Papers & contributions

Hyperspectral and Multispectral Image Fusion Using a Multi-Level Propagation Learning Network

Abstract:

Data fusion techniques have gained special interest in remote sensing due to the available capabilities to obtain measurements from the same scene using different instruments with varied resolution domains. In particular, multispectral (MS) and hyperspectral (HS) imaging fusion is used to generate high spatial and spectral images (HSEI). Deep learning data fusion models based on Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN) have been developed to achieve such task.In this work, we present a Multi-Level Propagation Learning Network (MLPLN) based on a LSTM model but that can be trained with variable data sizes in order achieve the fusion process. Moreover, the MLPLN provides an intrinsic data augmentation feature that reduces the required number of training samples. The proposed model generates a HSEI by fusing a high-spatial resolution MS image and a low spatial resolution HS image. The performance of the model is studied and compared to existing CNN and LSTM approaches by evaluating the quality of the fused image using the structural similarity metric (SSIM). The results show that an increase in the SSIM is still obtained while reducing of the number of training samples to train the MLPLN model.

Speaker: Carlos Theran 

Full paper IEEE

Analyzing the Effects of Pixel-Scale Data Fusion in Hyperspectral Image Classification Performance

Recently, multispectral and hyperspectral data fusion models based on deep learning have been proposed to generate images with a high spatial and spectral resolution. The general objective is to obtain images that improve spatial resolution while preserving high spectral content. In this work, two deep learning data fusion techniques are characterized in terms of classification accuracy. These methods fuse a high spatial resolution multispectral image with a lower spatial resolution hyperspectral image to generate a high spatial-spectral hyperspectral image. The first model is based on a multi-scale long short-term memory (LSTM) network. The LSTM approach performs the fusion using a multiple step process that transitions from low to high spatial resolution using an intermediate step capable of reducing spatial information loss while preserving spectral content. The second fusion model is based on a convolutional neural network (CNN) data fusion approach. We present fused images using four multi-source datasets with different spatial and spectral resolutions. Both models provide fused images with increased spatial resolution from 8m to 1m. The obtained fused images using the two models are evaluated in terms of classification accuracy on several classifiers: Minimum Distance, Support Vector Machines, Class-Dependent Sparse Representation and CNN classification. The classification results show better performance in both overall and average accuracy for the images generated with the multi-scale LSTM fusion over the CNN fusion.

Speaker: Michael Alvarez 

Full paper and presentation at SPIE 

Studying the effect of a decision fusion model on enhanced hyperspectral images

Abstract:

Recent advances in data fusion provide the capability to obtain enhanced hyperspectral data with high spatial and spectral information content, thus allowing for an improved classification accuracy. Although hyperspectral image classification is a highly investigated topic in remote sensing, each classification technique presents different advantages and disadvantages. For example; methods based on morphological filtering are particularly good at classifying human-made structures with basic geometrical spatial shape, like houses and buildings. On the other hand, methods based on spectral information tend to perform better classification in natural scenery with more shape diversity such as vegetation and soil areas. Even more, for those classes with mixed pixels, small training data or objects with similar re ectance values present a higher challenge to obtain high classification accuracy. Therefore, it is difficult to find just one technique that provides the highest accuracy of classification for every class present in an image. This work proposes a decision fusion approach aiming to increase classification accuracy of enhanced hyperspectral images by integrating the results of multiple classifiers. Our approach is performed in two-steps: 1) the use of machine learning algorithms such as Support Vector Machines (SVM), Deep Neural Networks (DNN) and Class-dependent Sparse Representation will generate initial classification data, then 2) the decision fusion scheme based on a Convolutional Neural Network (CNN) will integrate all the classification results into a unified classification rule. In particular, the CNN receives as input the different probabilities of pixel values from each implemented classifier, and using a softmax activation function, the final decision is estimated. We present results showing the performance of our method using different hyperspectral image datasets. 

Speaker: Carlos Theran 

Full paper and presentation at SPIE