Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Deep Learning for Land-cover Classification in Hyperspectral Images.
| Date | Stars |
|---|---|
| 2026-07-31 | 307 |
| 2026-08-01 | 307 |
| 2026-08-06 | 307 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
This repository has been ⛔️ DEPRECATED. Please take a look at our fairly recent work: **Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification** [[paper](https://arxiv.org/abs/1612.00144)] [[Code](https://github.com/kaustubh0mani/BASS-Net)] Deep Learning for Land-cover Classification in Hyperspectral Images =================================================================== Hyperspectral images are images captured in multiple bands of the electromagnetic spectrum. This project is focussed at the development of Deep Learned Artificial Neural Networks for robust landcover classification in hyperspectral images. Land-cover classification is the task of assigning to every pixel, a class label that represents the type of land-cover present in the location of the pixel. It is an image segmentation/scene labeling task. The following diagram describes the task. <hr> <img src="https://github.com/KGPML/Hyperspectral/blob/master/images/landcover-classification.png?raw=True" width="600"> <hr> This website describes our explorations with the performance of Multi-Layer Perceptrons and Convolutional Neural Networks at the task of Land-cover Classification in Hyperspectral Images. Currently we perform pixel-wise classification. <hr> Dataset ======= We have performed our experiments on the [Indian Pines Dataset](https://purr.purdue.edu/publications/1947/1). The following are the particulars of the dataset: * Source: AVIRIS sensor * Region: Indian Pines test site over north-western Indiana * Time of the year: June * Wavelength range: 0.4 – 2.5 micron * Number of spectral bands: 220 * Size of image: 145x145 pixel * Number of land-cover classes: 16 <hr> Input data format ================= Each pixel is described by an `NxN` patch centered at the pixel. `N` denotes the size of spatial context used for making the inference about a given pixel. The input data was divided into training set (75%) and a test set (25%). Hardware used ============= The neural networks were trained on a machine with dual Intel Xeon E5-2630 v2 CPUs, 32 GB RAM and NVIDIA Tesla K-20C GPU. <hr> Multi-Layer Perceptron ====================== Multi-Layer Perceptron (MLP) is an artificial neural network with one or more hidden layers of neurons. MLP is capable of modelling highly non-linear functions between the input and output and forms the basis of Deep-learning Neural Network (DNN) models. Architecture of Multi-Layer Perceptron used ------------------------------------------------- **input- [affine - relu] x 3 - affine - softmax** (Schematic representation below) <img src="https://github.com/KGPML/Hyperspectral/blob/master/images/architecture-MLP.png?raw=True" width="400"> `N`denotes the size of the input patch. <hr> Specifics of the learning algorithm ----------------------------------- The following are the details of the learning algorithm used: * Parameter update algorithm used: [Adagrad](http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf) * Batch size: 200 * Learning rate: 0.01 * Number of steps: until best validation performance <hr> Performance ----------- <img src="https://github.com/KGPML/Hyperspectral/blob/master/images/accuracy-bar-MLP.png?raw=True" width="500"> Decoding generated for different input patch sizes: <img src="https://github.com/KGPML/Hyperspectral/blob/master/images/performance-MLP.png?raw=True" width="800"> <hr> Convolutional Neural Network ============================ (CNN or ConvNet) are a special category of artificial neural networks designed for processing data with a gridlike structure. The ConvNet architecture is based on sparse interactions and parameter sharing and is highly effective for efficient learning of spatial invariances in images. There are four kinds of layers in a typical ConvNet architecture: convolutional (conv), pooling (pool), fullyconnected (affine) and rectifying linear unit (ReLU). Each convolutional layer transforms one s
Excerpt of 9,187 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b977ed63d0981140, llm:Repository description: "Deep Learning for Land-cover Classification in Hyperspectral Images." Language: Jupyter Notebook. No topics provided.
matched fp:b977ed63d0981140, llm:Repository description: "Deep Learning for Land-cover Classification in Hyperspectral Images." Language: Jupyter Notebook. No topics provided.
matched fp:b977ed63d0981140, llm:Repository description: "Deep Learning for Land-cover Classification in Hyperspectral Images." Language: Jupyter Notebook. No topics provided.