Mr. Dimo Dimov Profile

Dimo Dimov

at Geocledian GmbH

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SPIE Journal Paper | 31 May 2022

Classification of remote sensing time series and similarity metrics for crop type verification

Dimo Dimov

JARS, Vol. 16, Issue 02, 024519, (May 2022) https://doi.org/10.1117/12.10.1117/1.JRS.16.024519

KEYWORDS: Data modeling, Remote sensing, Classification systems, Agriculture, Image classification, Machine learning, Systems modeling, Vegetation, Statistical modeling, Sensors

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One of the greatest challenges in many machine learning applications is to build robust classification models that reduce calibration efforts, ensure model transferability to unseen data distributions, and handle high semantic resolution datasets with a relatively large number of target classes. We present an operational domain adaptation framework for crop type verification from remote sensing data for integrated administration and control and farm management systems. It is part of the ag|knowledge crop monitoring platform and demonstrates a more robust spatiotemporal model transferability than conventional supervised crop type classification approaches, which use multitemporal and multispectral features to classify the specific characteristics of crop phenology patterns. The proposed method is based on a machine learning model that has been trained on particular similarity metrics between all target crop types. The metrics are derived by quantifying the similarity of vegetation index time series between the observed parcel and the aggregated time series of reference objects for each parcel and crop type. This method achieves higher classification results than a model trained on the pure remote sensing time series data, as the algorithm does not learn the temporal vegetation index patterns, but instead, the adversarial characteristics and the differences between each crop type. Moreover, it also reduces dimensionality as the time series are summarized through the respective similarity metrics. We compare both approaches through different classification algorithms. Overall, the achieved classification accuracy for more than 67 crop types scores more than 80%.

SPIE Journal Paper | 14 June 2019 Open Access

Framework for agricultural performance assessment based on MODIS multitemporal data

Dimo Dimov, Fabian Löw, Johannes Uhl, Shavkat Kenjabaev, Olena Dubovyk, Mirzahayot Ibrakhimov, Chandrashekhar Biradar

JARS, Vol. 13, Issue 02, 025501, (June 2019) https://doi.org/10.1117/12.10.1117/1.JRS.13.025501

KEYWORDS: Agriculture, MODIS, Earth observing sensors, Landsat, Composites, Vegetation, Data modeling, Optical inspection, Visualization, Associative arrays

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We present a hierarchical classification framework for automated detection and mapping of spatial patterns of agricultural performance using satellite-based Earth observation data exemplified for the Aral Sea Basin (ASB) in Central Asia. The core element of the framework is the derivation of a composite agricultural performance index which is composed of different subindicators taking into account cropping intensity, crop diversity, crop rotations, fallow land frequency, land utilization, water use efficiency, and water availability. We derive these subindicators from net primary productivity and evapotranspiration data obtained from the MODIS sensor on board the Terra satellite during the observation period from 2000 to 2016, as well as from cropland maps created through multiannual classification of normalized difference vegetation index (NDVI). We classified pixel-based NDVI time series covering more than 8 × 106 ha of irrigated cropland based on a hierarchical approach concatenating unsupervised and supervised classification techniques to automatically generate and refine training labels, which are then used to train a decision fusion classifier, achieving an average overall accuracy of 78%. The results give unprecedented insights into spatial patterns of agricultural performance in the ASB. The proposed method is transferable and applicable for global-scale mapping, and the results of this remote sensing-aided assessment can provide important information for regional agricultural planning purposes.

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