Strathprints Institutional Repository Shuhui and Ren, Jinchang (2015) Effective and Efficient Midlevel Visual Elements-oriented Land-use Classification Using Vhr Remote Sensing Images. Ieee…

By Gong Cheng, Junwei Han, Lei Guo, Zhenbao Liu, Shuhui Bu, Jinchang Ren

Subfields: Computer Vision and Pattern Recognition, Remote Sensing

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Citations in the corpus

RIFD-CNN: Rotation-Invariant and Fisher Discriminative Convolutional Neural Networks for Object Detection (2016, methodology): “The DPM [30] and its variants [20, 22, 23, 34-39] have been the leading methods for object detection tasks for years owing to the carefully crafted features like HOG [32].”
Exploring Models and Data for Remote Sensing Image Caption Generation (2017, background): “However, the studies of the remote sensing images are still concentrate on scene classification [5], [16]–[19], object recognition [20], [21], and segmentation [22]–[25].”
Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities (2020, background): “a large-scale remote sensing image that contains clear semantic information on the earth surface [37], [38].”
Scene classification of high resolution remote sensing images using convolutional neural networks (2016, unknown): “However, most of them are based on handcrafted or shallow learning-based features, such as bag-of-visual-words (BOVW) model [23] and its variations [4, 20, 21], sparse coding-based features [12, 19], midlevel visual elements-oriented features [9, 11, 15, 18] and so on.”
High-resolution remote-sensing imagery retrieval using sparse features by auto-encoder (2015, unknown): “Some other similar works can be found in Cheng et al. (2015) and Han et al. (2014, 2015).”
Land Use Classification in Remote Sensing Images by Convolutional Neural Networks (2015, methodology): “In Fig.9 we show the per-class accuracies provided by the proposed method and some selected references, PSR [18], Partlets [36], and UFL [19], using again the results reported in the original papers.”
Semantic Annotation of High-Resolution Satellite Images via Weakly Supervised Learning (2016, methodology): “For land-use classification experiments, we follow the setting in [2], [3], and [7] to randomly select 80% of the land-use images from each class for training and the remaining 20% of the images for testing.”
Pre-Trained AlexNet Architecture with Pyramid Pooling and Supervision for High Spatial Resolution Remote Sensing Image Scene Classification (2017, background): “The rapid development of HSR remote sensing imaging sensors has provided us with a large number of HSR remote sensing images with abundant detail and structural information, and a higher spatial resolution [1].”
Deep Feature Fusion for VHR Remote Sensing Scene Classification (2017, methodology): “images in [10], where the two proposed methods are based on part detection and they achieve a high accuracy on the public”
Building Instance Classification Using Street View Images (2018, background): “, 2013) and features learned by sparse coding methods (Wang et al., 2014; Yang et al., 2014; Sun et al., 2015; Rigas et al., 2013; Zhang et al., 2015; Tuia et al., 2015, 2016; Yao et al., 2016; Cheng et al., 2015).”
Object detection in VHR optical remote sensing images via learning rotation-invariant HOG feature (2016, background): “More recently, with the advances of machine learning, especially the powerful feature representations and classifiers, many approaches regarded object detection as a classification problem [1-22].”
AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification (2016, methodology): “growing attention in recent years [2]–[4], [7]–[42].”
Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects (2021, background): “and land cover [29]–[32], environment monitoring and natural hazards detection [33], [34], vegetation mapping [35], [36] and urban planning.”
The Fisher Kernel Coding Framework for High Spatial Resolution Scene Classification (2016, methodology): “To bridge the semantic gap, scene classification methods based on the bag-of-visual-words (BOVW) model [13–18], part detectors [19,20], and neural networks [21–23] have been proposed, among which the BOVW model is one of the most popular approaches.”
Unsupervised Multilayer Feature Learning for Satellite Image Scene Classification (2016, methodology): “In addition, the part object detectors in [11] and [12] depend on the pretrained classifiers.”