jiajia99129

jiajia99129's Stars

• psellcam/Superpixel-Contracted-Graph-Based-Learning-for-Hyperspectral-Image-Classification

  Code for the Paper "Superpixel Contracted Graph-Based Learning for Hyperspectral Image Classification"

  Language:Python669

• Hang-Fu/MSF-PCs

  Fusion of PCA and Segmented-PCA Domain Multiscale 2-D-SSA for Effective Spectral-Spatial Feature Extraction and Data Classification in Hyperspectral Imagery, TGRS, 2020

  Language:MATLAB3

• davidstutz/extended-berkeley-segmentation-benchmark

  Extended version of the Berkeley Segmentation Benchmark [1] used for evaluation in [2].

  Language:C++4219

• dubin-learner/SalientRegionDetectionViaSuperpixelLevelAnalysis

  Save some code

  Language:MATLAB3

• GatorSense/SuperpixelMetrics

  Collection of traditional clustering metrics and modified superpixel versions

  Language:Jupyter Notebook2

• Jade999/face_antispoofing_matlab

  This is the code of "Face Anti-Spoofing using Speeded-Up Robust Features and Fisher Vector Encoding " with my own trained model

  Language:C214

• sheetal158/Image-Segmentation-by-Correlation-Clustering

  This is the project that we did for the Computer Vision course at Stony Brook University. It is a learning based method where the number of clusters do not have to be specified from the beginning. Image Segmentation by Correlation Clustering gave us extremely good results with Boundary displacement Error as low as 4.85 compared to 10.81 in the work "Higher-Order Correlation Clustering for Image Segmentation"

  Language:Matlab107

• liubing220524/MAPC-DRF-HSI

  MAPC and deep random forest for hyperspectral image classification

  Language:Python32

• mk60991/Keras-CNN-multiclass-image-classificication

  multiclass image classification using keras-CNN, SVM, and Random forest classification

  Language:Python22

• aslannezgiii/classification_hyperspectral_images

  Hyperspectral image classification with svm, knn and random forest

  Language:MATLAB8

• saguo/Image_Classification_Randomforests

  Machine-learning: Random Forests, SVM, Incremental Learning

  Language:Matlab85

• PraveenDubba/Image-Classification-using-Random-Forest

  Language:Python117

• fuy34/superpixel_fcn

  [CVPR‘20] SpixelFCN: Superpixel Segmentation with Fully Convolutional Network

  Language:Python38779

• aniket-k-mukherjee/ImageClassification

  Deep Learning has emerged as a new area in machine learning and is applied to a number of signal and image applications.The main purpose of the work presented in this paper, is to apply the concept of a Deep Learning algorithm namely, Convolutional neural networks (CNN) in image classification. The algorithm is tested on various standard datasets, like remote sensing data of aerial images (UC Merced Land Use Dataset) and scene images from SUN database. The performance of the algorithm is evaluated based on the quality metric known as Mean Squared Error (MSE) and classification accuracy. The graphical representation of the experimental results is given on the basis of MSE against the number of training epochs. The experimental result analysis based on the quality metrics and the graphical representation proves that the algorithm (CNN) gives fairly good classification accuracy for all the tested datasets.

  Language:Jupyter Notebook1

• wslerry/machine_learning_classifier

  QGIS plugin : Application of machine learning to generated classification data of remote sensing image.

  Language:Python3

• spectralpublic/SSAN

  This is the reserch code of the IEEE Transactions Geoscience and Remote Sensing 2020 paper "Spectral–Spatial Attention Network for Hyperspectral Image Classification".

  Language:Python4

• Chenyang-Qu/M-ASCNN-net

  This project is the code of the thesis "Multi-spectral remote sensing image classification method based on convolutional neural networks"

  Language:Python43

• firatkizilirmakk/MultiTemporal-MultiSpectral-RemoteSensing-CropClassification

  Crop Classification of Remotely Sensed Images containing Multi Temporal and Multispectral Information

  Language:Jupyter Notebook134

• susurrant/rs-img-classification

  Semantic segmentation of remote sensing images.

  Language:Python96

• rmkemker/EarthMapper

  Pipeline for the Semantic Segmentation (i.e., classification) of Remote Sensing Imagery

  Language:Python9649

• askeseler/superpixel_gmm_classification

  Language:Python1

• zcy179/MATLAB-codes-for-Hyperspectral-Image-Classification-With-Small-Training-Sample-Size-Using-Superpix-

  MATLAB codes for paper : ZHENG C, WANG N, CUI J. Hyperspectral Image Classification With Small Training Sample Size Using Superpixel-Guided Training Sample Enlargement. IEEE Transactions on Geoscience and Remote Sensing, 2019: 57(10): 7307-7316.

  Language:MATLAB102

• machine-reasoning-ufrgs/spixel-gat

  Companion code of "Superpixel Image Classification with GraphAttention Networks"

  272

• prashu324/Mean-Ensemble-for-HSI-classification

  This repository contains codes for Ensemble of multiple CNN classifiers for Hyperspectral Image Classification with superpixel smoothing

  Language:Python64

• qichaoliu/MSSG-UNet

  Q. Liu, L. Xiao, J. Yang and Z. Wei, "Multilevel Superpixel Structured Graph U-Nets for Hyperspectral Image Classification," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2021.3112586.

  Language:Python4810

• CallumAltham/AMUSE-CNN

  This repository is the official implementation of Adaptive Multiscale Superpixel Embedding Convolutional Neural Network for Land Use Classification. Code to build and train the networks are provided. The model takes in a provided remote sensing image and produces a colour coded output depending on provided categories.

  Language:Python2

• Jaychan-Tang/SNSSL

  Paper: Exploiting Superpixel-based Contextual Information on Active Learning for High Spatial Resolution Remote Sensing Image Classification

  Language:Python5

• llDev-Rootll/SLIC_Superpixel_classification_network

  Image segmentation using superpixels generated from SLIC

  Language:Jupyter Notebook21

• Cimy-wang/KNNRS-HSIC

  Code for KNN-based Representation of Superpixels for hyperspectral image classification

  Language:MATLAB62

• senjia1980/FG-SuULDA

  The code of the paper "Flexible Gabor-based Superpixel-level Unsupervised LDA for Hyperspectral Image Classification".

  Language:MATLAB156