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👉 satellite-image-deep-learning.com 👈

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Introduction

Deep learning has revolutionized the analysis and interpretation of satellite and aerial imagery, addressing unique challenges such as vast image sizes and a wide array of object classes. This repository provides an exhaustive overview of deep learning techniques specifically tailored for satellite and aerial image processing. It covers a range of architectures, models, and algorithms suited for key tasks like classification, segmentation, and object detection.

How to use this repository: use Command + F (Mac) or CTRL + F (Windows) to search this page for e.g. 'SAM'

Techniques

• Classification
• Segmentation
• Object detection
• Regression
• Cloud detection & removal
• Change detection
• Time series
• Crop classification
• Crop yield & vegetation forecasting
• Generative networks
• Autoencoders, dimensionality reduction, image embeddings & similarity search
• Few & zero shot learning
• Self-supervised, unsupervised & contrastive learning
• SAR
• Explainable Ai (XAI)
• Large vision & language models (LLMs & LVMs)
• Foundational models

Classification

<p align="center"> <img src="images/merced.png" width="600"> <br> <b>The UC merced dataset is a well known classification dataset.</b> </p>

Classification is a fundamental task in remote sensing data analysis, where the goal is to assign a semantic label to each image, such as 'urban', 'forest', 'agricultural land', etc. The process of assigning labels to an image is known as image-level classification. However, in some cases, a single image might contain multiple different land cover types, such as a forest with a river running through it, or a city with both residential and commercial areas. In these cases, image-level classification becomes more complex and involves assigning multiple labels to a single image. This can be accomplished using a combination of feature extraction and machine learning algorithms to accurately identify the different land cover types. It is important to note that image-level classification should not be confused with pixel-level classification, also known as semantic segmentation. While image-level classification assigns a single label to an entire image, semantic segmentation assigns a label to each individual pixel in an image, resulting in a highly detailed and accurate representation of the land cover types in an image. Read A brief introduction to satellite image classification with neural networks

• EuroSat-Satellite-CNN-and-ResNet -> Classifying custom image datasets by creating Convolutional Neural Networks and Residual Networks from scratch with PyTorch

• Detecting Informal Settlements from Satellite Imagery using fine-tuning of ResNet-50 classifier with repo

• Land-Cover-Classification-using-Sentinel-2-Dataset -> well written Medium article accompanying this repo but using the EuroSAT dataset

• Slums mapping from pretrained CNN network on VHR (Pleiades: 0.5m) and MR (Sentinel: 10m) imagery

• Comparing urban environments using satellite imagery and convolutional neural networks -> includes interesting study of the image embedding features extracted for each image on the Urban Atlas dataset

• RSI-CB -> A Large Scale Remote Sensing Image Classification Benchmark via Crowdsource Data. See also Remote-sensing-image-classification

• WaterNet -> a CNN that identifies water in satellite images

• Road-Network-Classification -> Road network classification model using ResNet-34, road classes organic, gridiron, radial and no pattern

• SSTN -> Spectral-Spatial Transformer Network for Hyperspectral Image Classification: A FAS Framework

• SatellitePollutionCNN -> A novel algorithm to predict air pollution levels with state-of-the-art accuracy using deep learning and GoogleMaps satellite images

• PropertyClassification -> Classifying the type of property given Real Estate, satellite and Street view Images

• remote-sense-quickstart -> classification on a number of datasets, including with attention visualization

• IGARSS2020_BWMS -> Band-Wise Multi-Scale CNN Architecture for Remote Sensing Image Scene Classification with a novel CNN architecture for the feature embedding of high-dimensional RS images

• image.classification.on.EuroSAT -> solution in pure pytorch

• hurricane_damage -> Post-hurricane structure damage assessment based on aerial imagery

• ISPRS_S2FL -> Multimodal Remote Sensing Benchmark Datasets for Land Cover Classification with A Shared and Specific Feature Learning Model

• ensemble_LCLU -> Deep neural network ensembles for remote sensing land cover and land use classification

• Urban-Analysis-Using-Satellite-Imagery -> classify urban area as planned or unplanned using a combination of segmentation and classification

• mining-discovery-with-deep-learning -> Mining and Tailings Dam Detection in Satellite Imagery Using Deep Learning

• sentinel2-deep-learning -> Novel Training Methodologies for Land Classification of Sentinel-2 Imagery

• Pay-More-Attention -> Remote Sensing Image Scene Classification Based on an Enhanced Attention Module

• Remote Sensing Image Classification via Improved Cross-Entropy Loss and Transfer Learning Strategy Based on Deep Convolutional Neural Networks

• SKAL -> Looking Closer at the Scene: Multiscale Representation Learning for Remote Sensing Image Scene Classification

• SAFF -> Self-Attention-Based Deep Feature Fusion for Remote Sensing Scene Classification

• GLNET -> Convolutional Neural Networks Based Remote Sensing Scene Classification under Clear and Cloudy Environments

• Remote-sensing-image-classification -> transfer learning using pytorch to classify remote sensing data into three classes: aircrafts, ships, none

• remote_sensing_pretrained_models -> as an alternative to fine tuning on models pretrained on ImageNet, here some CNN are pretrained on the RSD46-WHU & AID datasets

• OBIC-GCN -> Object-based Classification Framework of Remote