CLPM: A Hybrid Network With Cross-Space Learning and Perception-Driven Mechanism for Long-Tailed Remote Sensing Image Classification Article Swipe
Lei Zhang
,
Min Jung Kong
,
Changfeng Jing
,
Xing Xing
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2025.3575292
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2025.3575292
Long-tailed distribution is a common issue in remote sensing image classification (RSIC), and many datasets suffer from severe class imbalance. This imbalance often causes the classifier to focus on the head classes with more samples, neglecting the tail classes. As a result, the precision of the tail classes is reduced, which in turn affects the generalization ability of the classifier. To address this problem, a hybrid network based on cross-space learning and perception-driven mechanism (CLPM) is proposed to improve the classification accuracy of samples from the tail classes. The CLPM network consists of three components. The cross-space representation learning branch is designed to enhance the representation capability of tail-class samples by integrating multiscale and multiregion spatial features. In parallel, the adaptive perception classification branch dynamically adjusts the receptive fields to improve generalization across different resolutions and challenging scenarios. In addition, the CLPM innovatively applies the von Mises-Fisher (vMF) distribution to remote sensing images for high-dimensional interclass feature modeling. Building on this, a vMF-based contrastive loss function is proposed. This approach effectively coordinates the learning processes of head and tail classes while enhancing the precision of feature representation. The effectiveness of CLPM is validated on datasets with varying balance ratios, including SIRI-WHU, CLRS, and NWPU-RESISC45. Results show that CLPM significantly improves tail classes accuracy while maintaining high recognition rates for head and middle classes. Compared with the existing methods, CLPM has significant advantages in the overall recognition accuracy, the long-tailed problem, and diversity adaptation.
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https://doi.org/10.1109/jstars.2025.3575292Digital Object Identifier
- Title
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CLPM: A Hybrid Network With Cross-Space Learning and Perception-Driven Mechanism for Long-Tailed Remote Sensing Image ClassificationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
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2025-01-01Full publication date if available
- Authors
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Lei Zhang, Min Jung Kong, Changfeng Jing, Xing XingList of authors in order
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https://doi.org/10.1109/jstars.2025.3575292Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/jstars.2025.3575292Direct OA link when available
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Mechanism (biology), Computer science, Perception, Space (punctuation), Artificial intelligence, Remote sensing, Pattern recognition (psychology), Geography, Epistemology, Neuroscience, Operating system, Philosophy, BiologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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| referenced_works | https://openalex.org/W4402643477, https://openalex.org/W4404520625, https://openalex.org/W3022140654, https://openalex.org/W3088162569, https://openalex.org/W3081684346, https://openalex.org/W4401841981, https://openalex.org/W4403443212, https://openalex.org/W2793638091, https://openalex.org/W4319786822, https://openalex.org/W4391579028, https://openalex.org/W4402613259, https://openalex.org/W4402613307, https://openalex.org/W2088889013, https://openalex.org/W4393950954, https://openalex.org/W4366352743, https://openalex.org/W2962749812, https://openalex.org/W4281492582, https://openalex.org/W2074888575, https://openalex.org/W2767106145, https://openalex.org/W2867270703, https://openalex.org/W6760201928, https://openalex.org/W2962876782, https://openalex.org/W3158299003, https://openalex.org/W2954996726, https://openalex.org/W4309605561, https://openalex.org/W2148143831, https://openalex.org/W2963691377, https://openalex.org/W2884561390, https://openalex