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numpy-100

100 numpy exercises (with solutions)

programmingassignment2

Repository for Programming Assignment 2 for R Programming on Coursera

py

Repository to store sample python programs for python learning

pydata-book

Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media

python-100-days

Python - 100天从新手到大师

python-core-50-courses

Python语言基础50课

python-mini-projects

A collection of simple python mini projects to enhance your python skills

python-programming-exercises

100+ Python challenging programming exercises

python_for_data_analysis_2nd_chinese_version

《利用Python进行数据分析·第2版》

remote-sensing-image-classification

Remote sensing image classification based on deep learning

sensor-specific-hyperspectral-image-feature-learning

Learning Sensor-Specific Spatial-Spectral Features of Hyperspectral Images via Convolutional Neural Networks

spectralnet

spectralnet-1

Hyperspectral Image (HSI) classification using Convolutional Neural Networks (CNN) is widely found in the current literature. Approaches vary from using SVMs to 2D CNNs, 3D CNNs, 3D-2D CNNs. Besides 3D-2D CNNs and FuSENet, the other approaches do not consider both the spectral and spatial features together for HSI classification task, thereby resulting in poor performances. 3D CNNs are computationally heavy and are not widely used, while 2D CNNs do not consider multi-resolution processing of images, and only limits itself to the spatial features. Even though 3D-2D CNNs try to model the spectral and spatial features their performance seems limited when applied over multiple dataset. In this article, we propose SpectralNET, a wavelet CNN, which is a variation of 2D CNN for multi-resolution HSI classification. A wavelet CNN uses layers of wavelet transform to bring out spectral features. Computing a wavelet transform is lighter than computing 3D CNN. The spectral features extracted are then connected to the 2D CNN which bring out the spatial features, thereby creating a spatial-spectral feature vector for classification. Overall a better model is achieved that can classify multi-resolution HSI data with high accuracy. Experiments performed with SpectralNET on benchmark dataset, i.e. Indian Pines, University of Pavia, and Salinas Scenes confirm the superiority of proposed SpectralNET with respect to the state-of-the-art methods.

ssdgl

SSDGL: A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification (TCYB2021) https://ieeexplore.ieee.org/document/9440852

tensorflow-deep-learning

All course materials for the Zero to Mastery Deep Learning with TensorFlow course.

test

test-repo

This is a test repo

waveletcnn

Wavelet CNN, Texture Classification in Keras