Improved Classification of VHR Images of Urban Areas Using Directional Morphological Profiles

Bellens, Rik; Gautama, Sidharta; Martinez-Fonte, L.; Philips, Wilfried; Chan, Jonathan Cheung-Wai; Canters, Frank

doi:10.1109/tgrs.2008.2000628

Cited by 113 publications

(41 citation statements)

References 18 publications

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“…Morphological features are based on some basic operators, such as opening and closing, used to remove small bright (opening) or dark (closing) details while leaving the overall features relatively undisturbed. The morphological reconstruction is another commonly used technique that has been proved to have better shape preservation than classical morphological operators [5], [13]. Morphological features have been successfully applied to urban VHR images [5], [11].…”

Section: Morphological Texture Extractionmentioning

confidence: 99%

“…The morphological reconstruction is another commonly used technique that has been proved to have better shape preservation than classical morphological operators [5], [13]. Morphological features have been successfully applied to urban VHR images [5], [11]. In particular, some operations, such as gradients, top hats, and opening/closing by reconstruction, have been proved to be efficient for texture extraction and segmentation of natural landscapes [12].…”

Section: Morphological Texture Extractionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of Morphological Texture Features for Mangrove Forest Mapping and Species Discrimination Using Multispectral IKONOS Imagery

Huang

Zhang

Wang

2009

IEEE Geosci. Remote Sensing Lett.

109

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Abstract-This letter aims to exploit morphological textures in discriminating three mangrove species and surrounding environment with multispectral IKONOS imagery in a study area on the Caribbean coast of Panama. Morphological texture features are utilized to distinguish red (Rhizophora mangle), white (Laguncularia racemosa), and black (Avicennia germinans) mangroves and rainforest regions. Meanwhile, two fusion methods are presented, i.e., vector stacking and support vector machine (SVM) output fusion, for integrating the hybrid spectral-textural features. For comparison purposes, the object-based analysis and the gray-level co-occurrence matrix (GLCM) textures are adopted. Results revealed that the morphological feature opening by reconstruction (OBR) followed by closing by reconstruction (CBR) and its dual operator CBR followed by OBR gave very promising accuracies for both mangrove discrimination (89.1% and 91.1%, respectively) and forest mapping (91.4% and 93.7%, respectively), compared with the object-based analysis (80.5% for mangrove discrimination and 82.9% for forest mapping) and the GLCM method (81.9% and 87.2%, respectively). With respect to the spectral-textural information fusion algorithms, experiments showed that the SVM output fusion could obtain an additional 2.0% accuracy improvement than the vector-stacking approach.

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Section: Morphological Texture Extractionmentioning

confidence: 99%

Section: Morphological Texture Extractionmentioning

confidence: 99%

Evaluation of Morphological Texture Features for Mangrove Forest Mapping and Species Discrimination Using Multispectral IKONOS Imagery

Huang

Zhang

Wang

2009

IEEE Geosci. Remote Sensing Lett.

109

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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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“…Some of the most popular approaches are the object-based and contextual-based methods which can achieve precise object recognition (Bellens et al, 2008;Rizvi and Mohan, 2011;Tilton et al, 2012). Nevertheless, the HSR scenes often contain diverse land-cover objects, such as road, lawn, and building.…”

Section: Introductionmentioning

confidence: 99%

Scene Classfication Based on the Semantic-Feature Fusion Fully Sparse Topic Model for High Spatial Resolution Remote Sensing Imagery

Zhu

Zhong

Zhang

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Topic modeling has been an increasingly mature method to bridge the semantic gap between the low-level features and high-level semantic information. However, with more and more high spatial resolution (HSR) images to deal with, conventional probabilistic topic model (PTM) usually presents the images with a dense semantic representation. This consumes more time and requires more storage space. In addition, due to the complex spectral and spatial information, a combination of multiple complementary features is proved to be an effective strategy to improve the performance for HSR image scene classification. But it should be noticed that how the distinct features are fused to fully describe the challenging HSR images, which is a critical factor for scene classification. In this paper, a semantic-feature fusion fully sparse topic model (SFF-FSTM) is proposed for HSR imagery scene classification. In SFF-FSTM, three heterogeneous features-the mean and standard deviation based spectral feature, wavelet based texture feature, and dense scale-invariant feature transform (SIFT) based structural feature are effectively fused at the latent semantic level. The combination of multiple semantic-feature fusion strategy and sparse based FSTM is able to provide adequate feature representations, and can achieve comparable performance with limited training samples. Experimental results on the UC Merced dataset and Google dataset of SIRI-WHU demonstrate that the proposed method can improve the performance of scene classification compared with other scene classification methods for HSR imagery.

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Improved Classification of VHR Images of Urban Areas Using Directional Morphological Profiles

Cited by 113 publications

References 18 publications

Evaluation of Morphological Texture Features for Mangrove Forest Mapping and Species Discrimination Using Multispectral IKONOS Imagery

Evaluation of Morphological Texture Features for Mangrove Forest Mapping and Species Discrimination Using Multispectral IKONOS Imagery

Sparse Coding-Based Method Comparison For Land-Use Classification

Scene Classfication Based on the Semantic-Feature Fusion Fully Sparse Topic Model for High Spatial Resolution Remote Sensing Imagery

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