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
Enhancing Ship Classification in Optical Satellite Imagery: Integrating Convolutional Block Attention Module with ResNet for Improved Performance
This study presents an advanced Convolutional Neural Network (CNN) architecture for ship classification from optical satellite imagery, significantly enhancing performance through the integration of the Convolutional Block Attention Module (CBAM) and additional architectural innovations.
Improving Human-AI Collaboration With Descriptions of AI Behavior
People work with AI systems to improve their decision making, but often under- or over-rely on AI predictions and perform worse than they would have unassisted.
Data Generation for Satellite Image Classification Using Self-Supervised Representation Learning
Supervised deep neural networks are the-state-of-the-art for many tasks in the remote sensing domain, against the fact that such techniques require the dataset consisting of pairs of input and label, which are rare and expensive to collect in term of both manpower and resources.
2-speed network ensemble for efficient classification of incremental land-use/land-cover satellite image chips
Recognizing the need for an adaptable, accurate, and scalable satellite image chip classification scheme, in this research we present an ensemble of: i) a slow to train but high accuracy vision transformer; and ii) a fast to train, low-parameter convolutional neural network.
Out-of-distribution detection in satellite image classification
In satellite image analysis, distributional mismatch between the training and test data may arise due to several reasons, including unseen classes in the test data and differences in the geographic area.
Satellite Image Classification with Deep Learning
Yet traditional object detection and classification algorithms are too inaccurate and unreliable to solve the problem.
SatImNet: Structured and Harmonised Training Data for Enhanced Satellite Imagery Classification
Automatic supervised classification with complex modelling such as deep neural networks requires the availability of representative training data sets.
Using satellite image classification and digital terrain modelling to assess forest species distribution on mountain slopes.A case study in Varatec Forest District
The relation between ecological conditions and geomorphological factors is considered the basis for species distribution in Romania.
Morphological Network: How Far Can We Go with Morphological Neurons?
A few works have tried to utilize morphological neurons as a part of classification (and regression) networks when the input is a feature vector.
predictSLUMS: A new model for identifying and predicting informal settlements and slums in cities from street intersections using machine learning
We applied the model in five major cities in Egypt and India that have spatial structures in which informality is present.