.org/W4404520710, https://openalex.org/W4312344855, https://openalex.org/W4385577551, https://openalex.org/W2771838132, https://openalex.org/W2899095986, https://openalex.org/W2955547856, https://openalex.org/W3148270135, https://openalex.org/W3200466256, https://openalex.org/W4295832403, https://openalex.org/W2074909897, https://openalex.org/W4310588385, https://openalex.org/W2962984188, https://openalex.org/W3034654297, https://openalex.org/W3204988419, https://openalex.org/W4386913491, https://openalex.org/W3034711780, https://openalex.org/W3096688134, https://openalex.org/W3177230409, https://openalex.org/W4285123352, https://openalex.org/W4318617442, https://openalex.org/W3176307736, https://openalex.org/W3148105697, https://openalex.org/W6682124274, https://openalex.org/W4399731493, https://openalex.org/W4385938120, https://openalex.org/W2994992990, https://openalex.org/W4313827544, https://openalex.org/W4318767332, https://openalex.org/W4392114214, https://openalex.org/W2922509574, https://openalex.org/W6776700526, https://openalex.org/W2283168383, https://openalex.org/W2347115704, https://openalex.org/W3006792692, https://openalex.org/W2592962403, https://openalex.org/W4312244159, https://openalex.org/W4214718285, https://openalex.org/W4377235500, https://openalex.org/W3202232857, https://openalex.org/W6803641788, https://openalex.org/W6764733053, https://openalex.org/W4312986563, https://openalex.org/W4400229943, https://openalex.org/W4390873567, https://openalex.org/W4392122189, https://openalex.org/W6637373629, https://openalex.org/W2097117768, https://openalex.org/W2963163009, https://openalex.org/W2194775991, https://openalex.org/W2145001205, https://openalex.org/W1686810756 |
| referenced_works_count | 79 |
| abstract_inverted_index.a | 3, 40, 64, 161 |
| abstract_inverted_index.As | 39 |
| abstract_inverted_index.In | 117, 138 |
| abstract_inverted_index.To | 60 |
| abstract_inverted_index.by | 110 |
| abstract_inverted_index.in | 6, 51, 232 |
| abstract_inverted_index.is | 2, 48, 75, 100, 166, 191 |
| abstract_inverted_index.of | 44, 57, 82, 92, 107, 175, 184, 189 |
| abstract_inverted_index.on | 28, 68, 159, 193 |
| abstract_inverted_index.to | 26, 77, 102, 129, 149 |
| abstract_inverted_index.The | 88, 95, 187 |
| abstract_inverted_index.and | 12, 71, 113, 135, 177, 202, 220, 240 |
| abstract_inverted_index.for | 153, 218 |
| abstract_inverted_index.has | 229 |
| abstract_inverted_index.the | 24, 29, 36, 42, 45, 54, 58, 79, 85, 104, 119, 126, 140, 144, 172, 182, 225, 233, 237 |
| abstract_inverted_index.von | 145 |
| abstract_inverted_index.CLPM | 89, 141, 190, 207, 228 |
| abstract_inverted_index.This | 20, 168 |
| abstract_inverted_index.from | 16, 84 |
| abstract_inverted_index.head | 30, 176, 219 |
| abstract_inverted_index.high | 215 |
| abstract_inverted_index.loss | 164 |
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| abstract_inverted_index.more | 33 |
| abstract_inverted_index.show | 205 |
| abstract_inverted_index.tail | 37, 46, 86, 178, 210 |
| abstract_inverted_index.that | 206 |
| abstract_inverted_index.this | 62 |
| abstract_inverted_index.turn | 52 |
| abstract_inverted_index.with | 32, 195, 224 |
| abstract_inverted_index.(vMF) | 147 |
| abstract_inverted_index.CLRS, | 201 |
| abstract_inverted_index.based | 67 |
| abstract_inverted_index.class | 18 |
| abstract_inverted_index.focus | 27 |
| abstract_inverted_index.image | 9 |
| abstract_inverted_index.issue | 5 |
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| abstract_inverted_index.this, | 160 |
| abstract_inverted_index.three | 93 |
| abstract_inverted_index.which | 50 |
| abstract_inverted_index.while | 180, 213 |
| abstract_inverted_index.(CLPM) | 74 |
| abstract_inverted_index.across | 132 |
| abstract_inverted_index.branch | 99, 123 |
| abstract_inverted_index.causes | 23 |
| abstract_inverted_index.common | 4 |
| abstract_inverted_index.fields | 128 |
| abstract_inverted_index.hybrid | 65 |
| abstract_inverted_index.images | 152 |
| abstract_inverted_index.middle | 221 |
| abstract_inverted_index.remote | 7, 150 |
| abstract_inverted_index.severe | 17 |
| abstract_inverted_index.suffer | 15 |
| abstract_inverted_index.(RSIC), | 11 |
| abstract_inverted_index.Results | 204 |
| abstract_inverted_index.ability | 56 |
