Satellite Image Classification

7 papers with code • 3 benchmarks • 5 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.

Most implemented papers

Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classification

biasvariancelabs/aitlas-arena 14 Jul 2022

We present AiTLAS: Benchmark Arena -- an open-source benchmark suite for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation (EO).

DeepSat - A Learning framework for Satellite Imagery

debanjanxy/GNR-652 11 Sep 2015

Satellite image classification is a challenging problem that lies at the crossroads of remote sensing, computer vision, and machine learning.

Discriminative Learning of Deep Convolutional Feature Point Descriptors

etrulls/deepdesc-release ICCV 2015

Deep learning has revolutionalized image-level tasks such as classification, but patch-level tasks, such as correspondence, still rely on hand-crafted features, e. g. SIFT.

An Open-source Tool for Hyperspectral Image Augmentation in Tensorflow

mabdelhack/hyperspectral_image_generator 30 Mar 2020

Tensorflow tool allows for rapid prototyping and testing of deep learning models, however, its built-in image generator is designed to handle a maximum of four spectral channels.

Classification and understanding of cloud structures via satellite images with EfficientUNet

TashinAhmed/CloudsClassification 27 Sep 2020

Climate change has been a common interest and the forefront of crucial political discussion and decision-making for many years.

Generative Adversarial Minority Oversampling for Spectral-Spatial Hyperspectral Image Classification

mhaut/3D-HyperGAMO 1 Feb 2021

A different classifier from the generator and the discriminator is used in the 3D-HyperGAMO model, which is trained using both original and generated samples to {determine} the classes of newly generated samples to which they actually belong.

Diagnosing Model Performance Under Distribution Shift

namkoong-lab/disde 3 Mar 2023

In order to do this, we define a hypothetical distribution on $X$ consisting of values common in both training and target, over which it is easy to compare $Y \mid X$ and thus predictive performance.