Natural habitats are exposed to growing pressure due to intensification of land use and tourism development. Thus, obtaining information on the vegetation is necessary for conservation and management projects. In this context, remote sensing is an important tool for monitoring and managing habitats, being classification a crucial stage. The majority of image classifications techniques are based upon the pixel-based approach. An alternative is the object-based (OBIA) approach, in which a previous segmentation step merges image pixels to create objects that are then classified. Besides, improved results may be gained by incorporating additional spatial information and specific spectral indices into the classification process. The main goal of this work was to implement and assess object-based classification techniques on very-high resolution imagery incorporating spectral indices and contextual spatial information in the classification models. The study area was Teide National Park in Canary Islands (Spain) using Worldview-2 orthoready imagery. In the classification model, two common indices were selected Normalized Difference Vegetation Index (NDVI) and Optimized Soil Adjusted Vegetation Index (OSAVI), as well as two specific Worldview-2 sensor indices, Worldview Vegetation Index and Worldview Soil Index. To include the contextual information, Grey Level Co-occurrence Matrices (GLCM) were used. The classification was performed training a Support Vector Machine with sufficient and representative number of vegetation samples (Spartocytisus supranubius, Pterocephalus lasiospermus, Descurainia bourgaeana and Pinus canariensis) as well as urban, road and bare soil classes. Confusion Matrices were computed to evaluate the results from each classification model obtaining the highest overall accuracy (90.07%) combining both Worldview indices with the GLCM-dissimilarity.
The hyperspectral imagery is formed by a several narrow and continuous bands covering different regions of the electromagnetic spectrum, such as spectral bands of the visible, near infrared and far infrared. Hyperspectral imagery provides extremely higher spectral resolution than high spatial resolution multispectral imagery, improving the detection capability of terrestrial objects. The greatest difficulty found in the hyperspectral processing is the high dimensionality of these data, which brings out the 'Hughes' phenomenon. This phenomenon specifies that the size of training set required for a given classification increases exponentially with the number of spectral bands. Therefore, the dimensionality of the hyperspectral data is an important drawback when applying traditional classification or pattern recognition approaches to this hyperspectral imagery. In our context, the dimensionality reduction is necessary to obtain accurate thematic maps of natural protected areas. Dimensionality reduction can be divided into the feature-selection algorithms and featureextraction algorithms. We focus the study in the feature-extraction algorithms like Principal Component Analysis (PCA), Minimum Noise Fraction (MNF) and Independent Component Analysis (ICA). After a review of the state-of-art, it has been observed a lack of a comparative study on the techniques used in the hyperspectral imagery dimensionality reduction. In this context, our objective was to perform a comparative study of the traditional techniques of dimensionality reduction (PCA, MNF and ICA) to evaluate their performance in the classification of high spatial resolution imagery of the CASI (Compact Airborne Spectrographic Imager) sensor.
In last decades, there have been a decline in natural resources, becoming important to develop reliable methodologies for their management. The appearance of very high resolution sensors has offered a practical and cost-effective means for a good environmental management. In this context, improvements are needed for obtaining higher quality of the information available in order to get reliable classified images. Thus, pansharpening enhances the spatial resolution of the multispectral band by incorporating information from the panchromatic image. The main goal in the study is to implement pixel and object-based classification techniques applied to the fused imagery using different pansharpening algorithms and the evaluation of thematic maps generated that serve to obtain accurate information for the conservation of natural resources. A vulnerable heterogenic ecosystem from Canary Islands (Spain) was chosen, Teide National Park, and Worldview-2 high resolution imagery was employed. The classes considered of interest were set by the National Park conservation managers. 7 pansharpening techniques (GS, FIHS, HCS, MTF based, Wavelet ‘à trous’ and Weighted Wavelet ‘à trous’ through Fractal Dimension Maps) were chosen in order to improve the data quality with the goal to analyze the vegetation classes. Next, different classification algorithms were applied at pixel-based and object-based approach, moreover, an accuracy assessment of the different thematic maps obtained were performed. The highest classification accuracy was obtained applying Support Vector Machine classifier at object-based approach in the Weighted Wavelet ‘à trous’ through Fractal Dimension Maps fused image. Finally, highlight the difficulty of the classification in Teide ecosystem due to the heterogeneity and the small size of the species. Thus, it is important to obtain accurate thematic maps for further studies in the management and conservation of natural resources.
Both climate change and anthropogenic pressure impacts are producing a declining in ecosystem natural resources. In this work, a vulnerable coastal ecosystem, Maspalomas Natural Reserve (Canary Islands, Spain), is analyzed. The development of advanced image processing techniques, applied to new satellites with very high resolution sensors (VHR), are essential to obtain accurate and systematic information about such natural areas. Thus, remote sensing offers a practical and cost-effective means for a good environmental management although some improvements are needed by the application of pansharpening techniques. A preliminary assessment was performed selecting classical and new algorithms that could achieve good performance with WorldView-2 imagery. Moreover, different quality indices were used in order to asses which pansharpening technique gives a better fused image. A total of 7 pansharpening algorithms were analyzed using 6 spectral and spatial quality indices. The quality assessment was implemented for the whole set of multispectral bands and for those bands covered by the wavelength range of the panchromatic image and outside of it. After an extensive evaluation, the most suitable algorithm was the Weighted Wavelet <i>‘à trous’</i> through Fractal Dimension Maps technique which provided the best compromise between the spectral and spatial quality for the image. Finally, Quality Map Analysis was performed in order to study the fusion in each band at local level. As conclusion, novel analysis has been conducted covering the evaluation of fusion methods in shallow water areas. Hence, the excellent results provided by this study have been applied to the generation of challenging thematic maps of coastal and dunes protected areas.