Object-Based Image Analysis of High-Resolution Satellite Images Using Modified Cloud Basis Function Neural Network and Probabilistic Relaxation Labeling Process

Rizvi, Imdad; Mohan, B. Krishna

doi:10.1109/tgrs.2011.2171695

Cited by 58 publications

(26 citation statements)

References 15 publications

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“…Object-based supervised image classification method has been widely used for classification of land use/cover units from high resolution images since the final result is similar to the human visual interpretation of digital images (Rizvi and Mohan, 2011).…”

Section: Object-based Supervised Image Classificationmentioning

confidence: 99%

Modelling land use/cover change in Lake Mogan and surroundings using CA-Markov Chain Analysis

Durmusoglu¹,

Tanriover²

2017

JEB

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Aim : Methodology :Results : Interpretation :Lake Mogan, having high ecological and cultural significance, has been under intense pressure of urbanization and industrialization due to its location on the periphery of the capital Ankara. In this study, we analyzed data from satellite remote sensing, Geographic Information System and Cellular Automata Markov Chain modelling to predict land use/cover changes in Lake Mogan and surrounding areas.Three images recorded in 1975 and 1999 (air photos) and 2009 (Quickbird image) were used to detect the land use/cover changes over the time. An object-based supervised classification approach to simulate changes in the study area for the future was performed. Land use/cover changes for the year 2029 was modelled using Cellular Automata Markov Chain Analysis with the support of suitability analysis.The results indicated that most of the agricultural areas will be converted into settlements over the next 40 years. Modelling results for 2029 also showed that land degradation activities are likely to continue in the future.According to model on land use changes in Lake Mogan and surrounding areas, there is an immediate need to take long-term measures for sustainable land development based on the regional scales, preferably on the basin level.

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Section: Object-based Supervised Image Classificationmentioning

confidence: 99%

Modelling land use/cover change in Lake Mogan and surroundings using CA-Markov Chain Analysis

Durmusoglu¹,

Tanriover²

2017

JEB

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“…They used a kernel called -cloud base‖ function for classification. In [6] rule-based classification of very high resolution images, using cartographic data, was used. In the segmentation step, the seeds were picked in the center of cartographic objects; then, using some contextual rules, they classified the image.…”

Section: Introductionmentioning

confidence: 99%

A Survey of Satellite High Resolution Image Classification

Bharti¹,

Kurmi²

2017

IJCA

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The aim of this research is to present a detailed step-by-step method for classification of very high resolution urban satellite images (VHRSI) into specific classes such as road, building, vegetation, etc., using fuzzy logic. In this study, object-based image analysis is used for image classification. The main problems in high resolution image classification are the uncertainties in the position of object borders in satellite images and also multiplex resemblance of the segments to different classes. In order to solve this problem, fuzzy logic is used for image classification, since it provides the possibility of image analysis using multiple parameters without requiring inclusion of certain thresholds in the class assignment process Image classification is the most important process in pattern reorganization. Image classification is one of the most complex areas in image processing. It is more complex and difficult to classify if it contain blurry and noisy content. There are several methods to classify images and they provide good classification result but they fail to provide satisfactory classification result when the image contains blurry and noisy content. The two main methods for image classification are supervised and unsupervised classification. In general, this is a final stage of pattern matching. The classification process described the percentage of accuracy in pattern recognition. Feature extraction is another vital stage in pattern matching. These extracted feature are used for classification of the image database, that is pattern matching.

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“…A lot of previous research used analytical approach, which focused on pixel-or object based classification. It extracted spectral, texture, and geometrical attributes [8][9][10][11][12]. Nevertheless, the attribute is only used in a certain environment so it just produce less data representation.…”

Section: Introductionmentioning

confidence: 99%

Sparse Coding-Based Method Comparison For Land-Use Classification

Arsa

Jati

Hilman

2017

Jurnal Ilmu Komputer dan Informasi

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Land-use classification utilize high-resolution remote sensing image. The image is utilized for improving the classification problem. Nonetheless, in other side, the problem becomes more challenging cause the image is too complex. We have to represent the image appropriately. On of the common method to deal with it is Bag of Visual Word (BOVW). The method needs a coding process to get the final data interpretation. There are many methods to do coding such as Hard Quantization Coding (HQ), Sparse Coding (SC), and Locality-constrained Linear Coding (LCC). However, that coding methods use a different assumption. Therefore, we have to compare the result of each coding method. The coding method affects classification accuracy. The best coding method will produce the better classification result. Dataset UC Merced consisted 21 classes is used in this research. The experiment result shows that LCC got better performance / accuracy than SC and HQ. LCC method got 86.48 % accuracy. Furthermore, LCC also got the best performance on various number of training data for each class. Keywords: Land-use classification, high-resolution remote sensing image, Bag of Visual Word (BOVW), Sparse Coding (SC), Hard Quantization Coding (HQ) AbstrakKlasifikasi penggunaan lahan memanfaatkan gambar penginderaan jauh beresolusi tinggi. Citra digunakan untuk memperbaiki masalah klasifikasi. Meski begitu, di sisi lain, masalahnya menjadi lebih menantang karena gambarnya terlalu rumit. Kita harus mewakili gambar dengan tepat. Pada metode yang umum untuk mengatasinya adalah Bag of Visual Word (BOVW). Metode ini membutuhkan proses pengkodean untuk mendapatkan interpretasi data akhir. Ada banyak metode untuk melakukan pengkodean seperti Hard Quantization Coding (HQ), Sparse Coding (SC), dan Locality-constrained Linear Coding (LCC). Namun, metode pengkodean itu menggunakan asumsi yang berbeda. Oleh karena itu, kita harus membandingkan hasil setiap metode pengkodean. Metode pengkodean mempengaruhi akurasi klasifikasi. Metode pengkodean terbaik akan menghasilkan hasil klasifikasi yang lebih baik. Dataset UC Merced terdiri dari 21 kelas yang digunakan dalam penelitian ini. Hasil percobaan menunjukkan bahwa LCC memiliki kinerja / akurasi yang lebih baik daripada SC dan HQ. Metode LCC mendapat akurasi 86,48%. Selanjutnya, LCC juga mendapat performa terbaik pada berbagai jumlah data pelatihan untuk masing-masing kelas. Kata Kunci: Klasifikasi penggunaan lahan, citra penginderaan jauh beresolusi tinggi, Bag of Visual Word (BOVW), Sparse Coding (SC), Hard Quantization Coding (HQ)

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Object-Based Image Analysis of High-Resolution Satellite Images Using Modified Cloud Basis Function Neural Network and Probabilistic Relaxation Labeling Process

Cited by 58 publications

References 15 publications

Modelling land use/cover change in Lake Mogan and surroundings using CA-Markov Chain Analysis

Modelling land use/cover change in Lake Mogan and surroundings using CA-Markov Chain Analysis

A Survey of Satellite High Resolution Image Classification

Sparse Coding-Based Method Comparison For Land-Use Classification

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