Unsupervised Feature Learning for Land-Use Scene Recognition

Fan, Jianchao; Chen, Tao; Lu, Shijian

doi:10.1109/tgrs.2016.2640186

Cited by 49 publications

(31 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To guarantee comparability between the accuracy of the proposed method and those reported in works presented in [2,3,5,[7][8][9][10][11][12][13], the labeled dataset is divided into training and testing sets using a training-testing ratio of 80-20%, and five-fold cross validation is conducted. That is, the labeled image patches are almost equally divided into five non-overlapping groups randomly, with one group used as the testing set and the remaining four groups used as the training set in each fold.…”

Section: Methodsmentioning

confidence: 99%

“…Specifically, edge-like and corner-like bases that resemble the neuron responses of the primary visual cortex (V1) and visual extrastriate cortical area two (V2), respectively, were learnt by K-means clustering. Fan et al [11] utilized a multipath sparse coding architecture to extract dense low-level features from the raw data. The sparse features extracted from different paths were then concatenated to represent the whole image.…”

Section: Related Workmentioning

confidence: 99%

“…To illustrate the performance of SSF-CNN, we compare it with ten state-of-the-art research studies presented in [2,3,5,[7][8][9][10][11][12][13] respectively. These studies are selected because all of them have been evaluated on the UCMerced dataset under the same training-testing ratio (80-20%).…”

Section: Comparison With State-of-the-art Research On the Ucmerced Damentioning

confidence: 99%

See 2 more Smart Citations

Remote Sensing Scene Classification Based on Convolutional Neural Networks Pre-Trained Using Attention-Guided Sparse Filters

Wang

Chen

et al. 2018

Remote Sensing

View full text Add to dashboard Cite

Semantic-level land-use scene classification is a challenging problem, in which deep learning methods, e.g., convolutional neural networks (CNNs), have shown remarkable capacity. However, a lack of sufficient labeled images has proved a hindrance to increasing the land-use scene classification accuracy of CNNs. Aiming at this problem, this paper proposes a CNN pre-training method under the guidance of a human visual attention mechanism. Specifically, a computational visual attention model is used to automatically extract salient regions in unlabeled images. Then, sparse filters are adopted to learn features from these salient regions, with the learnt parameters used to initialize the convolutional layers of the CNN. Finally, the CNN is further fine-tuned on labeled images. Experiments are performed on the UCMerced and AID datasets, which show that when combined with a demonstrative CNN, our method can achieve 2.24% higher accuracy than a plain CNN and can obtain an overall accuracy of 92.43% when combined with AlexNet. The results indicate that the proposed method can effectively improve CNN performance using easy-to-access unlabeled images and thus will enhance the performance of land-use scene classification especially when a large-scale labeled dataset is unavailable.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Comparison With State-of-the-art Research On the Ucmerced Damentioning

confidence: 99%

See 1 more Smart Citation

Remote Sensing Scene Classification Based on Convolutional Neural Networks Pre-Trained Using Attention-Guided Sparse Filters

Wang

Chen

et al. 2018

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…BoVW [15] 71.86 SPM [19] 74.0 SPCK++ [4] 77.38 MS-based Correlaton [20] 81.32 ± 0.92 UFL [7] 81.67 ± 1.23 SG + UFL [8] 82.72 ± 1.18 UFL-SC [10] 90.26 ± 1.51 UFC + MSC [11] 91.95 ± 0.72 CCM-BoVW [21] 86.64 ± 0.81 PSR [22] 89.1 MSIFT [6] 90.97 ± 1.81 MS-CLBP + FV [56] 93.0 ± 1.2 MTJSLRC [55] 91.07 ± 0.67 VLAT [57] 94.3 MBVW [25] 96.14 OverFeat [31] 90.91 ± 1.19 CaffeNet [31] 93.42 ± 1.0 GoogLeNet + Fine-tune [53] 97.1…”

Section: Methodsmentioning

confidence: 99%

“…HRS images provide more of the appearance and spatial arrangement information needed in land-use scene category recognition [2]. It is usually difficult, however, to recognize land-use scene categories because they often comprise of multiple land covers or ground objects [3][4][5][6][7][8][9][10][11], such as airports with airplanes, runways and grass. Land-use scene categories are largely affected and determined by human social activities.…”

Section: Introductionmentioning

confidence: 99%

A Multiscale Deeply Described Correlatons-Based Model for Land-Use Scene Classification

Yang

Guan

et al. 2017

Remote Sensing

View full text Add to dashboard Cite

Abstract:Research efforts in land-use scene classification is growing alongside the popular use of High-Resolution Satellite (HRS) images. The complex background and multiple land-cover classes or objects, however, make the classification tasks difficult and challenging. This article presents a Multiscale Deeply Described Correlatons (MDDC)-based algorithm which incorporates appearance and spatial information jointly at multiple scales for land-use scene classification to tackle these problems. Specifically, we introduce a convolutional neural network to learn and characterize the dense convolutional descriptors at different scales. The resulting multiscale descriptors are used to generate visual words by a general mapping strategy and produce multiscale correlograms of visual words. Then, an adaptive vector quantization of multiscale correlograms, termed multiscale correlatons, are applied to encode the spatial arrangement of visual words at different scales. Experiments with two publicly available land-use scene datasets demonstrate that our MDDC model is discriminative for efficient representation of land-use scene images, and achieves competitive classification results with state-of-the-art methods.

show abstract

Multilabel classification of remote sensed satellite imagery

Kumar

Abhishek

Singh

et al. 2020

Trans Emerging Tel Tech

View full text Add to dashboard Cite

Multilabel scene classification has emerged as a critical research area in the domain of remote sensing. Contemporary classification models primarily emphasis on a single object or multiobject scene classification of satellite remote sensed images. These classification models rely on feature engineering from images, deep learning, or transfer learning. Comparatively, multilabel scene classification of very high resolution (V.H.R.) images is a fairly unexplored domain of research. Models trained for single label scene classification are unsuitable for the application of recognizing multiple objects in a single remotely sensed V.H.R. satellite image. To overcome this research gap, the current inquiry proposes to fine‐tune the state of the art convolutional neural network (C.N.N.) architectures for multilabel scene classification. The proposed approach pre trains C.N.N on the ImageNet dataset and further fine‐tunes them to the task of detecting multiple objects in V.H.R. images. To understand the efficacy of this approach, the final models are applied on a V.H.R. dataset: the U.C.M.E.R.C.E.D. image dataset containing 21 different terrestrial land use categories with a submeter resolution. The performance of the final models is compared with graph convolutional network‐based model by Khan et al. From the results on performance metrics, it was observed that proposed models achieve comparable results in significantly fewer epochs.

show abstract

Unsupervised Feature Learning for Land-Use Scene Recognition

Cited by 49 publications

References 35 publications

Remote Sensing Scene Classification Based on Convolutional Neural Networks Pre-Trained Using Attention-Guided Sparse Filters

Remote Sensing Scene Classification Based on Convolutional Neural Networks Pre-Trained Using Attention-Guided Sparse Filters

A Multiscale Deeply Described Correlatons-Based Model for Land-Use Scene Classification

Multilabel classification of remote sensed satellite imagery

Contact Info

Product

Resources

About