Performance of Deep Learning in Land Use Land Cover Classification of Indian Remote Sensing (IRS) LISS – III Multispectral Data

Identification of land use land cover is a very important task. However, methods existing for the above mention purpose are labor incentives, time-consuming, and costly. Remote sensing plays very important role in the mappings. classification of land cover features and offers very noteworthy and sensed information. The present study shows the semantic segmentation of Indian remote sensing (IRS) LISS-III multispectral image and the comparison of three algorithms U-Net, Deeplabv3+and Tiramisu. The deep neural network was used to perform the study. We present total 3 innovative datasets, built on these LISS-III images that has 4 different spectral bands (Band – 2 (Blue), Band-3 (Green), Band-4(Red), and Band-5 (Nearly Infrared), FCC (false color composite) images and the ground truth mask images. Dataset has 13500 labelled images. A fully-convolutional network (FCN) with skip connections is trained to take an input image of size 128 X 128 X 3 and outputs a matrix of shape 128 X 128 X 4 i.e., a one-hot encoded version of the mask. The experiment identifies 4 classes successfully (Water Bodies, Vegetation, Uncultivated Land, and Residential areas). The experiment showed that the U-Net algorithm has a very good capability for the classification of LISS -III images for land use land cover class detection then Tiramisu and Deeplabv3+. U-Net achieved accuracy 84%, Deelabv3+ achieved 29% whereas Tiramisu achieved accuracy 33%

Análisis y estudio de independencia espectral entre sensores espaciales y aerotransportados: integración con LiDAR

En esta Tesis Doctoral se han analizado un conjunto de nuevas técnicas de explotación de la información recogida por sensores espaciales y aerotransportados, estudiando las ventajas de una explotación combinada entre sensores. Este planteamiento general se concreta en los siguientes objetivos: Analizar el concepto independencia en el procesado y explotación de imágenes. Evaluar la influencia de la curva de respuesta espectral relativa de un sensor multiespectral en los procesos de clasificación mediante operadores basados en estadística de primer orden. Analizar el parámetro intensidad registrado por sensores ALS para valorar métodos y procesos que permitan una mejor explotación y aprovechamiento. Estudiar la combinación de sensores multiespectrales y ALS estudiando su influencia en los procesos de clasificación de usos de suelo. La Tesis Doctoral aparece estructurada en 6 capítulos. El Capítulo 1, presenta el estado del arte en cuanto a sensores y procesos se refiere. En los siguientes capítulos se presentan y desarrollan los trabajos que fundamentan el núcleo de la presente Tesis. En este sentido, en el Capítulo 2 se presenta las implicaciones de trabajar en un marco de independencia con las bandas de un sensor multiespectral, presentando los beneficios de trabajar con técnicas basadas en el análisis de componentes independientes frente a técnicas más convencionales en Teledetección como el análisis de componentes principales. El Capitulo 3 presenta una metodología diseñada para mitigar los efectos del comportamiento solapado de las bandas de sensores multiespectrales en los procesos de clasificación mediante operadores de clasificación apoyados en estadística de primer orden. A lo largo del Capítulo 4 se estudia como poder aprovechar la información registrada por sensores ALS mediante técnicas de clasificación, estudiando métodos para la normalización de la variable intensidad y sus beneficios con la combinación de información multiespectral registrada por sensores aéreos. En el Capítulo 5 se aplica el concepto de independencia estudiado en el Capítulo 2 en la explotación combinada de sensores multiespectrales y ALS y sus repercusiones en los procesos de clasificación de imágenes. Por último, en el Capítulo 6 se presentan unas conclusiones generales sobre la viabilidad del uso combinado de sensores para la explotación de la información desde el punto temático

