Subpixel Target Enhancement in Hyperspectral Images

Hyperspectral images due to their higher spectral resolution are increasingly being used for various remote sensing applications including information extraction at subpixel level. Typically whenever an object gets spectrally resolved but not spatially, mixed pixels in the images result. Numerous man made and/or natural disparatetar gets may thus occur inside such mixed pixels giving rise to subpixel target detection problem. Various spectral unmixing models such as linear mixture modeling (LMM) are in vogue to recover components of a mixed pixel. Spectral unmixing outputs both the endmember spectrum and their corresponding a bundance fractions inside the pixel. It, however, does not provide spatial distribution of these abundance fractions within a pixel. This limits the applicability of hyperspectral data for subpixel target detection. In this paper, a new inverse Euclidean distance based super-resolution mapping method has been presented. In this method, the subpixel target detection is performed by adjusting spatial distribution of abundance fraction within a pixel of an hyperspectral image. Results obtainedat different resolutions indicate that super-resolution mapping may effectively be utilized in enhancing the target detection at sub-pixel level.Defence Science Journal, 2013, 63(1), pp.63-68, DOI:http://dx.doi.org/10.14429/dsj.63.376

Application of Fuzzy-Neural Network in Classification of Soils using Ground-penetrating Radar Imagery

Errors associated with visual inspection and interpretation of radargrams often inhibits the intensive surveying of widespread areas using ground-penetrating radar (GPR). To automate the interpretive process, this paper presents an application of a fuzzy-neural network (F-NN) classifier for unsupervised clustering and classification of soil profile using GPR imagery. The classifier clusters and classifies soil profiles strips along a traverse based on common pattern similarities that can relate to physical features of the soil (e.g., number of horizons; depth, texture and structure of the horizons; and relative arrangement of the horizons, etc). This paper illustrates this classification procedure by its application on GPR data, both simulated and actual real-world. Results show that the procedure is able to classify the profile into zones that corresponded with those obtained by visual inspection and interpretation of radargrams. Results indicate that an F-NN model can supply real-time soil profile clustering and classification during field surveys

Application of Fuzzy-Neural Network in Classification of Soils using Ground-penetrating Radar Imagery

Errors associated with visual inspection and interpretation of radargrams often inhibits the intensive surveying of widespread areas using ground-penetrating radar (GPR). To automate the interpretive process, this paper presents an application of a fuzzy-neural network (F-NN) classifier for unsupervised clustering and classification of soil profile using GPR imagery. The classifier clusters and classifies soil profiles strips along a traverse based on common pattern similarities that can relate to physical features of the soil (e.g., number of horizons; depth, texture and structure of the horizons; and relative arrangement of the horizons, etc). This paper illustrates this classification procedure by its application on GPR data, both simulated and actual real-world. Results show that the procedure is able to classify the profile into zones that corresponded with those obtained by visual inspection and interpretation of radargrams. Results indicate that an F-NN model can supply real-time soil profile clustering and classification during field surveys

استفاده از مدل زیر پیکسل جاذبه به منظور افزایش قدرت تفکیک مکانی مدل رقومی ارتفاع (DEM)

افزایش قدرت تفکیک مکانی به منظور افزایش میزان اطلاعات در مدل رقومی ارتفاع (DEM) از جمله مهمترین موضوعات در ژئومورفولوژی کمی محسوب می‌شود. تاکنون مدل‌های مختلفی به منظور افزایش قدرت تفکیک مکانی ارائه شده است که از بین مدل‌ها، مدل جاذبه به عنوان جدیدترین مدل، دارای دقت بسیار بالایی می‌باشد. این مدل برای اولین بار به منظور افزایش قدرت تفکیک مکانی بر روی تصاویر ماهواره‌ای استفاده شده است. در این تحقیق از مدل جاذبه برای اولین بار به منظور افزایش قدرت تفکیک مکانی DEM استفاده شد. در بررسی حاضر، از دو مدل همسایگی پیکسل‌های مماس (Touching) و مدل همسایگی چهارگانه (Quadrant) به منظور تخمین مقادیر زیر پیکسل ها استفاده گردید. در مدل جاذبه احتیاجی به کالیبره کردن و آموزش الگوریتم همانند الگوریتم‌های یادگیری ماشین نیست، این امر موجب می‌شود که زمان محاسبات برای اجرای الگوریتم کم شود. پس از تولید تصاویر خروجی برای زیر پیکسل‌ها، در مقیاس های 2، 3 و4 با همسایگی‌های متفاوت، بهترین مقیاس با مناسب‌ترین نوع همسایگی با استفاده از نقاط کنترل زمینی تعیین شد و مقادیر RMSE برای آن‌ها محاسبه شد. تعداد کل نقاط کنترل زمین مستخرج از عملیات نقشه برداری، 2118 نقطه بود. مقدار RMSE برای هر DEM به صورت جداگانه محاسبه شد. نتایج نشان داد که با استفاده از مدل جاذبه صحت تصاویر خروجی بهبود بخشیده شده و همچنین قدرت تفکیک مکانی آن‌ها نیز افزایش پیدا کرده است. بر اساس نتایج از بین مقیاس‌ها با همسایگی‌های مختلف، مقیاس 3 و مدل همسایگی چهارگانه نسبت به سایر روش‌ها دارای بیشترین دقت با کمترین میزان RMSE (54/5) برای DEM 30 متر و DEM  90 متر (13/9) می‌باشد

Reducing the impacts of intra-class spectral variability on the accuracy of soft classification and super-resolution mapping of shoreline

The main objective of this research is to assess the impact of intra-class spectral variation on the accuracy of soft classification and super-resolution mapping. The accuracy of both analyses was negatively related to the degree of intra-class spectral variation, but the effect could be reduced through use of spectral sub-classes. The latter is illustrated in mapping the shoreline at a sub-pixel scale from Landsat ETM+ data. Reducing the degree of intra-class spectral variation increased the accuracy of soft classification, with the correlation between predicted and actual class coverage rising from 0.87 to 0.94, and super-resolution mapping, with the RMSE in shoreline location decreasing from 41.13 m to 35.22 m