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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">geomgou</journal-id><journal-title-group><journal-title xml:lang="ru">Географическая среда и живые системы</journal-title><trans-title-group xml:lang="en"><trans-title>Geographical Environment and Living Systems</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2712-7613</issn><issn pub-type="epub">2712-7621</issn><publisher><publisher-name>Московский государственный областной университет</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.18384/2310-7189-2018-4-39-49</article-id><article-id custom-type="elpub" pub-id-type="custom">geomgou-130</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>РАЗДЕЛ I. ГЕОГРАФИЧЕСКИЕ НАУКИ</subject></subj-group></article-categories><title-group><article-title>СПЕКТРАЛЬНЫЕ ХАРАКТЕРИСТИКИ РЕЛЬЕФА СУШИ В ЗАДАЧАХ ЕГО АВТОМАТИЗИРОВАННОЙ КЛАССИФИКАЦИИ (НА ПРИМЕРЕ ЮЖНОЙ АМЕРИКИ)</article-title><trans-title-group xml:lang="en"><trans-title>THE SPECTRAL LAND FORM SIGNATURES FOR AUTOMATIC TERRAIN CLASSIFICATION (ON THE EXAMPLE OF SOUTH AMERICA)</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Харченко</surname><given-names>Сергей Владимирович</given-names></name><name name-style="western" xml:lang="en"><surname>Kharchenko</surname><given-names>Sergey V.</given-names></name></name-alternatives><email xlink:type="simple">xar4enkkoff@yandex.ru</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Казаков</surname><given-names>Станислав Геннадьевич</given-names></name><name name-style="western" xml:lang="en"><surname>Kazakov</surname><given-names>Stanislav G.</given-names></name></name-alternatives><email xlink:type="simple">kazaks@rambler.ru</email><xref ref-type="aff" rid="aff-2"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>Московский государственный университет им. М.В. Ломоносова; Институт географии Российской академии наук</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Lomonosov Moscow State University; Institute of Geography, Russian Academy of Sciences</institution><country>Russian Federation</country></aff></aff-alternatives><aff-alternatives id="aff-2"><aff xml:lang="ru"><institution>Курский государственный университет</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Kursk State University</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2018</year></pub-date><pub-date pub-type="epub"><day>04</day><month>05</month><year>2022</year></pub-date><volume>0</volume><issue>4</issue><fpage>39</fpage><lpage>49</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Харченко С.В., Казаков С.Г., 2022</copyright-statement><copyright-year>2022</copyright-year><copyright-holder xml:lang="ru">Харченко С.В., Казаков С.Г.</copyright-holder><copyright-holder xml:lang="en">Kharchenko S.V., Kazakov S.G.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://www.geoecosreda.ru/jour/article/view/130">https://www.geoecosreda.ru/jour/article/view/130</self-uri><abstract><p>Описан подход к расчету некоторых спектральных характеристик рельефа (далее - СХР). Примером для демонстрации выбрана территория Южной Америки. СХР потенциально могут использоваться для классификации рельефа по характеру периодичности его расчленения, а затем и для картографирования. В мелком масштабе созданы цифровые модели спектральных характеристик рельефа: амплитуда важнейшей гармоники, значимость этой гармоники, генеральное направление колебаний поля высот, значимость этого направления, длина волны важнейшей гармоники. Распределение некоторых из характеристик в значительной степени коррелирует с традиционными морфометрическими величинами, однако, связь между ними далеко не функциональная, другие характеристики полностью самостоятельны. Создана нейронная сеть, разделившая всю территорию материка на 225 отдельных нейронов сети, а они, в свою очередь, иерархической кластеризацией разделены в 4 более общих группы. Каждая из групп выделена по характерному для ряда нейронов сочетанию пяти спектральных характеристик рельефа. Получена схема кластеров рельефа территории этого материка по характеру периодичности структуры его расчленения.</p></abstract><trans-abstract xml:lang="en"><p>The way for computing some spectral landform’s characteristics (SLCs) is described on the example of the territory of the South America. SLCs can be potentially used for terrain classification with regard to the topographic dissection character, and then for a terrain thematic mapping. Five digital models in the small scale are designed: maximum of the wave’s magnitudes, the importance of the given share of waves, the general direction of height fluctuations, the severity of this direction, and the general wavelength. The distributions of some characteristics are largely correlated with the canonical geomorphometric variables; however, this relation is far from functional, other SLCs being completely independent. The Kohonen neural network dividing the South America territory into 225 separate neurons is constructed. The neurons with the hierarchical clustering are combined into 4 more general groups. Each group is defined by a typical combination of five SLCs. The scheme of the South America terrain clusters which reflect the types of topographic dissection is constructed.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>рельеф земной поверхности</kwd><kwd>дискретное преобразование Фурье</kwd><kwd>спектральные характеристики</kwd><kwd>Южная Америка</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Ласточкин А.Н., Одесский И.А. Гармонический анализ гипсометрических профилей с целью выявления волнообразных деформаций // Геоморфология. 1970. № 2. С. 78-88.</mixed-citation><mixed-citation xml:lang="en">Ласточкин А.Н., Одесский И.А. Гармонический анализ гипсометрических профилей с целью выявления волнообразных деформаций // Геоморфология. 1970. № 2. 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