Effectiveness of SID as Spectral Similarity Measure to Develop Crop Spectra from Hyperspectral Image
| Autor: | B. Krishna Mohan, Hasmukh J. Chauhan |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Kullback–Leibler divergence
010504 meteorology & atmospheric sciences Correlation coefficient Pixel Contextual image classification Physics::Instrumentation and Detectors business.industry Geography Planning and Development Multispectral image 0211 other engineering and technologies Hyperspectral imaging Pattern recognition 02 engineering and technology 01 natural sciences Measure (mathematics) Spectral line Earth and Planetary Sciences (miscellaneous) Artificial intelligence business 021101 geological & geomatics engineering 0105 earth and related environmental sciences Mathematics |
| Zdroj: | Journal of the Indian Society of Remote Sensing. 46:1853-1862 |
| ISSN: | 0974-3006 0255-660X |
| DOI: | 10.1007/s12524-018-0845-4 |
| Popis: | The present study was undertaken with the objective to check effectiveness of spectral information divergence (SID) to develop spectra from image for crop classes based on spectral similarity with field spectra. In multispectral and hyperspectral remote sensing, classification of pixels is obtained by statistical comparison (by means of spectral similarity) of known field or library spectra to unknown image spectra. Though these algorithms are readily used, little emphasis has been placed on use of various spectral similarity measures to develop crop spectra from the image itself. Hence, in this study methodology suggested to develop spectra for crops based on SID. Absorption features are unique and distinct; hence, validation of the developed spectra is carried out using absorption features by comparing it with field spectra and finding average correlation coefficient r = 0.982 and computed SID equivalent r = 0.989. Effectiveness of developed spectra for image classification was computed by probability of spectral discrimination (PSD) and resulted in higher probability for the spectra developed based on SID. Image classification was carried out using field spectra and spectra assigned by SID. Overall classification accuracy of the image classified by field spectra is 78.30% and for the image classified by spectra assigned through SID-based approach is 91.82%. Z test shows that image classification carried out using spectra developed by SID is better than classification carried out using field spectra and significantly different. Validation by absorption features, effectiveness by PSD and higher classification accuracy show possibility of new approach for spectra development based on SID spectral similarity measure. |
| Databáze: | OpenAIRE |
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