Abstract
Remote sensing scene classification is a fundamental responsibility of earth observation, aiming at identifying information granular for land cover classification. The multi-granular land use for multi-source 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 time–consuming. 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 97.36% 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.
Highlights
Land use is one of the most applications of Remote Sensing (RS) data[1]
Image classification methods based on deep convolutional neural networks (DCNN) are one way of turning private and public sensing data into meaningful categories[2][3]
The fine-tuning only the top layers and the time needed to fine-tune all layers are lower than fine-tuning the top layers
Summary
Land use is one of the most applications of Remote Sensing (RS) data[1]. RS technology helps collect electromagnetic radiation information from earth object target on satellite aircraft and identify the earth's environment and resources. It has been using in many practical applications such as RS classification, RS object detection, and RS segmentation. The land cover and land use classification system provides remote sensor data[4]–[6]. Land use (LU) plays an essential role in applying environmental protection, urban planning, and economic resource management[7]. Land cover detection is necessary to update land cover maps and manage natural resources, constituting a genuine challenge to build environmental satellite imagery applications [8].
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