Pixel Purity Index Algorithm and N-Dimensional Visualization For ETM+ Image Analysis: A Case of District Vehari

The hyperspectral image analysis technique one of the most advanced remote sensing tools has been used as a possible means of identifying from a single pixel or in the field of view of the sensor An important problem in hyperspectral image processing is to decompose the mixed pixels into the information that contribute to the pixel endmember and a set of corresponding fractions of the spectral signature in the pixel abundances and this problem is known as un-mixing The effectiveness of the hyperspectral image analysis technique used in this study lies in their ability to compare a pixel spectrum with the spectra of known pure vegetation extracted from the spectral endmember selection procedures including the reflectance calibration of Landsat ETM image using ENVI software minimum noise fraction MNF pixel purity index PPI and n-dimensional visualization The Endmember extraction is one of the most fundamental and crucial tasks in hyperspectral data exploitation an ultimate goal of an endmember extraction algorithm is to find the purest form of spectrally distinct resource information of a scene The endmember extraction tendency to the type of endmembers being derived and the number of endmembers estimated by an algorithm with respect to the number of spectral bands and the number of pixels being processed also the required input data and the kind of noise if any in the signal model surveying done Results of the present study using the hyperspectral image analysis technique ascertain that Landsat ETM data can be used to generate valuable vegetative information for the District Vehari Punjab Province Pakista

INDEXING HYPERSPECTRAL IMAGE USING MORPHOLOGICAL NEURAL NETWORKS

En este artículo se explica el procedimiento para indexar imágenes dereconocimiento remoto utilizando información espectral y espacial. Para obtener características espectrales se aplican redes neuronales morfológicas, obteniendo el conjunto de endmembers de la imagen. Inicialmente se presenta una revisión de conceptos relativos a redes neuronales morfológicas de tipo memorias asociativas. Después se muestran los resultados de segmentación aplicado a un conjunto de imágenes sintéticas. Dichos resultados sirven de apoyo para esta aproximación como caracterización de las imágenes para su uso en la construcción de sistemas CBIR de imágenes hiperespectrales.This paper explains how to index remote sensing images using spectral andspatial information. To obtain spectral features it apply morphological neural network, obtaining the set of endmembers of the image. Initially it presents a review of concepts of morphological associative memories. Following are the results of segmentation of the images compared to some other approaches to calculating the endmember spectra. These results contribute to support this approach as a characterization of images for use in the construction of hyperspectral imaging CBIR system

Key Information Retrieval in Hyperspectral Imagery through Spatial-Spectral Data Fusion

Hyperspectral (HS) imaging is measuring the radiance of materials within each pixel area at a large number of contiguous spectral wavelength bands. The key spatial information such as small targets and border lines are hard to be precisely detected from HS data due to the technological constraints. Therefore, the need for image processing techniques is an important field of research in HS remote sensing. A novel semisupervised spatial-spectral data fusion method for resolution enhancement of HS images through maximizing the spatial correlation of the endmembers (signature of pure or purest materials in the scene) using a superresolution mapping (SRM) technique is proposed in this paper. The method adopts a linear mixture model and a fully constrained least squares spectral unmixing algorithm to obtain the endmember abundances (fractional images) of HS images. Then, the extracted endmember distribution maps are fused with the spatial information using a spatial-spectral correlation maximizing model and a learning-based SRM technique to exploit the subpixel level data. The obtained results validate the reliability of the technique for key information retrieval. The proposed method is very efficient and is low in terms of computational cost which makes it favorable for real-time applications

