machines” Department of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, Private Bag X3, Wits, 2050, South Africa.
[3] W. Wei, , X. Zhang,, X. Chen, J. Tang, M. Jiang (2008) “Wetland Mapping Using Sub Pixel Analysis and Decision Tree Classification in the Yellow River Delta Area” Institute of Light Industry Environmental Protection, Beijing, China - hnzzwwx@163.com, Institute of Remote Sensing and GIS, Peking University, Beijing, China - xfzhang@pku.edu.cn, Commission VI, WG VII/4.
[4] M. Niroumand J. ,A. R. Safdarinezhad , M. R. Sahebi , M. Mokhtarzade (2012) “A Novel Approach to Super Resolution Mapping of Multispectral Imagery based on Pixel Swapping Technique” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume I-7, 2012 XXII ISPRS Congress, 25 August – 01 September 2012, Melbourne, Australia.
[5] Satish Kumar, G. Saravana, R Rout, Ankur Pandit, Snehmani (2012) “Fuzzy-Based Sub-Pixel Classification of Satellite Imagery” International Journal of Computer Science and Technology. [6] C. Heltin Genitha, K. Vani (2013) “Sub-pixel Classification using FCM and FWCM Algorithms” 2013th
Fifth International Conference on Advanced Computing (ICoAC).
[7] Suresh Merugu, Kamal Jain (2014) “Sub Pixel Classification of Remote Sensing Data using CIE Chromaticity Values” Journal of Innovation in Electronics and Communication.
[8] Xiong Xu, Yanfei Zhong, Member, IEEE, and Liangpei Zhang, Senior Member, IEEE (2014) “Adaptive Subpixel Mapping Based on a Multiagent System for Remote-Sensing Imagery” IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 52, NO. 2, FEBRUARY 2014.
[9] Mohammed Arif, Merugu Suresh, Kamal Jain, Sowjanya Dundhigal (2015) “Sub Pixel Classification of High Resolution Satellite Imagery” International Journal of Computer Applications (0975 – 8887) Volume 129 – No.1, November 2015.
[10] Xiaohua Tong, Xiong Xu, Antonio Plaza, Fellow, IEEE, Huan Xie, Haiyan Pan, Wen Cao, and Dong Lv (2016) “A New Genetic Method for Sub pixel Mapping Using Hyperspectral Images” IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING.
[11] Qingxiang Liu, John Trinder, Ian Turner (2016) “A COMPARISON OF SUB-PIXEL MAPPING METHODS FOR COASTAL AREAS” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume III-7, 2016 XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic.
[12] Qunming Wang, Whenzhong Shi, Peter M. Atkinson (2014) “Sub pixel mapping of remote sensing images based on radial basis function interpolation” ISPRS Journal of Photogrammetry and Remote Sensing.
[13] Suresh Merugu, Dr Kamal Jain (2014) “Sub Pixel Level Classification Using Colorimetric Color Space” 15th Esri India User Conference 2014.
[14] C. Palaniswami, A. K. Upadhyay, H. P. Maheswarappa (2006) “Spectral mixture analysis for subpixel classification of coconut” Current Science, Vol. 91, No. 12, 25 December 2006.
