Super‐speed up robust features image geometrical registration algorithm

Yan, Ning; Liao, Mengmeng; Jung, Cheolkon

doi:10.1049/iet-ipr.2015.0528

Cited by 6 publications

(1 citation statement)

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“…It implements some successful land-use scene classification methods [44] for highresolution imagery. Scale Invariant Feature Transform (SIFT) descriptor [45] represents feature extraction keypoint detection and densely sampled local regions with extending speedup with features (SURFs) [46] wolfing local feature detector and descriptor.…”

Section: Related Work a Remote Sensing Scene Classificationmentioning

confidence: 99%

Learning Multi-Granularity Neural Network Encoding Image Classification Using DCNNs for Easter Africa Community Countries

2021

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Remote sensing scene classification is a fundamental responsibility of earth observation, aiming to identify information granular for land cover classification. The multi-granular land use for multisource remotely sensed image categories is now a principal task in remote sensing data augmentation and data selection. Understanding image representations are meaningful for the scene classification task. Training deep learning model-based approaches has to do with scene classification and brings about fantastic achievement. At the same time, these high-level approaches are computationally expensive and timeconsuming. This paper introduces multi-granularity Neural Network Encoding architecture base on InceptionV3, InceptionReseNetV2, VGG16, and DenseNet201 architecture into remote sensing scene classification. To improve performance and to solve intra-class variation for multi-class scene issues remote sensing dataset. By using pre-trained CNN, activation function and ensemble learning have been adopting. InceptionV3 and VGG16 are used to extract features. InceptionResNetV2 use for fine-tuning, which consists of unfreezing the entre model and retraining the new data with a lower learning rate. The proposed fine-tune whole pre-trained model produces better results of test set up to 97.84 % than features extracted by InceptionResNetV2. Also, we use DCNNs ensemble average and weighted average to achieve better outcomes for 97.3.6% and 99.10%. In our experiments, we attempted to fine-tune the deep convolutional neural networks (DCNNs) training method for remote sensing scene classification on two public datasets UCM, SIRI-WHU, and one dataset I collected through the google earth engine from East Africa Community Countries (EACC) within nine classes within total 2112 labeled images. The results indicate that our proposed fine-tuning of the pre-trained model with few epochs and less computational time increases accuracy.INDEX TERMS Convolutional neural networks (CNNs), fine-tuning, granularity feature extraction, machine learning, and remote sensing (RS) I.

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Section: Related Work a Remote Sensing Scene Classificationmentioning

confidence: 99%