LixinHHU

LixinHHU's Stars

• IDEA-Research/Grounded-Segment-Anything

  Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything

  Language:Jupyter Notebook14.7k1.4k

• tomeramit/SegDiff

  Language:Python13315

• diff-usion/Awesome-Diffusion-Models

  A collection of resources and papers on Diffusion Models

  Language:HTML10.7k926

• ZwwWayne/K-Net

  [NeurIPS2021] Code Release of K-Net: Towards Unified Image Segmentation

  Language:Python46244

• ethanweber/IncidentsDataset

  Code and data (Incidents Dataset) for ECCV 2020 Paper "Detecting natural disasters, damage, and incidents in the wild".

  Language:Jupyter Notebook8125

• LixinHHU/Remote-sensing-image-semantic-segmentation

  The project uses Unet-based improved networks to study Remote sensing image semantic segmentation, which is based on keras.

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• hellozhuo/pidinet

  Code for the ICCV 2021 paper "Pixel Difference Networks for Efficient Edge Detection" (Oral).

  Language:Python44569

• LixinHHU/mmsegmentation

  OpenMMLab Semantic Segmentation Toolbox and Benchmark.

  1

• feevos/resuneta

  mxnet source code for the resuneta semantic segmentation models

  Language:Python11930

• RashmiS5/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.

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