Unsupervised Feature Learning for Aerial Scene Classification

Cheriyadat, Anil M.

doi:10.1109/tgrs.2013.2241444

Cited by 407 publications

(277 citation statements)

References 25 publications

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“…BOVW [28] 76.8 SPM [28] 75.3 BOVW + Spatial Co-occurrence Kernel [28] 77.7 Color Gabor [28] 80.5 Color histogram (HLS) [28] 81.2 Structural texture similarity [7] 86.0 Unsupervised feature learning [33] 81.7˘1.2 Saliency-Guided unsupervised feature learning [34] 82.7˘1.2 Concentric circle-structured multiscale BOVW [5] 86.6˘0.8 Multifeature concatenation [35] 89.5˘0.8 Pyramid-of-Spatial-Relatons (PSR) [36] 89.1 MCBGP + E-ELM [37] 86.52˘1.3 ConvNet with specific spatial features [38] 89.39˘1.10 gradient boosting randomconvolutional network [39] 94.53 GoogLeNet [40] 92.80˘0.61 OverFeatConvNets [40] 90.91˘1.19 MS-CLBP [17] 90 Figure 14 shows the confusion matrix of the proposed method for the 21-class land-use dataset. The diagonal elements of the matrix denote the mean class-specific classification accuracy (%).…”

Section: Methodsmentioning

confidence: 99%

Remote Sensing Image Scene Classification Using Multi-Scale Completed Local Binary Patterns and Fisher Vectors

et al. 2016

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An effective remote sensing image scene classification approach using patch-based multi-scale completed local binary pattern (MS-CLBP) features and a Fisher vector (FV) is proposed. The approach extracts a set of local patch descriptors by partitioning an image and its multi-scale versions into dense patches and using the CLBP descriptor to characterize local rotation invariant texture information. Then, Fisher vector encoding is used to encode the local patch descriptors (i.e., patch-based CLBP features) into a discriminative representation. To improve the discriminative power of feature representation, multiple sets of parameters are used for CLBP to generate multiple FVs that are concatenated as the final representation for an image. A kernel-based extreme learning machine (KELM) is then employed for classification. The proposed method is extensively evaluated on two public benchmark remote sensing image datasets (i.e., the 21-class land-use dataset and the 19-class satellite scene dataset) and leads to superior classification performance (93.00% for the 21-class dataset with an improvement of approximately 3% when compared with the state-of-the-art MS-CLBP and 94.32% for the 19-class dataset with an improvement of approximately 1%).

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Section: Methodsmentioning

confidence: 99%

Remote Sensing Image Scene Classification Using Multi-Scale Completed Local Binary Patterns and Fisher Vectors

et al. 2016

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“…Research [13][14][15] also uses this method as a classification method. In addition, this method is chosen because it is able to maximize margin in the formation of decision boundary.…”

Section: Classifier Trainingmentioning

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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“…Several researchers have applied the UFL methods to the land use scene classification. In [6], a UFL method in which the sparse coding is used for learning sparse features is successfully applied to aerial scene classification. Zhang et al [39] presents a saliency-guided UFL framework for scene classification.…”

Section: Related Workmentioning

confidence: 99%

Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery

Xia

et al. 2016

Remote Sensing

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Scene classification of high-resolution remote sensing (HRRS) imagery is an important task in the intelligent processing of remote sensing images and has attracted much attention in recent years. Although the existing scene classification methods, e.g., the bag-of-words (BOW) model and its variants, can achieve acceptable performance, these approaches strongly rely on the extraction of local features and the complicated coding strategy, which are usually time consuming and demand much expert effort. In this paper, we propose a fast binary coding (FBC) method, to effectively generate efficient discriminative scene representations of HRRS images. The main idea is inspired by the unsupervised feature learning technique and the binary feature descriptions. More precisely, equipped with the unsupervised feature learning technique, we first learn a set of optimal "filters" from large quantities of randomly-sampled image patches and then obtain feature maps by convolving the image scene with the learned filters. After binarizing the feature maps, we perform a simple hashing step to convert the binary-valued feature map to the integer-valued feature map. Finally, statistical histograms computed on the integer-valued feature map are used as global feature representations of the scenes of HRRS images, similar to the conventional BOW model. The analysis of the algorithm complexity and experiments on HRRS image datasets demonstrate that, in contrast with existing scene classification approaches, the proposed FBC has much faster computational speed and achieves comparable classification performance. In addition, we also propose two extensions to FBC, i.e., the spatial co-occurrence matrix and different visual saliency maps, for further improving its final classification accuracy.

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Unsupervised Feature Learning for Aerial Scene Classification

Cited by 407 publications

References 25 publications

Remote Sensing Image Scene Classification Using Multi-Scale Completed Local Binary Patterns and Fisher Vectors

Remote Sensing Image Scene Classification Using Multi-Scale Completed Local Binary Patterns and Fisher Vectors

Sparse Coding-Based Method Comparison For Land-Use Classification

Fast Binary Coding for the Scene Classification of High-Resolution Remote Sensing Imagery

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