Abstract
In recent years, hyperspectral remote sensing (HRS) has become common practice for remote analyses of the physiognomy and composition of forests. Supervised classification is often used for this purpose, but demands intensive sampling and analyses, whereas unsupervised classification often requires information retrieval out of the large HRS datasets, thereby not realizing the full potential of the technology. An improved principal component analysis-based classification (PCABC) scheme is presented and intended to provide accurate and sequential image-based unsupervised classification of Mediterranean forest species. In this study, unsupervised classification and reduction of data size are performed simultaneously by applying binary sequential thresholding to principal components, each time on a spatially reduced subscene that includes the entire spectral range. The methodology was tested on HRS data acquired by the airborne AisaFENIX HRS sensor over a Mediterranean forest in Mount Horshan, Israel. A comprehensive field-validation survey was performed, sampling 257 randomly selected individual plants. The PCABC provided highly improved results compared to the traditional unsupervised classification methodologies, reaching an overall accuracy of 91%. The presented approach may contribute to improved monitoring, management, and conservation of Mediterranean and similar forests.
Highlights
A wide range of environmental and ecological studies, including studies of forest and vegetation landscapes, require reliable information on land cover at local to global scales [1,2]
A Mediterranean forest in Mount Horshan, Israel was used as the test site for the methodology applied to data from Specim’s airborne AisaFENIX hyperspectral remote sensing (HRS) sensor, and this study reports on the outcome of the approach
The rationale underlying the principal component analysis-based classification (PCABC) is that exclusion of dominant endmembers from the dataset may allow detection of new dominant endmembers in the remaining spatial subset, which retains all spectral bands. Reiterating this process in a sequential manner may result in a detailed classification of the dataset to most of its endmembers. This was done in the iteration, as illustrated in Figure 4, which presents a subscene of principal component analysis (PCA) first iteration 1, component 1 (Figure 4a) next to a subscene of the second iteration performed on the reflectance image including all 293 bands, and masking out only non-vegetated pixels (Figure 4b)
Summary
A wide range of environmental and ecological studies, including studies of forest and vegetation landscapes, require reliable information on land cover at local to global scales [1,2]. Unsupervised classifications, on the other hand, utilize statistical and numerical processes to outline groups of pixels with similar spectral features in the image without any preliminary sampling or training data [26,27,49,52] This type of classification results in a thematic map of spectral clusters which must later be identified as classes and validated by the analyst in the field [26,27]. The presented methodology provides better classification of the vegetation using the advantages of PCA for unsupervised variability detection of HRS data, while overcoming the known disadvantages: (a) low sensitivity to subtle differences in the target reference, (b) inability to detect small classes, and (c) a reduction of spectral data (bands) during the process. Areas outside the forest area such as agricultural fields have been excluded manually from the image as they do not include Mediterranean plant species
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