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Abstract:As an important application in remote sensing, landcover classification remains one of the most challenging tasks in very-high-resolution (VHR) image analysis. As the rapidly increasing number of Deep Learning (DL) based landcover methods and training strategies are claimed to be the state-of-the-art, the already fragmented technical landscape of landcover mapping methods has been further complicated. Although there exists a plethora of literature review work attempting to guide researchers in making an informed choice of landcover mapping methods, the articles either focus on the review of applications in a specific area or revolve around general deep learning models, which lack a systematic view of the ever advancing landcover mapping methods. In addition, issues related to training samples and model transferability have become more critical than ever in an era dominated by data-driven approaches, but these issues were addressed to a lesser extent in previous review articles regarding remote sensing classification. Therefore, in this paper, we present a systematic overview of existing methods by starting from learning methods and varying basic analysis units for landcover mapping tasks, to challenges and solutions on three aspects of scalability and transferability with a remote sensing classification focus including (1) sparsity and imbalance of data; (2) domain gaps across different geographical regions; and (3) multi-source and multi-view fusion. We discuss in detail each of these categorical methods and draw concluding remarks in these developments and recommend potential directions for the continued endeavor.
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From: Rongjun Qin [view email] [v1] Mon, 7 Feb 2022 16:38:40 UTC (1,193 KB)
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What is the highest resolution remote sensing?
Low resolution: over 30 m/pixel (e.g., NASA Terra & Aqua MODIS Satellites) Medium resolution: 5-30 m/pixel (e.g., USGS/NASA Landsat 8 Satellite) High resolution: 1-5 m/pixel (e.g., Planet Labs Rapid Eye Satellite) Very high resolution: <1m/pixel (e.g., Maxar's Worldview Satellites)
What is the resolution of remote sensing data?
In remote sensing we refer to three types of resolution: spatial, spectral and temporal. Spatial Resolution refers to the size of the smallest feature that can be detected by a satellite sensor or displayed in a satellite image. It is usually presented as a single value representing the length of one side of a square.
Is High spatial resolution good?
While higher spatial resolution gives finer details, they are not always necessary for accurate spatial analysis. In some cases, a medium or even low spatial resolution will do. Let's get into more depth about the various spatial resolutions of different types of satellites, their practical benefits, and limitations.
How reliable is remote sensing?
Our review shows that by using remote sensing, the spatial distribution of evapotranspiration can be mapped with an overall accuracy of 95 % (STD 5 %) and rainfall with an overall accuracy of 82 % (STD 15 %). sensors.