Key Pointers for Developing Pre-Trained Convolutional Neural Networks for Remote Sensing Image Classification
Keywords:
Image Classification, Convolution Neural Networks, Pre-Trained ModelsAbstract
The process of correctly identifying objects in an image is known as image classification. Effectively classifying high-resolution spatial images for huge remote sensing archives is known as remote sensing image classification. Better classification performance is directly correlated with effective feature extraction from images. The majority of feature extraction steps employed manually created low-level features that concentrated on basic components like color, form, and texture before deep learning was widely used in remote sensing image classification. However, because of their poor performance, these conventional handmade methods were swiftly superseded by Convolution Neural Networks (CNN’s), that successfully recovered abstract information. However, while creating deep Convolution Neural Networks, it is important to carefully consider the significant training constraints of CNNs. This research aims to investigate the primary training challenges encountered while training deep learning pre-trained models utilizing a transfer learning approach.
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