Multi-class geospatial object detection and geographic image classification based on collection of part detectors

By Gong Cheng, Junwei Han, Peicheng Zhou, Lei Guo. 2014

928 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology, Ocean Engineering

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Citations in the corpus

Tiny Object Detection in Aerial Images (2021, background): “In the past years, numerous object detection datasets in aerial images, such as NWPU VHR-10 [16], HRSC2016 [17], VEDAI [18], xView [19], DOTA [1], VisDrone [20], UAVDT [21], and DIOR [3] have been proposed to make advancement in object detection research for Earth Vision.”
Deep Feature Fusion for VHR Remote Sensing Scene Classification (2017, methodology): “based on part detection [9], and land-use classification with compressive sensing multifeature fusion reported in [45] and”
Unsupervised Feature Learning Via Spectral Clustering of Multidimensional Patches for Remotely Sensed Scene Classification (2015, background): “In spite of a little better performance than ours, the computation cost of method in [55] is limited by the number of part detectors.”
Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects (2021, methodology): “Usually they are used to construct bag-of-visualwords (BoVW) models [36], [37], [41], [71], [76]–[81] and HOG feature-based models [35], [82].”
Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmark (2019, methodology): “This dataset has been widely used in the earth observation community (Cheng et al., 2014; Cheng et al., 2018b; Farooq et al., 2017; Guo et al., 2018; Han et al., 2017a; Li et al., 2018; Yang et al., 2018b; Yang et al., 2017; Zhong et al., 2018).”
Detecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmark (2022, neutral): “…al., 2020b), DOTA (Xia et al., 2018), xView (Lam et al., 2018), VisDrone (Zhu et al., 2018), HRSC2016 (Liu et al., 2016b), VEDAI (Razakarivony and Jurie, 2016), NWPU VHR-10 (Cheng et al., 2014), UAVDT (Yu et al., 2019), and FAIR1M (Sun et al., 2021b) are all public aerial object detection datasets.”
Using convolutional features and a sparse autoencoder for land-use scene classification (2016, neutral): “Cheng et al. (2014) introduced a rotation invariant approach for multi-class geospatial object detection and geographic image classi ﬁ cation based on the collection of part detectors.”
AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification (2016, background): “Due to the drastically increasing number of aerial images and the highly complex geometrical structures and spatial patterns, to effectively understand the semantic content of them is particularly important, driven by many real-world applications in remote sensing community.”
Semantic Classification of High-Resolution Remote-Sensing Images Based on Mid-level Features (2016, background): “It has attracted such intensive attention in visual categorization that the model is introduced into many fields due to its good performance and flexibility, such as scene classification [7], [8], object detection [9], and cloud extraction [10].”
Scene classification of high resolution remote sensing images using convolutional neural networks (2016, background): “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.”
Weakly supervised target detection in remote sensing images based on transferred deep features and negative bootstrapping (2015, methodology): “Finally, a post-processing scheme is used to eliminate repeated detections via non-maximum suppression (Cheng et al. 2013b, 2014; Han et al. 2015a, 2014; Zhang et al. 2015).”