Spectral Signatures Of Pixels Research Articles

Deep learning framework based on Spectral and Spatial properties for Land-Cover Classification using Landsat Hyperspectral Images

Hyperspectral Imaging is used to monitor the earth on basis of spectral continuous data ranges starting from visible to short wave infrared region of the electromagnetic spectrum. It enables the detailed identification and classification of minerals and land cover on basis of with improved spectral and spatial resolutions provides the opportunity to obtain accurate land-cover classification. Several challenges have been generated due to Hughes phenomenon (curse of dimensionality) and Quantification of land cover in urban area. In order to alleviate those problems, novel framework named as Deep learning framework on spectral and spatial properties on Landsat image has been proposed which composed of several techniques. Initially hyperspectral (HS) data exploitation model on identification of pure spectral signatures (endmembers) and their corresponding fractional abundances in each pixel of the HS data cube has been proposed. Feature reduction strategy based on principle component analysis has been employed to generate reduced dimensionality of the features on retaining the most useful information. The reduced features have been taken for the spectral analysis and spatial analysis using Multiobjective Discrete Spectral and Spatial optimized representation model through encompassing the sparse and low-rank structure on the spectral signature of pixels. Identification and mapping of the land cover classification categorized as agriculture area and bare land has been identified using spectral indices (end members). The spectral indices calculation provides the type of land cover on the pixel purity index and it classifies based on the spectral and spatial value using N finder algorithm. N finder Algorithm is a change vector analysis. Further Ensemble based method has been proposed in addition to generate diverse classification results and the discrete high correlation classifier method which can enhance the accuracy and diversity of a single classifier simultaneously. Finally an efficient agriculture land cover spectral evolution mapping has been proposed using Multivariate principle component analysis. It is considered as change detection method explores efficiently the context of images, which leads to a good tradeoff between wider receptive field and the use of Context towards mapping Agriculture Land cover spectral evolution. It computes the spectral correlation between two images on spectral similarity. It predicts the accurately on temporal changes of the earth surfaces. Experimental analysis has been carried out using Landsat-8 dataset to evaluating the performance of the proposed representative framework using available spectral indices against the state of art approaches. Proposed framework achieves accuracy of 99% on reflectance value against the different wavelength which superior with other existing classification approaches.

Multiobjective Discrete Spectral and Spatial Optimized Representation for Land-Cover Classification using Landsat Hyperspectral Images

The availability of hyperspectral images with improved spectral and spatial resolutions provides the opportunity to obtain accurate land-cover classification. . The changes in land cover largely affect the terrestrial ecosystem, thus information on land cover is important for understanding the ecological environment. Quantification of land cover in urban area is challenging due to their diversified activities and large spatial and temporal variations. In order to improve urban land cover classification and mapping, a novel framework named as Multiobjective Discrete Spectral and Spatial optimized representation for end member extraction has been proposed in this paper. It is considered as hyperspectral (HS) data exploitation model on identification of pure spectral signatures (endmembers) and their corresponding fractional abundances in each pixel of the HS data cube. High dimensionality of the data leads to computational complexity as it represents the Hughes phenomenon. Feature reduction strategy based on principle component analysis has been employed to generate reduced dimensionality of the features on retaining the most useful information. The reduced features have been taken for the spectral analysis and spatial analysis using Multiobjective Discrete Spectral and Spatial optimized representation model through encompassing the sparse and low-rank structure on the spectral signature of pixels. Identification and mapping of the land cover classification categorized as agriculture area and bare land has been identified using spectral indices (end members). The spectral indices calculation provides the type of land cover on the pixel purity index and it classifies based on the spectral and spatial value using N finder algorithm. N finder Algorithm is a change vector analysis. Experimental analysis has been carried out using Landsat-8 dataset to evaluating the performance of the proposed representative framework using available spectral indices against the state of art approaches. Proposed framework achieves accuracy of 99% on reflectance value against the different wavelength which superior with other existing classification approaches.

