grseb9s Goto Github PK

s1tbx

The Sentinel-1 Toolbox

s2_tilefinder

Find the Sentinel2 tiles intersecting your region of interest

s2p

Satellite Stereo Pipeline

s2p-1

This repository is not maintained, please use https://github.com/centreborelli/s2p instead.

s2tbx

Sentinel 2 Toolbox (s2tbx)

s3tbx

A toolbox for the OLCI and SLSTR instruments on board of ESA's Sentinel-3 satellite

salbenchmark

Salient Object Detection: A Benchmark

salib

Sensitivity Analysis Library in Python (Numpy). Contains Sobol, Morris, Fractional Factorial and FAST methods.

saliency-cnn-image-quality-assessment

saliency2013

Salient Object Detection in Hyperspectral Imagery

salient-object-detection

This is tensorflow implementation for cvpr2017 paper "Deeply Supervised Salient Object Detection with Short Connections"

salt_iccv2017

SALT (iccv2017) based Video Denoising Codes, Matlab implementation with state-of-the-art performance

sam-classification-of-satellite-images

The project aims at explaining the usage of SAM algorithm for satellite image classification. Hyperspectral Image provides pixel spectrum that fetches detailed information about a surface to identify and distinguish between spectrally similar (but unique) materials. The Hyperspectral Image sensor placed on board the Remote Sensing Satellite captures Hyperspectral Images with various bands of spectrum. Experiments are carried out for the implementation of Spectral Angle Mapper (SAM) on Hyper- spectral Images for classification of pixels on the surface. The false color composite of the image is also obtained for better visualization of surface differences. The Hyperspectral Images of various bands are stacked one after the other to form three-dimensional Cube of images for SAM implementation. SAM is a supervised classification algorithm which identifies the various classes in the image based on the calculation of the spectral angle. The spectral angle is calculated between the test vector built for each pixel and the reference vector built for each reference classes selected by the user. Results are obtained to read and reorganize multiple 2-D datasets into a single compact 3D dataset cube. The reference vector is built for performing SAM classification and the angle between the reference vector and pixel vector is calculated to compare with the determined threshold angle value. The color coding is then applied to distinguish between the various classes that have been recognized by the SAM algorithm. Hence using SAM, Hyperspectral images are analyzed to extract thematic information such as land-cover, water bodies, and clouds.

sample-data

Sample data files used in the SPy web site and documentation.

sample_svdd

A sampling SVDD algorithm for large datasets.

sampling-technique-for-fuzzy-c-means

Contains Random Sampling and Systematic Sampling Codes used for Fuzzy C Means Method

sampling_tensor_decomp

Fast Approximate Orthogonal Tensor Decomposition Based on Importance Sampling

sar-change-detection

Change detection in polarimetric SAR images in Python

sardocker

Source files for Docker image mort/sardocker/

saris

Synthetic Aperture Radar Imaging System

sarpy

Synthesis Aperture Radar (SAR) images processing

sasir-library

Sparse Aperture Synthesis Image Reconstruction libraries

sat-api-lib

A library for creating a search API of public Satellites metadata using Elasticsearch

sat-locat-reference-team-lucc

The teamlucc package is designed to facilitate analysis of land use and cover change (LUCC) around the monitoring sites of the Tropical Ecology Assessment and Monitoring (TEAM) Network. The TEAM Network is a global network of sites in tropical forests wth standardized real-time data collection designed to measure tropical forest responses to climate variability and change, land cover and land use change, and other threats.

satellite

work on the kaggle dstl competition (https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection/)

satellite-image-classification

Satellite images classification with Transfer learning approach

satellite-image-forgery-detection-and-localization

satellite-imagery-feature-detection

Solution to the DSTL Satellite Imagery Feature Detection challenge on Kaggle

satellite_challenge

Dstl Satellite Imagery Feature Detection - Kaggle challenge

satelliteimageclassification

Pixel based classification of satellite imagery - feature generation using Orfeo Toolbox, feature selection using Learning Vector Quantization, CLassification using Decision Tree, Neural Networks, Random Forests, KNN and Naive Bayes Classifier