Satellite Image Classification
11 papers with code • 4 benchmarks • 7 datasets
Satellite image classification is the most significant technique used in remote sensing for the computerized study and pattern recognition of satellite information, which is based on diversity structures of the image that involve rigorous validation of the training samples depending on the used classification algorithm.
Benchmarks
These leaderboards are used to track progress in Satellite Image Classification
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Datasets
WorldStrat
Cross-View Time Dataset
NASA Worldview
UV6K
Latest papers with no code
Adversarial Examples in Remote Sensing
This paper considers attacks against machine learning algorithms used in remote sensing applications, a domain that presents a suite of challenges that are not fully addressed by current research focused on natural image data such as ImageNet.
Learning Multi-Scale Deep Features for High-Resolution Satellite Image Classification
In this paper, we propose a multi-scale deep feature learning method for high-resolution satellite image classification.
Recurrent Neural Networks to Correct Satellite Image Classification Maps
Instead, our goal is to directly learn the iterative process itself.
Satellite image classification and segmentation using non-additive entropy
Here we compare the Boltzmann-Gibbs-Shannon (standard) with the Tsallis entropy on the pattern recognition and segmentation of coloured images obtained by satellites, via "Google Earth".
Satellite image classification methods and Landsat 5TM bands
This paper attempts to find the most accurate classification method among parallelepiped, minimum distance and chain methods.