Kelp-bed dynamics across scales: Enhancing mapping capability with remote sensing and GIS

Kelp are important drivers of productivity and biodiversity patterns in cold-water and nutrient-rich rocky reefs. Scuba- and boat-based methods are routinely used to study submerged kelp beds. However, these time-consuming and labor-intensive methods enable monitoring of beds or the factors and processes that control their distribution over only small spatial (few 100s of m2) and temporal (<5 years) scales. Remote sensing and geographic information system (GIS) technologies are increasingly used to compare marine species distribution over multiple spatiotemporal scales. However, there is currently no clear framework and limited demonstration of their potential for studies of broad-scale changes in completely submerged kelp beds. The present study aims to establish the foundation of a simple, accessible, and robust set of remote sensing and GIS-based methods to address key questions about the stability of subtidal kelp beds across multiple spatial and temporal scales. It tests the suitability of conventional image classification methods for mapping kelp from digital aerial (acquired on board a helicopter) and satellite (SPOT 7) imagery of ~250 ha of seabed around four islands in the Mingan Archipelago (northern Gulf of St. Lawrence, Canada). Three classification methods are compared: 1) a software-led unsupervised classification in which pixels are grouped into clusters based on similarity in spectral signature among pixels; 2) a software-led supervised classification in which pixels are assigned to categories based on similarity in the spectral signature of pixels and that of reference data from each category; and 3) a visual classification carried out by a trained observer. Supervised classification of satellite imagery and visual classification of aerial imagery were the top methods to map kelp, with overall accuracies of 89% and 90%, respectively. Unsupervised classification of both types of imagery showed poor discrimination between kelp and non-kelp benthic classes. Kelp bed edges were more difficult to identify on satellite than aerial imagery because the former presented poorer contrasts and a lower spatial resolution. Kelp bed edges identified with visual classification appeared artificially jagged for both types of imagery, mainly because of the coarse (225-m2) spatial units used for this classification. Kelp bed edges were smoother on maps created with the unsupervised and supervised classifications, which used 1-m-pixel images. The present study demonstrates that conventional remote sensing and GIS methods can accurately map submerged kelp beds over large spatial domains in the Mingan Archipelago or in other benthic systems with similar oceanic conditions and a largely dichotomous (kelp-barrens) biological makeup.

Scale Object Selection (SOS) through a hierarchical segmentation by a multi-spectral per-pixel classification

In high resolution multispectral optical data, the spatial detail of the images are generally smaller than the dimensions of objects, and often the spectral signature of pixels is not directly representative of classes we are interested in. Thus, taking into account the relations between groups of pixels becomes increasingly important, making object-oriented approaches preferable. In this work several scales of detail within an image are considered through a hierarchical segmentation approach, while the spectral information content of each pixel is accounted by a per-pixel classification. The selection of the most suitable spatial scale for each class is obtained by merging the hierarchical segmentation and the per-pixel classification through the Scale Object Selection (SOS) algorithm. The SOS algorithm starts processing data from the highest level of the hierarchical segmentation, which has the least amount of spatial detail, down to the last segmentation map. At each segmentation level, objects are assigned to a specific class whenever the percentage of pixels belonging to the latter, according to a pixel-based procedure, exceeds a predefined threshold, thereby automatically selecting the most appropriate spatial scale for the classification of each object. We apply our method to multispectral, panchromatic and pan-sharpened QuickBird images, considering two different test cases: a region on the Etna volcano (Italy), imaged at a 2.4m resolution, and an area close to the town of Balakot (Pakistan), imaged at a 0.6m resolution. On both test-cases the proposed approach enhanced the classification accuracy with respect to the single-segmentation per-pixel classification approach. A detailed analysis of the benefits achieved using the hierarchical segmentation with respect to a single segmentation is reported.

Exploiting Sparsity in Hyperspectral Image Classification via Graphical Models

A significant recent advance in hyperspectral image (HSI) classification relies on the observation that the spectral signature of a pixel can be represented by a sparse linear combination of training spectra from an overcomplete dictionary. A spatiospectral notion of sparsity is further captured by developing a joint sparsity model, wherein spectral signatures of pixels in a local spatial neighborhood (of the pixel of interest) are constrained to be represented by a common collection of training spectra, albeit with different weights. A challenging open problem is to effectively capture the class conditional correlations between these multiple sparse representations corresponding to different pixels in the spatial neighborhood. We propose a probabilistic graphical model framework to explicitly mine the conditional dependences between these distinct sparse features. Our graphical models are synthesized using simple tree structures which can be discriminatively learnt (even with limited training samples) for classification. Experiments on benchmark HSI data sets reveal significant improvements over existing approaches in classification rates as well as robustness to choice of training.