MODELO LINEAR DE MISTURA ESPECTRAL EM IMAGEM DE MODERADA RESOLUÇÃO ESPACIAL

The concept of spectral mixture offers a wide range of applications in theRemote Sensing area. The application of this concept, however, requires theprior estimation of the component’s (endmembers) spectral response. Thislatter requirement can be achieved by different methods, as reported in theliterature, such as techniques for the detection of pure pixels, use of spectrallibraries, and field radiometric measurements. Among those, the most oftenused is the pure pixel approach. In this approach, the components’ spectralreflectances are estimated by means of pixels covered entirely by a singlecomponent. This approach offers the advantage of allowing the extraction ofthe required spectral reflectance directly from the image data. This approach,however, becomes increasingly unfeasible as the spatial resolution of theimage data decreases, due to the larger ground area covered by a single pixel.In this study we propose a methodology to estimate the spectral reflectance foreach component class in moderate spatial resolution image data, by applyingthe linear mixing model (MLME), and higher spatial resolution image data asauxiliary data. It is expected that this methodology will provide a morepractical way to implement the spectral mixture approach to moderateresolution image data, allowing in this way the expansion of the informationabout the components’ proportions across larger areas, up-scaling informationin regional and global studies. Experiments were carried out using CCD (20m ground resolution) and IRMSS (80 m ground resolution) and WFI (260 mground resolution) CBERS-2 image data, as medium and moderate spatialresolution data, respectively. The spectral reflectances for the components inthe IRMSS and WFI CBERS-2 spectral bands are estimated by applying theproposed methodology. The reliability of the proposed methodology wasassessed by both analyzing scatter plots for CBERS-2 data and by comparingthe fraction images produced by image data sets of the sensors analyzed.O conceito de mistura espectral apresenta várias aplicações na área desensoriamento remoto. Esta abordagem requer, entretanto, o conhecimento apriori da resposta espectral dos membros de referência. Existem, na literatura,diferentes propostas para estimar esta resposta, como por exemplo, o uso depixels puros, o uso de bibliotecas espectrais e a realização de medidasradiométricas de campo. Na prática, a abordagem via pixel puro tem sido amais comumente empregada, por utilizar dados disponíveis na própriaimagem. Esta abordagem vai, entretanto tornando-se gradativamenteimpraticável, na medida em que a resolução espacial dos dados decresce,devido às dimensões da área coberta no terreno por cada pixel. Como soluçãoa este problema, é proposta neste estudo uma metodologia para fins deestimação de refletâncias espectrais em dados de imagens de moderadaresolução espacial, empregando o modelo linear de mistura espectral (MLME)e dados de imagens de resolução espacial média, na qualidade de dadosauxiliares. Objetiva-se desta forma facilitar a utilização das técnicas demistura espectral em estudos regionais, nos quais imagens de moderadaresolução espacial são freqüentemente as mais adequadas. A metodologia proposta foi testada utilizando-se dados dos sensores CCD (20 m) e IRMSS(80 m) e WFI (260 m) a bordo da plataforma CBERS-2, na qualidade dedados de média e moderada resolução espacial, respectivamente. Asrefletâncias espectrais para as classes membros de referência foram estimadaspara as bandas espectrais do IRMSS e WFI CBERS-2 por meio dametodologia proposta. A confiabilidade da abordagem proposta foi avaliadapor meio de diagramas de espalhamento para os dados CBERS-2 e tambémpela comparação entre as imagens-fração, produzidas a partir dos conjuntos dedados de imagem dos sensores analisados

Comparison Between Fractional Vegetation Cover Retrievals from Vegetation Indices and Spectral Mixture Analysis: Case Study of PROBA/CHRIS Data Over an Agricultural Area

In this paper we compare two different methodologies for Fractional Vegetation Cover (FVC) retrieval from Compact High Resolution Imaging Spectrometer (CHRIS) data onboard the European Space Agency (ESA) Project for On-Board Autonomy (PROBA) platform. The first methodology is based on empirical approaches using Vegetation Indices (VIs), in particular the Normalized Difference Vegetation Index (NDVI) and the Variable Atmospherically Resistant Index (VARI). The second methodology is based on the Spectral Mixture Analysis (SMA) technique, in which a Linear Spectral Unmixing model has been considered in order to retrieve the abundance of the different constituent materials within pixel elements, called Endmembers (EMs). These EMs were extracted from the image using three different methods: i) manual extraction using a land cover map, ii) Pixel Purity Index (PPI) and iii) Automated Morphological Endmember Extraction (AMEE). The different methodologies for FVC retrieval were applied to one PROBA/CHRIS image acquired over an agricultural area in Spain, and they were calibrated and tested against in situ measurements of FVC estimated with hemispherical photographs. The results obtained from VIs show that VARI correlates better with FVC than NDVI does, with standard errors of estimation of less than 8% in the case of VARI and less than 13% in the case of NDVI when calibrated using the in situ measurements. The results obtained from the SMA-LSU technique show Root Mean Square Errors (RMSE) below 12% when EMs are extracted from the AMEE method and around 9% when extracted from the PPI method. A RMSE value below 9% was obtained for manual extraction of EMs using a land cover use map

Thermal Infrared Remote Sensing for Analysis of Landscape Ecological Processes: Current Insights and Trends

NASA or NOAA Earth-observing satellites are not the only space-based TIR platforms. The European Space Agency (ESA), the Chinese, and other countries have in orbit or plan to launch TIR remote sensing systems. Satellite remote sensing provides an excellent opportunity to study land-atmosphere energy exchanges at the regional scale. A predominant application of TIR data has been in inferring evaporation, evapotranspiration (ET), and soil moisture. In addition to using TIR data for ET and soil moisture analysis over vegetated surfaces, there is also a need for using these data for assessment of drought conditions. The concept of ecological thermodynamics provides a quantification of surface energy fluxes for landscape characterization in relation to the overall amount of energy input and output from specific land cover types