Satellite image classification is an important and challenging task in the modern technological age. Satellites can capture images of danger-prone areas with very little effort. However, the size and ...

This project implements ResNet-50, a deep convolutional neural network with 50 layers that uses residual connections to enable training of very deep networks. The architecture includes identity ...

1 School of Electronics and Electrical Engineering, Zhengzhou University of Science and Technology, Zhengzhou, China 2 Department of Mechanical and Electrical Engineering, Henan Vocational College of ...

FYP_2025_Clean/ │ ├── CCA/ # Streamlit app folder │ ├── app.py # Main Streamlit app │ ├── pages/ # Subpages for UI │ ├── models/ # Model definitions │ │ └── trained_models/ # Pretrained .pth files │ ...

ABSTRACT: In this study, multi-source remote sensing data and machine learning algorithms were used to delineate the prospect area of remote sensing geological prospecting in eastern Botswana. Landsat ...

Abstract: Traditional joint sparse representation based hyperspectral classification methods define a local region for each pixel. Through representing the pixels within the local region ...

Abstract: The Land use and land cover classification is one of the important areas of remote sensing and satellite image interpretation. There are several techniques are implemented for the land use ...