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sar-despeckling

sar-ship-dataset

sar_change_detection_cwnn

Matlab code for the GRSL19 paper "Sea ice change detection in SAR images based on convolutional-wavelet neural networks"

sar_change_detection_fda_rmg

The matlab code for the JARS18 paper "Synthetic aperture radar image change detection based on frequency domain analysis and random multigraphs"

sar_change_detection_garborpcanet

Matlab code for paper "Automatic Change Detection in Synthetic Aperture Radar Images Based on PCANet", IEEE GRSL 2016

sar_change_detection_nr_elm

The matlab code for the JARS16 paper "Change Detection from Synthetic Aperture Radar Images Based on Neighborhood-Based Ratio and Extreme Learning Machine"

sdm

A Monitor for Automatically Measuring Structural Deformation

sen2proc

Sen2Cor and Sen2Three processors

senseearth2020-changedetection

1st place solution to the Satellite Image Change Detection Challenge hosted by SenseTime

slightlimon

A free and open-source profile analyzer for street lighting power meter data

smart-monitoring-of-deformations-on-transformers-using-image-processing-and-uwb-transceivers

In this paper, a novel method based on image processing is proposed for detecting the presence and location of mechanical deformations on an actual power transformer winding. A vertical imaging setup is used to obtain a two-dimensional (2-D) image of the transformer winding using synthetic aperture radar (SAR) imaging method and Kirchhoff migration algorithm. The main goal of the image processing method is to detect the location of the radial deformation on the transformer high-voltage (HV) winding. Two different deformations are applied on the winding under the test in this paper. The first one is a modeled bulgymechanical deformation and the second one is an actual concave buckling made on the actual transformer HV winding. The experimental results for different cases show the effectiveness of the proposed method to detect the presence and location of the deformation on the transformer winding.

smartcamera_opencl_de1soc

This project contains code developed by a research team at University of Colorado,Boulder and Embry Riddle Aeronautical University to explore FPGA and GP-GPU co-processor power efficiency for continous transform 3D mapping and sensor fusion. Please use at your own risk. We are sharing so that other researchers and developers can recreate our results and make suggestions to improve and extend the benchmarks over time. The results we obtained were documented in the SPIE Sensor and Technologies paper: S. Siewert, V. Angoth, R. Krishnamurthy, K. Mani, K. Mock, S. B. Singh, S. Srivistava, C. Wagner, R. Claus, M. Demi Vis, “Software Defined Multi-Spectral Imaging for Arctic Sensor Networks”, SPIE Algorithms and Technologies for Multipectral, Hyperspectral, and Ultraspectral Imagery XXII, Baltimore, Maryland, April 2016. This repository includes FPGA codes

sofa

SHARAD Open Focusing Attempt

sopa

SHARAD Open Processing Attempt

speckle2void

Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural Networks