Microwave Remote Sensing Lab's repositories
GRSS-IADF-School2022
IEEE-GRSS IADF School -- PolSAR Session
CONAESpringSchool2021
Session 22 September 2021
CMIR-NET-A-deep-learning-based-model-for-cross-modal-retrieval-in-remote-sensing
We address the problem of cross-modal information retrieval in the domain of remote sensing. In particular, we are interested in two application scenarios: i) cross-modal retrieval between panchromatic (PAN) and multispectral imagery, and ii) multi-label image retrieval between very high resolution (VHR) images and speech-based label annotations. These multi-modal retrieval scenarios are more challenging than the traditional uni-modal retrieval approaches given the inherent differences in distributions between the modalities. However, with the increasing availability of multi-source remote sensing data and the scarcity of enough semantic annotations, the task of multi-modal retrieval has recently become extremely important. In this regard, we propose a novel deep neural network-based architecture that is considered to learn a discriminative shared feature space for all the input modalities, suitable for semantically coherent information retrieval. Extensive experiments are carried out on the benchmark large-scale PAN - multispectral DSRSID dataset and the multi-label UC-Merced dataset. Together with the Merced dataset, we generate a corpus of speech signals corresponding to the labels. Superior performance with respect to the current state-of-the-art is observed in all the cases.
Image-to-Region-Adjacency-Graph-creation
Convertion of an RGB image to a Region Adjacency Graph (RAG) using SLIC super-pixel based segmentation technique.
MachineLearning
Code repository for the C-MInDS, IIT Bombay course.
PlanetaryComputerExamples
Examples of using the Planetary Computer
PolSAR-tools
A QGIS plugin to generate polarimetric SAR descriptors.
Siamese-spatial-Graph-Convolution-Network
Siamese graph convolutional network for content based remote sensing image retrieval