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sar-image-segmentation

The project consists of two architecture for SAR Image segmentation

sar-ship-dataset

sar.ai

:satellite: Multi-Level Pattern Histogram for Synthetic-Aperture Radar (SAR) image classification into terrain classes.

sar2opt-heterogeneous-dataset

sar_river_segmentation_pytorch-unet

This project details the development of a deep learning architecture for river segmentation from Synthetic Aperture Radar (SAR) images. The focus has been placed on detecting thin rivers as they are relatively more difficult to identify compared to larger ones

sar_specific_models

sar_vehicle_detection_dataset

High resolution SAR image vehicle detection dataset collected from Sandia MiniSAR/FARAD SAR images and MSTAR images

satellite-image-deep-learning

Deep learning with satellite & aerial imagery

sccovnet

SccovNet for remote sensing scene image classification which accepted by TNNLS

sea-ice-and-water-sar-imagery-classification

Sea Ice and water SAR Imagery Classification Using Convolutional Neural Networks

sf-cnn

Code for the paper of Scale-Free Convolutional Neural Network for Remote Sensing Scene Classification, which is accepted by IEEE Transactions on Geoscience and Remote Sensing

shearlet-framework

shearlet-framework-old

shearlet_cnn_super_resolution

ship-detection-from-satellite-images-using-yolov4

Ship detection from remote sensing imagery is a crucial application for maritime security which includes among others traffic surveillance, protection against illegal fisheries, oil discharge control and sea pollution monitoring. This is typically done through the use of an Automated Identification System (AIS), which uses VHF radio frequencies to wirelessly broadcast the ships location, destination and identity to nearby receiver devices on other ships and land-based systems. AIS are very effective at monitoring ships which are legally required to install a VHF transponder, but fail to detect those which are not, and those which disconnect their transponder. So how do you detect these uncooperative ships? This is where satellite imagery can help. Synthetic Aperture Radar (SAR) imagery uses radio waves to image the Earth’s surface. Unlike optical imagery, the wavelengths which the instruments use are not affected by the time of day or meteorological conditions, enabling imagery to be obtained day or night, with cloudy, or clear skies. Satellites are collecting these images which could be used to make algorithms for ship detection and segmentation.

siamxt

Simple iamxt toolbox

sinnlrr

SinNLRR: a robust subspace clustering meth-od for cell type detection by nonnegative and low rank representation

slic_for_hyperspectral_images

This set of files contains the Matlab code for the SLIC segmentation on the hyperspectral images.

slic_individual-light

A light version of SLIC_individual which consumes less memory.

smbn

snn

Matlab code implementation the modified Non Local Means and Bilateral filters, as described in I. Frosio, J. Kautz, Statistical Nearest Neighbors for Image Denoising, IEEE Trans. Image Processing, 2018. The repository also includes the Matlab code to replicate the results of the toy problem described in the paper.

sparse-representation-classifier

Machine learning algorithm that exploits the subspace property in data

ssan

Spectral-Spatial Attention Network for Hyperspectral Image Classification

ssnmf_classification

spectral image classification

ssrn

This is a tensorflow and keras based implementation of SSRNs in the IEEE T-GRS paper "Spectral-Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework".

sssanet

S3ANet: Spatial-Scattering Separated Attention Network for Polarimetric SAR Image Classification

step

CVPR2019 STEP: Spatio-Temporal Progressive Learning for Video Action Detection

sunsal-spark-pipeline

pfa

superpixel-based-fast-fuzzy-c-means-clustering-for-color-image-segmentation

We propose a superpixel-based fast FCM (SFFCM) for color image segmentation. The proposed algorithm is able to achieve color image segmentation with a very low computational cost, yet achieve a high segmentation precision.

superpixel-benchmark

An extensive evaluation and comparison of 28 state-of-the-art superpixel algorithms on 5 datasets.