Classification of Multispectral Satellite Images using Clustering with SVM Classifier

SPrasad, S. V.; Savitri, T. Satya; Krishna, Iyyanki V. Murali

doi:10.5120/4399-6107

Cited by 20 publications

(8 citation statements)

References 14 publications

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“…Support Vector Machine (SVM) [6], [7], [8], [9] is a supervised learning model with associated learning algorithms that analyse data and recognize patterns, used for classification and regression analysis [10] of multispectral [11] satellite images.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Support Vector Machine and various methods of Multi-Spectral satellite image classification

Patki¹,

Kelkar²

2013

2013 International Conference on Advances in Technology and Engineering (ICATE)

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Use of satellite images is one of the prominent methods for information about land coverage. Multi-Spectral satellite image is an appropriate source for providing this information. Classification of these Multi-Spectral images is an effective way to recover the information. This can be achieved based on the kinds of pattern models used, the types of information used, the manner in which they are applied to the image and the manner in which they partition the image into classes. Here, along with Support Vector Machine (SVM) algorithm, various other classification techniques are discussed and compared based on several parameters.

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Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Support Vector Machine and various methods of Multi-Spectral satellite image classification

Patki¹,

Kelkar²

2013

2013 International Conference on Advances in Technology and Engineering (ICATE)

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“…With the development of machine learning, the support vector machine (SVM) algorithm has been widely used in image classification of multispectral remote sensing data 7 – 16 These studies show that the SVM algorithm is suitable for a variety of data, and its classification results are more accurate than other spectral analysis algorithms. For the study of lithological classification of multispectral remote sensing data, Le et al 17 .…”

Section: Introductionmentioning

confidence: 99%

Lithological classification by PCA-QPSO-LSSVM method with thermal infrared hyper-spectral data

Fang

Xiao

Yan

et al. 2022

J. Appl. Rem. Sens.

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.To improve the accuracy and operation efficiency of lithological classification using a thermal infrared airborne hyper-spectral imager (TASI) data, an innovative combinatorial algorithm (PCA-QPSO-LSSVM) based on principal component analysis (PCA), quantum-behaved particle swarm optimization (QPSO), and least-squares support vector machines (LSSVM) is proposed. After pre-processing, the emissivity data was extracted from TASI data, and 27 types of lithological units were selected in comparison with the measured spectrum and Johns Hopkins University spectrum library. The PCA was used for statistical analysis, and the appropriate n principal components were selected instead of the original 32 bands of TASI emissivity data. Based on the LSSVM classification algorithm, the improved QPSO algorithm was used to optimize the regularization parameter γ and the kernel function σ2 in the classification process. Finally, test samples selected from TASI data were classified by PCA-QPSO-LSSVM. The results show that, compared with the traditional LSSVM algorithm, PCA-QPOS-LSSVM has a higher recognition accuracy and greater operation efficiency. The field verification of the study area was carried out in LiuYuan town, GanSu province, China, and the accuracy of the field verification was 74.36% for the classification result, which is more consistent with the actual lithological distribution compared with the LSSVM algorithm. However, there are some flaws in this paper, such as the low classification accuracy in Moyite and plauenite. In general, The PCA-QPSO-LSSVM algorithm is a practical algorithm for lithological classification of TASI data.

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“…The motivation is to develop an unmanned system that can autonomously classify the categories of the scene overflown by the UAV system and can geo-reference itself with respect to the ground-truth imagery of remote-sensing database. Most of the previous research on scene classification have analyzed the multispectal imagery from high-altitude satellites 11,12 for land-usage monitoring or the forward-looking infra-red imagery from military aircrafts for target tracking. 13 Our research in this paper analyzes electro-optical camera images from low-altitude UAVs for the categorization of scenes into 'man-made areas' versus 'natural vegetation' areas.…”

Section: Introductionmentioning

confidence: 99%

Scene Classification and Landmark Spotting in Multilayer Image Mosaic for UAVs in GPS-denied Situations

Park

Kaabouch

Fevig

et al. 2012

Infotech@Aerospace 2012

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In GPS-denied UAV navigation, it is important for the UAV system to autonomously adapt itself to the changing environment by analyzing sensor signals on the fly. The autonomous navigation system should analyze the instantaneous profile of the sensor data as well as manage the longer-term temporal evolution patterns of the environment. This requires the UAV system achieve the situational awareness at sensor data level and at contextual knowledge level in order to assist the autonomous decision making in the GPSdenied situation. The proposed system fulfills the requirements as follows. Firstly, the system builds the 'image hierarchy' incrementally on the fly by generating a multilayer image mosaic composed of 'short-duration mosaic' and 'long-duration mosaic' simultaneously. The 'long-duration mosaic' covers the overall area of the scene swept through by the UAV, while the 'short-duration mosaic' maintains and updates more detailed local area of the scene. Secondly, the system analyzes the individual image frame in terms of Hough line profiles, and classifies the scene into the 'man-made zone' versus the 'natural fields' categories. Each category is divided into two sub-categories. The Hough line profiles of the image frames show distinctive characteristics among the four sub-categories. The Hough line profile distribution is modeled by Gaussian mixtures, and the Bayesian classifier recognizes the scene categories of new image frames. Thirdly, the proposed system can select a specific landmark on demand such as a building or a crossroad from the input image and spot the corresponding landmark from the stored reference database such as satellite imagery. The proposed system achieves correct spotting of man-made objects between the UAV-acquired input image and the reference imagery database from satellites. The proposed system was evaluated with extensive field test data. The performances of the hierarchical multilevel mosaicing, the scene classification, and the landmark spotting show promising results.

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Classification of Multispectral Satellite Images using Clustering with SVM Classifier

Cited by 20 publications

References 14 publications

Support Vector Machine and various methods of Multi-Spectral satellite image classification

Support Vector Machine and various methods of Multi-Spectral satellite image classification

Lithological classification by PCA-QPSO-LSSVM method with thermal infrared hyper-spectral data

Scene Classification and Landmark Spotting in Multilayer Image Mosaic for UAVs in GPS-denied Situations

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