Classification of Forest Land Information Using Environment Satellite (HJ-1) Data
Reads0
Chats0
TLDR
The experiment proved that there were good vector results on HJ-1A remote sensing image in the view of visual judgment, and extracted deferent forest land by the overall accuracy 87% with the supports by those variables' distribution knowledge, such as conifer, mixed forest, broadleaf, shrubby.Abstract:
For researching properties of HJ-1A CCD camera multi- spectral data in performance on extraction of land features information, this paper selected the east area of NiLeke forest farm in the western Tianshan mountain as the study area, and analyzed different accuracies for HJ-1A CCD data in identifying forest land categories using various classification methods. Firstly, maximum- likelihood classifier, Mahalanobis distance classifier, minimum distance classifier and K-means classifier were used to category land use types with two different scales on HJ-1A CCD1 and Landsat5 TM images, and analyzed separately with confusion matrix. Secondly, forest land types were distinguished by texture information and the smallest polygon size using K-NN method based on clustering algorithm. The comparing results show: at first, different classification system have different accuracy. In the first land use classification system, the accuracy of HJ-1A CCD1 images are lower than TM images, but higher in the second land use classification system. Secondly, accuracy result of maximum- likelihood classification is the best method to classify land use types. In the first land use classification system, TM total accuracy is up to 85.1% and Kappa coefficient is 0.8. In the second land use classification system, the result is up to 85.4% and kappa coefficient is 0.74.Thirdly, judgment both from the view of visual interpretation and quantitative accuracy testes, non-supervised method with K- means classifier has low qualities where many land features have characters of scattered distribution and small different spectrum information. Finally, the experiment proved that there were good vector results on HJ-1A remote sensing image in the view of visual judgment, and extracted deferent forest land by the overall accuracy 87% with the supports by those variables' distribution knowledge, such as conifer, mixed forest, broadleaf, shrubby. Index Terms—HJ-1A CCD1 data, image classification, different scale, land use features extractionread more
Citations
More filters
Journal ArticleDOI
Fast transient-based detection of busbar faults employing improved morphological gradient
TL;DR: The proposed algorithm has high security and dependability in discrimination of external and internal faults with the ultra-fast operation and in the case of the existing two busbars in a substation layout, the proposed indices can discriminate between the faults of the two bus zones.
Posted Content
Image Fusion Technologies In Commercial Remote Sensing Packages
TL;DR: A state-of-art of multi-sensor image fusion technologies as well as review on the quality evaluation of the single image or fused images in the commercial remote sensing packages are provided.
Journal Article
Image fusion technologies in commercial remote sensing packages
TL;DR: In this paper, the authors provide a state-of-the-art of multi-sensor image fusion technologies as well as review on the quality evaluation of the single image or fused images in the commercial remote sensing packages.
References
More filters
Book
Remote Sensing Digital Image Analysis: An Introduction
TL;DR: In this paper, the authors present an introduction to quantitative evaluation of satellite and aircraft derived from remotely retrieved data, without detailed mathematical treatment of computer based algorithms, but in a manner conductive to an understanding of their capabilities and limitations.
BookDOI
Spatial statistics for remote sensing
TL;DR: In this article, Gorte and van der Meer describe the basic elements of statistics for image classification, including spatial prediction by linear kriging and spectral unmixing.
Journal Article
Ecological scale and scaling
TL;DR: The principles and applications of ecological scale will be improved through the deepening and advancing of the understanding and skills.
Proceedings ArticleDOI
Improved minimum distance classification with Gaussian outlier detection for industrial inspection
TL;DR: This work describes a method for off-line outlier detection, which cleans the training data set and yields substantially better classification results, and two improved versions of the minimum distance classifier, which both yield higher rates of correct classification with practically no speed-loss.