Classification of the wildland-urban interface: A comparison of pixel- and object-based classifications using high-resolution aerial photography, Computers Environment and Urban Systems, 32(4): 317-326.Cleve, C.
Classification is the most important method to determine type of crop contained in a region for agricultural planning. There are two types of the classification. First is pixel based and the other is object based classification method. While pixel based classification methods are based on the inform...
This is succeeded by a review of object-based change detection techniques. Finally there is a brief discussion of spatial data mining techniques in image processing and change detection from remote sensing data. The merits and issues of different techniques are compared. The importance of the ...
Haq MA, Rahaman G, Baral P, Ghosh A (2021) Deep learning based supervised image classification using uav images for forest areas classification. J Indian Soc Remote Sens 49(3):601–606 Article Google Scholar Javed A, Jung S, Lee WH, Han Y (2020) Object-based building change detection...
5.3.3 Error-pixel based fusion vs. bagging A bagging result was obtained by training the detail branch by setting every pixel as erroneous and then averaging its result with the result from the initial DeepLabv3+ network, which leads to an mIoU of 79.38% (the 6th row in Table 3). ...
Bio:Priyanka Kochharhas been a data scientist for 10+ years. She now has her own deep learning consultancy and loves to work on interesting problems. She has helped several startups deploy innovative AI based solutions. If you have a project that she can collaborate on then please contact he...
PixelDatabase.Net is now live:https://pixeldatabase.netA Free Online Text Based Image Editor I am starting a batch version of this project now because I need it for videos. Please visit my YouTube channel, as I make videos for PixelDatabase.Net as often as I can:https://www.youtube...
In this work, we present a novel framework for unsupervised domain adaptation based on the notion of target-domain consistency training. Intuitively, our work is based on the idea that in order to perform well on the target domain, a model's output should...
The objective of the study is to evaluate the performance of pixel-based and object-oriented classifiers for rubber growth classification. At the first level, the general land cover was classified into eight land cover classes (soil, water body, rubber, mature oil palm, young oil palm, forest...
The block-based approach is noise-tolerant but is not accurate in edge localization while the pixel-based approach gives accurate edge localisation but is not noise-tolerant. We propose a new approach that combines both techniques and retains the advantages of each. The new method is evaluated ...