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Showing results for tags 'classification'.
Found 8 results
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Dear all I'm new in remote sensing and I have problems when try to extract built-up area from an image or during image classification. I am using landsat 7 ETM+ and TM images, when I use supervised or un-supervised methods for image classification, urban area (built-up area) are not recognizable from bare-soil. I read several papers about built-up Indexes and tried some of them but there is no tangible improvement in results. Please guide me as step by step process. Regards
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Hello everyone, I just want to share a new software of point cloud processing: LiDAR360. This software has the ability to visualize large point cloud data and automatically align flight strips, classify points and generate spatial products. Main Functions: Strips alignment Automatic and semi-automatic point cloud classification(ground, buildings, vegetation, powerline, tower and so on) Forest analysis(including forest metrics calculation, tree segmentation) Powerline analysis(including power line segmentation based on towers, danger points detection, report generation automatically) Image processing(Stitching images together) 3D model generation from images For more information, you can contact : Website: www.greenvalleyintl.com Google Group: https://groups.google.com/forum/?hl=zh-cn#!forum/lidar360 -
Hello, I'm seeking for a solution to extract/classify all grassland-areas from satellite images or orthophotos for a whole country (Switzerland). - Which methods are feasible (any papers to recommend?)? - Which dataset would be the best concerning spatial resolution and spectral resolution? - I do have Orthophotos (NIR, R, G, B ) with 25cm spatial res. Are there any suggestings to work with? Would be very pleased for any advice or paper suggestion! Thanks a lot.
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I am using Erdas Imagine 2015 software to perform an unsupervised classification of a Landsat 8 img file. When using the Isodata method is there any parameters I can adapt such as standard deviation, convergence threshold etc that are a standard to produce the most accurate results for a land cover assessment rather than the default settings. As recoding and masking individual classes is a lengthy and complicated process. Thanks
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Hi guys! I`m working on land cover change detection project of Kyiv province in Ukraine using Landsat data and ancillary information. I want to find change in Land Cover from 1990 to 2014 of Kyiv province using Decision Tree approach in ENVI. I have already done radiometric correction, atmospheric correction and apply cloud mask to whole Landsat`s scenes, and now I have question and request. Do I need to do mosaicking before classification or mosaicking classified image ? Does anybody have some decision rules for splitting pixels for appropriate land cover classes using Landsat data, DEM, VI? Thanks
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Hi, I'm trying to perform some wetland supervised classification, and I am getting quite a bit of spectral confusion, with overlapping and incorrect classes. I am using ERDAS 2014 and Landsat 8 imagery. When evaluating the training classes, some of the bands show quite distinct and separate histograms, while in other bands the histograms interfere with each other. Is there a way to only use some of the bands to classify certain training sites? The only way I have seen so far is to create a subset image without certain bands. However, that requires a whole new training signature file to be made. Is there a quicker way? Thanks
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Hi, I'm using Idrisi Selva to do an unsupervised (ISOCLUST) classification of Landsat TM images. My study area is at the juncture of two Landsat scenes and it spans two UTM zones. I radiometrically corrected each band of each image in R, then mosaicked each band in ArcGIS v10, then clipped the mosaicked bands to the boundary of my study area. I exported the data to ASCII, then imported three bands (R, NIR, MIR) to Idrisi Selva. Using ISOCLUST, I specified that I wanted 10 classes with a minimum class size of 30 pixels. The problem is that every few minutes, I would get an error that said "Variance/covariance matrix is singular. Check for perfectly correlated variables or variables with zero variance." This message was immediately followed by: "Inverse matrix process failed." I found that if I sat there and clicked through the errors when they popped up every few minutes, the classification worked (at least from a quick visual assessment). My questions are: 1- Why is this error occurring? 2- How is the variance/covariance matrix generated? 3- Did the unsupervised classification work in spite of the error messages? Any help would be really appreciated.
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Hello everybody! I am trying to make a relatively simple LULC classification of a region with only 5 categories - grassland, agricultural, built-up, forest and other. I have the data for summer seasons. I tried with several RGB combinations, PCA, band ratios from every possible tutorial, still I can't differentiate barren land (in this case "agricultural" category") and built-up. Is there any algorithm or some procedure which would select some of the possible good combinations? I am working with ArcGIS 9.3 and am very frustrated. Also, when I tried with several procedures, I did it parallel on 1991 and 2011 datasets, LANDSAT same bands and same month of the year, unsupervised classification, I got completely different results, some classes were impossible to distinguish on one dataset, and were separated in the other. Thank you in advance.