| abstract_inverted_index.address | 61 |
| abstract_inverted_index.adjusts | 125 |
| abstract_inverted_index.affects | 53 |
| abstract_inverted_index.applies | 143 |
| abstract_inverted_index.balance | 197 |
| abstract_inverted_index.classes | 31, 47, 179, 211 |
| abstract_inverted_index.enhance | 103 |
| abstract_inverted_index.feature | 156, 185 |
| abstract_inverted_index.improve | 78, 130 |
| abstract_inverted_index.network | 66, 90 |
| abstract_inverted_index.overall | 234 |
| abstract_inverted_index.ratios, | 198 |
| abstract_inverted_index.result, | 41 |
| abstract_inverted_index.samples | 83, 109 |
| abstract_inverted_index.sensing | 8, 151 |
| abstract_inverted_index.spatial | 115 |
| abstract_inverted_index.varying | 196 |
| abstract_inverted_index.Building | 158 |
| abstract_inverted_index.Compared | 223 |
| abstract_inverted_index.accuracy | 81, 212 |
| abstract_inverted_index.adaptive | 120 |
| abstract_inverted_index.approach | 169 |
| abstract_inverted_index.classes. | 38, 87, 222 |
| abstract_inverted_index.consists | 91 |
| abstract_inverted_index.datasets | 14, 194 |
| abstract_inverted_index.designed | 101 |
| abstract_inverted_index.existing | 226 |
| abstract_inverted_index.function | 165 |
| abstract_inverted_index.improves | 209 |
| abstract_inverted_index.learning | 70, 98, 173 |
| abstract_inverted_index.methods, | 227 |
| abstract_inverted_index.problem, | 63, 239 |
| abstract_inverted_index.proposed | 76 |
| abstract_inverted_index.reduced, | 49 |
| abstract_inverted_index.samples, | 34 |
| abstract_inverted_index.SIRI-WHU, | 200 |
| abstract_inverted_index.accuracy, | 236 |
| abstract_inverted_index.addition, | 139 |
| abstract_inverted_index.different | 133 |
| abstract_inverted_index.diversity | 241 |
| abstract_inverted_index.enhancing | 181 |
| abstract_inverted_index.features. | 116 |
| abstract_inverted_index.imbalance | 21 |
| abstract_inverted_index.including | 199 |
| abstract_inverted_index.mechanism | 73 |
| abstract_inverted_index.modeling. | 157 |
| abstract_inverted_index.parallel, | 118 |
| abstract_inverted_index.precision | 43, 183 |
| abstract_inverted_index.processes | 174 |
| abstract_inverted_index.proposed. | 167 |
| abstract_inverted_index.receptive | 127 |
| abstract_inverted_index.vMF-based | 162 |
| abstract_inverted_index.validated | 192 |
| abstract_inverted_index.advantages | 231 |
| abstract_inverted_index.capability | 106 |
| abstract_inverted_index.classifier | 25 |
| abstract_inverted_index.imbalance. | 19 |
| abstract_inverted_index.interclass | 155 |
| abstract_inverted_index.multiscale | 112 |
| abstract_inverted_index.neglecting | 35 |
| abstract_inverted_index.perception | 121 |
| abstract_inverted_index.scenarios. | 137 |
| abstract_inverted_index.tail-class | 108 |
| abstract_inverted_index.Long-tailed | 0 |
| abstract_inverted_index.adaptation. | 242 |
| abstract_inverted_index.challenging | 136 |
| abstract_inverted_index.classifier. | 59 |
| abstract_inverted_index.components. | 94 |
| abstract_inverted_index.contrastive | 163 |
| abstract_inverted_index.coordinates | 171 |
| abstract_inverted_index.cross-space | 69, 96 |
| abstract_inverted_index.dynamically | 124 |
| abstract_inverted_index.effectively | 170 |
| abstract_inverted_index.integrating | 111 |
| abstract_inverted_index.long-tailed | 238 |
| abstract_inverted_index.maintaining | 214 |
| abstract_inverted_index.multiregion | 114 |
| abstract_inverted_index.recognition | 216, 235 |
| abstract_inverted_index.resolutions | 134 |
| abstract_inverted_index.significant | 230 |
| abstract_inverted_index.Mises-Fisher | 146 |
| abstract_inverted_index.distribution | 1, 148 |
| abstract_inverted_index.innovatively | 142 |
| abstract_inverted_index.effectiveness | 188 |
| abstract_inverted_index.significantly | 208 |
| abstract_inverted_index.NWPU-RESISC45. | 203 |
| abstract_inverted_index.classification | 10, 80, 122 |
| abstract_inverted_index.generalization | 55, 131 |
| abstract_inverted_index.representation | 97, 105 |
| abstract_inverted_index.representation. | 186 |
| abstract_inverted_index.high-dimensional | 154 |
| abstract_inverted_index.perception-driven | 72 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.27124234 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |