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The fusion of hyperspectral and optical datasets performed the best, with accuracy values increased from 0.64 to 0.96-0.97 when using a training map produced by unsupervised clustering and Mapcurves labeling for both CNN models. We also perform accuracy assessments of the developed data products and evaluate varying CNN architectures.
#Network radar similar windows Patch
We compare two CNN approaches: (1) breaking up the images into small patches (e.g., 6 × 6) and predict the vegetation class for entire patch and (2) semantic segmentation and predict the vegetation class for every pixel. We develop a convolutional neural network (CNN) approach for developing high resolution vegetation maps for our study region in Arctic. We fist applied a quantitative goodness-of-fit method, called Mapcurves, that shows the degree of spatial concordance between the public coarse resolution maps and k-means clustering values and relabels the k values based on the best overlap. We seek to evaluate the integration of hyper-spectral, multi-spectral, radar, and terrain datasets using unsupervised and supervised classification techniques over a ~343.72 km 2 area for generating vegetation classifications at a variety of resolutions (5 m and 12.5 m). We focus detailed analysis and validation study around the Kougarok river, located in the central Seward Peninsula of Alaska. This study proposes a new remote sensing based multi-sensor data fusion approach more » for developing high-resolution maps of vegetation in the Seward Peninsula, Alaska. Remote sensing-based approaches for mapping vegetation, while promising, are challenging in high latitude environments due to frequent cloud cover, polar darkness, and limited availability of high-resolution remote sensing datasets (e.g., ~ 5 m). However, most existing Arctic vegetation maps are at a coarse resolution and with a varying degree of detail and accuracy. Department of Energy's Next Generation Ecosystem Experiment (NGEE) Arctic. = ,Īccurate and high-resolution maps of vegetation are critical for projects seeking to understand the terrestrial ecosystem processes and land-atmosphere interactions in Arctic ecosystems, such as U.S. Performance after convergence is seen to be comparable to that obtained with a supervised ML classifier, while maintaining the advantages of an unsupervised technique. It is shown that this algorithm converges, more » and significantly improves classification accuracy. To overcome this poor accuracy, an iterative algorithm is proposed where the SAR image is reclassified using Maximum Likelihood (ML) classifier.
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Results show that one neural network method-Learning Vector Quantization (LVQ)-outperforms the conventional unsupervised classifiers, but is still inferior to supervised methods. Several types of unsupervised neural networks are first applied to the classification of SAR images, and the results are compared to those of more conventional unsupervised methods. In this paper, a new terrain classification technique is introduced to determine terrain classes in polarimetric SAR images, utilizing unsupervised neural networks to provide automatic classification, and employing an iterative algorithm to improve the performance.
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In contrast, unsupervised methods determine classes automatically, but generally show limited ability to accurately divide terrain into natural classes. Supervised methods have yielded higher accuracy than unsupervised techniques, but suffer from the need for human interaction to determine classes and training regions. A number of methods have been developed to classify ground terrain types from fully polarimetric synthetic aperture radar (SAR) images, and these techniques are often grouped into supervised and unsupervised approaches. Classification of terrain cover using polarimetric radar is an area of considerable current interest and research.