Deep Learning Based Feature Selection for Remote Sensing Scene Classification
By Qin Zou, Lihao Ni, T. Zhang, Qian Wang. 2015
795 citations recorded by the source corpus.
With the popular use of high-resolution satellite images, more and more research efforts have been placed on remote sensing scene classification/recognition. In scene classification, effective feature selection can significantly boost the final performance. In this letter, a novel deep-learning-based feature-selection method is proposed, which formulates the feature-selection problem as a feature reconstruction problem. Note that the popular deep-learning technique, i.e., the deep belief network (DBN), achieves feature abstraction by minimizing the reconstruction error over the whole feature set, and features with smaller reconstruction errors would hold more feature intrinsics for image representation. Therefore, the proposed method selects features that are more reconstructible as the discriminative features. Specifically, an iterative algorithm is developed to adapt the DBN to produce the inquired reconstruction weights. In the experiments, 2800 remote sensing scene images of seven categories are collected for performance evaluation. Experimental results demonstrate the effectiveness of the proposed method.
Subfields: Computer Vision and Pattern Recognition, Media Technology
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Citations in the corpus
- Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks (2019, neutral): “Training of neural networks, is usually performed using pre-trained networks on large image datasets, e.g., COCO (Lin et al., 2014), Pascal VOC (Everingham et al., 2010) or ImageNet (Deng et al., 2009) which in general reach impressive accuracies (Hu et al., 2015; Zou et al., 2015).”
- Geographical Knowledge-Driven Representation Learning for Remote Sensing Images (2021, methodology): “We validate the effectiveness of pre-training on the RSSCN7 and Vaihingen datasets, respectively, using ResNet50 as the backbone.”
- Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects (2021, background): “Such models have shown great potential for feature representation in remote sensing image classification [86], [87].”
- A Review on Deep Learning in UAV Remote Sensing (2021, background): “Scene-wise classification or scene recognition refers to methods that associate a label / theme for one image (or patch) based on numerous images, such as in agricultural scenes, beach scenes, urban scenes, and others [219, 128].”
- Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art (2016, background): “In recent years, it has also been successfully proposed for object recognition [56] and scene classification [57].”
- Multisource Remote Sensing Data Classification Based on Convolutional Neural Network (2018, methodology): “Due to its effectiveness, the technique has been applied for large-scale highresolution remote-sensing image classification [44]–[52], and demonstrated superior performance over traditional methods.”
- AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification (2016, methodology): “3) RSSCN7 Data Set [41]: This data set is also collected from Google Earth,3 which contains 2800 aerial scene images labeled into 7 typical scene categories, i.”
- CLRS: Continual Learning Benchmark for Remote Sensing Image Scene Classification (2020, background): “With the rapid development of remote sensing imaging technology, high-resolution remote sensing images are easily obtained and the interpretation of remote sensing images has evolved from the pixel- to the scene-level [1,2].”
- Building Instance Classification Using Street View Images (2018, methodology): “In [39], deep belief networks are employed for the feature learning in remote sensing scene classification.”
- Bigearthnet: A Large-Scale Benchmark Archive for Remote Sensing Image Understanding (2019, background): “RSSCN7 [4] Aerial RGB Single Label 2,800”
- Deep learning in remote sensing: a review (2017, background): “Consequently, some construct a new smaller network and train it from scratch using satellite images to better fit the satellite data [80], [84], [85], [92].”
- Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities (2020, neutral): “4& 6 [126], RSSCN7 [125], Brazilian Coffee Scene [85], RSC11 [128], SIRI-WHU [127], RSCI-CB [129], AID [79], NWPU-RESISC45 [80], OPTIMAL-31 [74], EuroSAT [130],”
- Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review (2020, background): “For instance, the authors in [61] propose a model seeing the feature selection problem as a feature reconstruction problem using a deep belief network and compare its efficiency in time with a deep CNN end-to-end model.”
- EarthGPT: A Universal Multimodal Large Language Model for Multisensor Image Comprehension in Remote Sensing Domain (2024, background): “It is comprised of 45 scene categories, with each category containing 700 RS images.”
- Deep Feature Fusion for VHR Remote Sensing Scene Classification (2017, background): “[14] proposed deep learning to select informative features for remote sensing scene classification.”
- Deep learning for remote sensing image classification: A survey (2018, methodology): “…been different approaches proposed in the literature, where smaller networks are used to better fit the RS images (Luus et al., 2015; Volpi & Tuia, 2016; Zhang, Du, & Zhang, 2016; Zou et al., 2015), which may also help improve the performance regarding the overfitting and local minimum problems.”