Identification of Mangrove Areas by Remote Sensing: The ROC Curve Technique Applied to the Northwestern Mexico Coastal Zone Using Landsat Imagery

In remote sensing, traditional methodologies for image classification consider the spectral values of a pixel in different image bands. More recently, classification methods have used neighboring pixels to provide more information. In the present study, we used these more advanced techniques to discriminate between mangrove and non‑mangrove regions in the Gulf of California of northwestern Mexico. A maximum likelihood algorithm was used to obtain a spectral distance map of the vegetation signature characteristic of mangrove areas. Receiver operating characteristic (ROC) curve analysis was applied to this map to improve classification. Two classification thresholds were set to determine mangrove and non-mangrove areas, and two performance statistics (sensitivity and specificity) were calculated to express the uncertainty (errors of omission and commission) associated with the two maps. The surface area of the mangrove category obtained by maximum likelihood classification was slightly higher than that obtained from the land cover map generated by the ROC curve, but with the difference of these areas to have a high level of accuracy in the prediction of the model. This suggests a considerable degree of uncertainty in the spectral signatures of pixels that distinguish mangrove forest from other land cover categories.

HYPERSPECTRAL REMOTE SENSING FOR MINERAL MAPPING: A CASE-STUDY AT ALTO PARAÍSO DE GOÍAS, CENTRAL BRAZIL

After many decades of successful advances in applying multispectral remote sensing to the study of the Earth's characteristics and processes, a new data acquisition technology is emerging: hyperspectral remote sensing. This technology comprises the remote measurement of specific chemical and physical properties of surface materials through imaging spectroscopy. In this paper we examine the capability of hyperspectral remote sensing data acquired by the AVIRIS airborne instrument, over an area near Alto Paraiso de Goias. This area was chosen as a function of data availability, and not due to its geological characteristics. It comprises Proterozoic metasediments with little mineralogical variations and, although it does not represent an ideal site for the use of hyperspectral data, some interesting results were found in terms of mineral identification in portions of the image where enough ground exposure existed. Mineral assemblages, comprising mixtures of primary and secondary minerals, the latter related to weathering processes, were identified in the imagery, based solely on spectral signatures of pixels. These mixtures comprised mainly hematite, goethite, halloysite, kaolinite and Na-montmorillonite. Results were confirmed by laboratory reflectance measurements and scanning electron microscope (SEM) analysis of soil samples, demonstrating the usefulness of AVIRIS data and the efficiency of the methods employed for remote mineral mapping. However, vegetation showed to represent a constraint for mineral mapping, since it is likely to conceal spectral information due to rocks and minerals.

HYPERSPECTRAL REMOTE SENSING FOR MINERAL MAPPING: A CASE-STUDY AT ALTO PARAÍSO DE GOÍAS, CENTRAL BRAZIL

After many decades of successful advances in applying multispectral remote sensing to the study of the Earth's characteristics and processes, a new data acquisition technology is emerging: hyperspectral remote sensing. This technology comprises the remote measurement of specific chemical and physical properties of surface materials through imaging spectroscopy. In this paper we examine the capability of hyperspectral remote sensing data acquired by the AVIRIS airborne instrument, over an area near Alto Paraíso de Goías. This area was chosen as a function of data availability, and not due to its geological characteristics. It comprises Proterozoic metasediments with little mineralogical variations and, although it does not represent an ideal site for the use of hyperspectral data, some interesting results were found in terms of mineral identification in portions of the image where enough ground exposure existed. Mineral assemblages, comprising mixtures of primary and secondary minerals, the latter related to weathering processes, were identified in the imagery, based solely on spectral signatures of pixels. These mixtures comprised mainly hematite, goethite, halloysite, kaolinite and Na-montmorillonite. Results were confirmed by laboratory reflectance measurements and scanning electron microscope (SEM) analysis of soil samples, demonstrating the usefulness of AVIRIS data and the efficiency of the methods employed for remote mineral mapping. However, vegetation showed to represent a constraint for mineral mapping, since it is likely to conceal spectral information due to rocks and minerals.