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

Qi, Kunlun; Yang, Chao; Guan, Qingfeng; Wu, Huayi; Gong, Jianya

doi:10.3390/rs9090917

Cited by 27 publications

(16 citation statements)

References 48 publications

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“…Chen et al adopted a CNN classification method that incorporates pixel spectral information and spatial information and studied the importance of spatial information in classifying HRRS images (Chen et al 2016). Qi et al (2017) presented a Multiscale Deeply Described Correlation-based algorithm that jointly incorporates appearance and spatial information at multiple scales to perform land-use-type classification. Therefore, CNN has surpassed traditional pattern recognition and machine learning algorithms and has achieved superior performance and accuracy.…”

Section: Introductionmentioning

confidence: 99%

Object-Oriented Method Combined with Deep Convolutional Neural Networks for Land-Use-Type Classification of Remote Sensing Images

Jin

Zhang

et al. 2019

J Indian Soc Remote Sens

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Land-use information provides a direct representation of the effect of human activities on the environment, and an accurate and efficient land-use classification of remote sensing images is an important element of land-use and land-cover change research. To solve the problems associated with traditional land-use classification methods (e.g., rapid increase in dimensionality of data, inadequate feature extraction, and low running efficiency), a method that combines object-oriented approach with deep convolutional neural network (COCNN) is presented. First, a multi-scale segmentation algorithm is used to segment images to generate image segmentation regions with high homogeneity. Second, a typical rule set of feature objects is constructed on the basis of the object-oriented segmentation results, and the segmentation objects are classified and extracted to form a training sample set. Third, a convolutional neural network (CNN) model structure is modified to improve classification performance, and the training algorithm is optimized to avoid the overfitting phenomenon that occurs during training using small datasets. Ten land-use types are classified by using the remote sensing images covering the area around Fuxian Lake as an example. By comparing the COCNN method with the method based solely on CNN, precision and kappa index were selected to evaluate the classification accuracy of the two methods. For the COCNN method, on the basis of the classification statistics, precision and kappa index coefficients are 96.2% and 0.96, respectively, which are 8.98% and 0.1 higher than those of the method based solely on CNN. Experimental results show that the COCNN method reasonably and efficiently combines object-oriented and deep learning approaches, thereby effectively solving the problem of the inaccurate classification of typical features with better classification accuracy than the simple use of CNN.

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Section: Introductionmentioning

confidence: 99%

Object-Oriented Method Combined with Deep Convolutional Neural Networks for Land-Use-Type Classification of Remote Sensing Images

Jin

Zhang

et al. 2019

J Indian Soc Remote Sens

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“…On the WHU-RS dataset, our method achieved considerably better performance (98.23 ± 0.40%) than the MS-CLBP + FV method (94.32 ± 1.2%) [61] and the SIFT + LTP-HF + Color Histogram (93.6%) [55]. The classification accuracies of our CCP-net are slightly inferior to MDDC (98.27 ± 0.53) [39] and the method (98.64%) presented in Reference [36]. These two methods extract features from the convolutional layers of a pre-trained CNN and use a simple linear classifier to train and test.…”

Section: Comparision With State-of-the-art Methodsmentioning

confidence: 86%

Concentric Circle Pooling in Deep Convolutional Networks for Remote Sensing Scene Classification

Guan

Yang

et al. 2018

Remote Sensing

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Convolutional neural networks (CNNs) have been increasingly used in remote sensing scene classification/recognition. The conventional CNNs are sensitive to the rotation of the image scene, which will inevitably result in the misclassification of remote sensing scene images that belong to the same category. In this work, we equip the networks with a new pooling strategy, "concentric circle pooling", to alleviate the above problem. The new network structure, called CCP-net can generate a concentric circle-based spatial-rotation-invariant representation of an image, hence improving the classification accuracy. The square kernel is adopted to approximate the circle kernels in concentric circle pooling, which is much more efficient and suitable for CNNs to propagate gradients. We implement the training of the proposed network structure with standard back-propagation, thus CCP-net is an end-to-end trainable CNNs. With these advantages, CCP-net should in general improve CNN-based remote sensing scene classification methods. Experiments using two publicly available remote sensing scene datasets demonstrate that using CCP-net can achieve competitive classification results compared with the state-of-art methods.

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“…The multi-scale and dynamic-state characteristics are utilized in change detection in [8]. The nonlinear characteristic was reflected in the multi-scale correlatons [3] and land use sequential pattern [9]. Thanks to the high quality of the eight selected papers, we believe that the special issue provides the reader with a clear perspective of the current state of remote sensing big data, which is now fully entering the Big Data era.…”

Section: Discussionmentioning

confidence: 99%

“…By considering spectral similarities, it avoided insufficient correlations with intrinsic spectral variation of a material by Euclidean distance and formed a appropriate graph representation. To tackle the difficulties of scene classification for High-Resolution Satellite (HRS) images, in [3], the authors use a convolutional neural network to learn and characterize the dense convolutional descriptors at different scales. Then, an adaptive vector quantization termed multiscale correlatons is applied to encode the spatial arrangement of visual words at different scales, and it achieves promising classification results.…”

Section: Overview Of Contributionsmentioning

confidence: 99%

Remote Sensing Big Data: Theory, Methods and Applications

Liu

et al. 2018

Remote Sensing

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

Cited by 27 publications

References 48 publications

Object-Oriented Method Combined with Deep Convolutional Neural Networks for Land-Use-Type Classification of Remote Sensing Images

Object-Oriented Method Combined with Deep Convolutional Neural Networks for Land-Use-Type Classification of Remote Sensing Images

Concentric Circle Pooling in Deep Convolutional Networks for Remote Sensing Scene Classification

Remote Sensing Big Data: Theory, Methods and Applications

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