Scene Classification via a Gradient Boosting Random Convolutional Network Framework

Zhang, Fan; Du, Bo; Zhang, Liangpei

doi:10.1109/tgrs.2015.2488681

Cited by 385 publications

(171 citation statements)

References 24 publications

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“…LLC also shows its ability better than SC using little data. However when compared to [16], the accuracy of LLC is lower. But high accuracy is followed by high complexity in model development.…”

Section: Discussionmentioning

confidence: 94%

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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Section: Discussionmentioning

confidence: 94%

Sparse Coding-Based Method Comparison For Land-Use Classification

Arsa

Jati

Hilman

2017

Jurnal Ilmu Komputer dan Informasi

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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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“…Based on SAE, SCAE changed all the input, output and the hidden layers with a one-dimension structure and used the convolution network for improved conservation of the spatial features [12]. Similar to the traditional CNN network, SCAE is the stacking of several building blocks [13], with each block containing a convolutional layer, pooling layer and a nonlinearity layer. The structure of the SCAE network is shown in Figure 4.…”

Section: Feature Extractionmentioning

confidence: 99%

GAN-Assisted Two-Stream Neural Network for High-Resolution Remote Sensing Image Classification

Tao

Zhong

et al. 2017

Remote Sensing

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Abstract:Using deep learning to improve the capabilities of high-resolution satellite images has emerged recently as an important topic in automatic classification. Deep networks track hierarchical high-level features to identify objects; however, enhancing the classification accuracy from low-level features is often disregarded. We therefore proposed a two-stream deep-learning neural network strategy, with a main stream utilizing fine spatial-resolution panchromatic images to retain low-level information under a supervised residual network structure. An auxiliary line employed an unsupervised net to extract high-level abstract and discriminative features from multispectral images to supplement the spectral information in the main stream. Various feature extraction types from the neural network were selected and jointed in the novel net, as the combined high-and low-level features could provide a superior solution to image classification. In traditional convolutional neural networks, increased network depth might not influence the network performance perceptibly; however, we introduced a residual neural network to develop the expressive ability of the deeper net, increasing the role of net depth in feature extraction. To enhance feature robustness, we proposed a novel consolidation part in feature extraction. An adversarial net improved the feature extraction capabilities and aided digging the inherent and discriminative features from data, with increased extraction efficacy. Tests on satellite images indicated the high overall accuracy of our novel net, verifying that net depth or number of convolution kernels affected the classification capability. Various comparative tests proved the structural rationality for our two-stream structure.

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Scene Classification via a Gradient Boosting Random Convolutional Network Framework

Cited by 385 publications

References 24 publications

Sparse Coding-Based Method Comparison For Land-Use Classification

Sparse Coding-Based Method Comparison For Land-Use Classification

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

GAN-Assisted Two-Stream Neural Network for High-Resolution Remote Sensing Image Classification

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