Unsupervised methods in multilingual and multimodal semantic modeling

In the first part of this project, independent component analysis has been applied to extract word clusters from two Farsi corpora. Both word-document and word-context matrices have been considered to extract such clusters. The application of ICA on the word-document matrices extracted from these two corpora led to the detection of syntagmatic word clusters, while the utilization of word-context matrix resulted in the extraction of both syntagmatic and paradigmatic word clusters. Furthermore, we have discussed some potential benefits of this automatically extracted thesaurus. In such a thesaurus, a word is defined by some other words without being connected to the outer physical objects. In order to fill such a gap, symbol grounding has been proposed by philosophers as a mechanism which might connect words to their physical referents. From their point of view, if words are properly connected to their referents, their meaning might be realized. Once this objective is achieved, a new promising horizon would open in the realm of artificial intelligence. In the second part of the project, we have offered a simple but novel method for grounding words based on the features coming from the visual modality. Firstly, indexical grounding is implemented. In this naïve symbol grounding method, a word is characterized using video indexes as its context. Secondly, such indexical word vectors have been normalized according to the features calculated for motion videos. This multimodal fusion has been referred to as the pattern grounding. In addition, the indexical word vectors have been normalized using some randomly generated data instead of the original motion features. This third case was called randomized grounding. These three cases of symbol grounding have been compared in terms of the performance of translation. Besides that, word clusters have been excerpted by comparing the vector distances and from the dendrograms generated using an agglomerative hierarchical clustering method. We have observed that pattern grounding exceled the indexical grounding in the translation of the motion annotated words, while randomized grounding has deteriorated the translation significantly. Moreover, pattern grounding culminated in the formation of clusters in which a word fit semantically to the other members, while using the indexical grounding, some of the closely related words dispersed into arbitrary clusters

Méthodes de séparation aveugle de sources et application à la télédétection spatiale

Cette thèse concerne la séparation aveugle de sources, qui consiste à estimer un ensemble de signaux sources inconnus à partir d'un ensemble de signaux observés qui sont des mélanges à paramètres inconnus de ces signaux sources. C'est dans ce cadre que le travail de recherche de cette thèse concerne le développement et l'utilisation de méthodes linéaires innovantes de séparation de sources pour des applications en imagerie de télédétection spatiale. Des méthodes de séparation de sources sont utilisées pour prétraiter une image multispectrale en vue d'une classification supervisée de ses pixels. Deux nouvelles méthodes hybrides non-supervisées, baptisées 2D-Corr-NLS et 2D-Corr-NMF, sont proposées pour l'extraction de cartes d'abondances à partir d'une image multispectrale contenant des pixels purs. Ces deux méthodes combinent l'analyse en composantes parcimonieuses, le clustering et les méthodes basées sur les contraintes de non-négativité. Une nouvelle méthode non-supervisée, baptisée 2D-VM, est proposée pour l'extraction de spectres à partir d'une image hyperspectrale contenant des pixels purs. Cette méthode est basée sur l'analyse en composantes parcimonieuses. Enfin, une nouvelle méthode est proposée pour l'extraction de spectres à partir d'une image hyperspectrale ne contenant pas de pixels purs, combinée avec une image multispectrale, de très haute résolution spatiale, contenant des pixels purs. Cette méthode est fondée sur la factorisation en matrices non-négatives couplée avec les moindres carrés non-négatifs. Comparées à des méthodes de la littérature, d'excellents résultats sont obtenus par les approches méthodologiques proposées.This thesis concerns the blind source separation problem, which consists in estimating a set of unknown source signals from a set of observed signals which are mixtures of these source signals, with unknown mixing coefficients. In this thesis, we develop and use innovative linear source separation methods for applications in remote sensing imagery. Source separation methods are used and applied in order to preprocess a multispectral image for a supervised classification of this image. Two new unsupervised methods, called 2D-Corr-NLS and 2D-Corr-NMF, are proposed in order to extract abundance maps from a multispectral image with pure pixels. These methods are based on sparse component analysis, clustering and non-negativity constraints. A new unsupervised method, called 2D-VM, is proposed in order to extract endmember spectra from a hyperspectral image with pure pixels. This method is based on sparse component analysis. Also, a new method is proposed for extracting endmember spectra from a hyperspectral image without pure pixels, combined with a very high spatial resolution multispectral image with pure pixels. This method is based on non-negative matrix factorization coupled with non-negative least squares. Compared to literature methods, excellent results are obtained by the proposed methodological approaches