negative impacts of under-segmentation errors become significantly large at large scales and (2) there are both advantages and limitations in using object-based classification, and their trade-off determines the overall effect of object-based classification, which is dependent on the segmentation scales...
One aspect of the object-based classification workflow is the assignment of each image object to a habitat class on the basis of its spectral, textural, or geometric properties. While a skilled image interpreter can achieve this task accurately through manual editing, full or partial automation is...
The study aims to improve the classification and mapping of snow cover over the Himalayan region, which is essential for assessing water availability and understanding hydrological and climatic interactions. The normalized difference snow index (NDSI) is a traditional digital classification method for snow...
Geographic Object-Based Image Analysis (GEOBIA) is becoming more prevalent in remote sensing classification, especially for high-resolution imagery. Many supervised classification approaches are applied to objects rather than pixels, and several studies have been conducted to evaluate the performance of suc...
Second, for scenarios of targeted object classification, (i.e., most of an image is unclassified), an alternate strategy is utilized for reference-data acquisition. This involves acquiring comprehensive reference information for selected subsites to ensure proper estimates of commission....
Height data, in the form of nDSM, were the most important feature for greenhouse classification. The best overall accuracy values were close to 90%, and they were not improved by using multiangle orthoimages. 展开 关键词: object-based classification greenhouses GeoEye-1 WorldView-2 normalized ...
Graph-Based Object Classification for Neuromorphic Vision Sensing Summary NVS 可以显著提高采样率,同时显著提高能量效率和对光照变化的鲁棒性。然而 NVS 不使用帧表示图像,所以不能利用最先进的循环神经网络(CNNs)。我们提出了一个基于 NVS 的紧凑的图表示,并使用结合残差网络来做分类,这种残差 Graph CNN 保持了尖...
Object-based image classification for land-cover mapping purposes using remote-sensing imagery has attracted significant attention in recent years. Numerous studies conducted over the past decade have investigated a broad array of sensors, feature selection, classifiers, and other factors of interest. How...
Here, we propose a general Bayesian framework for within-object classification. Images are represented as a regular grid of non-overlapping patches. In training, these patches are approximated by a predefined library. In inference, the choice of approximating patch determines the classification decision...
Distance metric learning (DML) has been successfully applied to object classification, both in the standard regime of rich training data and in the few-shot scenario, where each category is represented by only a few examples. In this work, we propose a new method for DML that simultaneously ...