%0 Journal Article %1 noauthororeditor %A S.Nivetha1, %A and Dr.R.Jensi2, %D 2019 %J Advances in Engineering: an International Journal (ADEIJ) %K Change detection heterogeneous image. images mapping remote sensing %N 3 %P 01-13 %T REGION CLASSIFICATION AND CHANGE DETECTION USING LANSAT-8 IMAGES %U https://airccse.com/adeij/papers/2319adeij01.pdf %V 2 %X The change detection in remote sensing images remains an important and open problem for damage assessment. A new change detection method for LANSAT-8 images based on homogeneous pixel transformation (HPT) is proposed. Homogeneous Pixel Transformation transfers one image from its original feature space (e.g., gray space) to another feature space (e.g., spectral space) in pixel-level to make the pre-event images and post-event images to be represented in a common space or projection space for the convenience of change detection. HPT consists of two operations, i.e., forward transformation and backward transformation. In the forward transformation, each pixel of pre-event image in the first feature space is taken and will estimate its mapping pixel in the second space corresponding to post-event image based on the known unchanged pixels. A multi-value estimation method with the noise tolerance is produced to determine the mapping pixel using K-nearest neighbours technique. Once the mapping pixels of pre-event image are identified, the difference values between the mapping image and the post-event image can be directly generated. Then the similar work is done for backward transformation to combine the post-event image with the first space, and one more difference value for each pixel will be generated. Then, the two difference values are taken and combined to improve the robustness of detection with respect to the noise and heterogeneousness of images. (FRFCM) Fast and Robust Fuzzy C-means clustering algorithm is employed to divide the integrated difference values into two clusters- changed pixels and unchanged pixels. This detection results may contain few noisy regions as small error detections, and a spatial-neighbor based noise filter is developed to reduce the false alarms and missing detections. The experiments for change detection with real images of LANSAT-8 in Tuticorin between 2013-2019 are given to validate the percentage of the changed regions in the proposed method.

@article{noauthororeditor, abstract = {The change detection in remote sensing images remains an important and open problem for damage assessment. A new change detection method for LANSAT-8 images based on homogeneous pixel transformation (HPT) is proposed. Homogeneous Pixel Transformation transfers one image from its original feature space (e.g., gray space) to another feature space (e.g., spectral space) in pixel-level to make the pre-event images and post-event images to be represented in a common space or projection space for the convenience of change detection. HPT consists of two operations, i.e., forward transformation and backward transformation. In the forward transformation, each pixel of pre-event image in the first feature space is taken and will estimate its mapping pixel in the second space corresponding to post-event image based on the known unchanged pixels. A multi-value estimation method with the noise tolerance is produced to determine the mapping pixel using K-nearest neighbours technique. Once the mapping pixels of pre-event image are identified, the difference values between the mapping image and the post-event image can be directly generated. Then the similar work is done for backward transformation to combine the post-event image with the first space, and one more difference value for each pixel will be generated. Then, the two difference values are taken and combined to improve the robustness of detection with respect to the noise and heterogeneousness of images. (FRFCM) Fast and Robust Fuzzy C-means clustering algorithm is employed to divide the integrated difference values into two clusters- changed pixels and unchanged pixels. This detection results may contain few noisy regions as small error detections, and a spatial-neighbor based noise filter is developed to reduce the false alarms and missing detections. The experiments for change detection with real images of LANSAT-8 in Tuticorin between 2013-2019 are given to validate the percentage of the changed regions in the proposed method.}, added-at = {2019-06-03T09:13:54.000+0200}, author = {S.Nivetha1 and and Dr.R.Jensi2}, biburl = {https://www.bibsonomy.org/bibtex/26d6780ec9d1100a3fec9dbe6d18a9212/adeij_journal}, interhash = {e7567883af4434aa53fdd2467516416d}, intrahash = {6d6780ec9d1100a3fec9dbe6d18a9212}, journal = {Advances in Engineering: an International Journal (ADEIJ)}, keywords = {Change detection heterogeneous image. images mapping remote sensing}, language = {English}, month = may, number = 3, pages = {01-13}, timestamp = {2019-06-03T09:13:54.000+0200}, title = {REGION CLASSIFICATION AND CHANGE DETECTION USING LANSAT-8 IMAGES}, url = {https://airccse.com/adeij/papers/2319adeij01.pdf}, volume = 2, year = 2019 }