Hyperspectral tree crown classification using the multiple instance adaptive cosine estimator

Zou, Sheng; Gader, Paul D.; Zare, Alina

doi:10.7287/peerj.preprints.27052v1

Cited by 1 publication

(3 citation statements)

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“…We had the most participants in this task (six): BRG (Sumsion et al, 2018), Conor (McMahon, 2018), FEM (Dalponte, Frizzera & Gianelle, 2018), GatorSense (Zou, Gader & Zare, 2018), StanforCCB (Anderson, 2018), and our baseline system (Fig. 6).…”

Section: Resultsmentioning

confidence: 99%

“…FEM applied a four step pipeline, consisting of data normalization, sequential forward floating feature selection, building of a support vector machine classifier, and crown level aggregation by majority rule (Dalponte, Frizzera & Gianelle, 2018). The GatorSense group built a series of one-vs-one applied multiple instance adaptive cosine estimator classifiers (Zare, Jiao & Glenn, 2017; Zou, Gader & Zare, 2018) that automatically select the best subset of pixels to use for classification. Crown level probabilities were assigned by majority vote of pixel scale predictions.…”

Section: Methodsmentioning

confidence: 99%

“…We present the details of the tasks and data, and summarize and synthesize the results from the participants. In a set of short accompanying papers and preprints the participants describe the methods used (Table 1), present detailed results for those methods, and discuss lessons learned and future directions for these methods (Anderson, 2018; Dalponte, Frizzera & Gianelle, 2018; Taylor, 2018; McMahon, 2018; Sumsion et al, 2018; Zou, Gader & Zare, 2018). Finally, we discuss the broad potential for competitions in ecology and the biological sciences more generally.…”

Section: Introductionmentioning

confidence: 99%

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A data science challenge for converting airborne remote sensing data into ecological information

Marconi

Graves

Gong

et al. 2019

PeerJ

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Ecology has reached the point where data science competitions, in which multiple groups solve the same problem using the same data by different methods, will be productive for advancing quantitative methods for tasks such as species identification from remote sensing images. We ran a competition to help improve three tasks that are central to converting images into information on individual trees: (1) crown segmentation, for identifying the location and size of individual trees; (2) alignment, to match ground truthed trees with remote sensing; and (3) species classification of individual trees. Six teams (composed of 16 individual participants) submitted predictions for one or more tasks. The crown segmentation task proved to be the most challenging, with the highest-performing algorithm yielding only 34% overlap between remotely sensed crowns and the ground truthed trees. However, most algorithms performed better on large trees. For the alignment task, an algorithm based on minimizing the difference, in terms of both position and tree size, between ground truthed and remotely sensed crowns yielded a perfect alignment. In hindsight, this task was over simplified by only including targeted trees instead of all possible remotely sensed crowns. Several algorithms performed well for species classification, with the highest-performing algorithm correctly classifying 92% of individuals and performing well on both common and rare species. Comparisons of results across algorithms provided a number of insights for improving the overall accuracy in extracting ecological information from remote sensing. Our experience suggests that this kind of competition can benefit methods development in ecology and biology more broadly.

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