RACDNet: Resolution- and Alignment-Aware Change Detection Network for Optical Remote Sensing Imagery Article Swipe
Juan Tian
,
Daifeng Peng
,
Haiyan Guan
,
Haiyong Ding
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14184527
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14184527
Change detection (CD) methods work on the basis of co-registered multi-temporal images with equivalent resolutions. Due to the limitation of sensor imaging conditions and revisit period, it is difficult to acquire the desired images, especially in emergency situations. In addition, accurate multi-temporal images co-registration is largely limited by vast object changes and matching algorithms. To this end, a resolution- and alignment-aware change detection network (RACDNet) is proposed for multi-resolution optical remote-sensing imagery CD. In the first stage, to generate high-quality bi-temporal images, a light-weighted super-resolution network is proposed by fully considering the construction difficulty of different regions, which facilitates to detailed information recovery. Adversarial loss and perceptual loss are further adopted to improve the visual quality. In the second stage, deformable convolution units are embedded in a novel Siamese–UNet architecture for bi-temporal deep features alignment; thus, robust difference features can be generated for change information extraction. We further use an atrous convolution module to enlarge the receptive field, and an attention module to bridge the semantic gap between the encoder and decoder. To verify the effectiveness of our RACDNet, a novel multi-resolution change detection dataset (MRCDD) is created by using Google Earth. The quantitative and qualitative experimental results demonstrate that our RACDNet is capable of enhancing the details of the reconstructed images significantly, and the performance of CD surpasses other state-of-the-art methods by a large margin.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs14184527
- https://www.mdpi.com/2072-4292/14/18/4527/pdf?version=1663139528
- OA Status
- gold
- Cited By
- 17
- References
- 101
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4295413873
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4295413873Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs14184527Digital Object Identifier
- Title
-
RACDNet: Resolution- and Alignment-Aware Change Detection Network for Optical Remote Sensing ImageryWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-09-10Full publication date if available
- Authors
-
Juan Tian, Daifeng Peng, Haiyan Guan, Haiyong DingList of authors in order
- Landing page
-
https://doi.org/10.3390/rs14184527Publisher landing page
- PDF URL
-
https://www.mdpi.com/2072-4292/14/18/4527/pdf?version=1663139528Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2072-4292/14/18/4527/pdf?version=1663139528Direct OA link when available
- Concepts
-
Computer science, Change detection, Artificial intelligence, Computer vision, Encoder, Remote sensing, Pattern recognition (psychology), Geology, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
17Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 7, 2024: 7, 2023: 3Per-year citation counts (last 5 years)
- References (count)
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101Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2948648905, https://openalex.org/W6752130569, https://openalex.org/W2809328251, https://openalex.org/W2958038879, https://openalex.org/W2789781087, https://openalex.org/W2157026765, https://openalex.org/W2085289201, https://openalex.org/W3027201985, https://openalex.org/W1979061792, https://openalex.org/W2132625770, https://openalex.org/W2577832105, https://openalex.org/W2144552105, https://openalex.org/W2154451793, https://openalex.org/W2804528682, https://openalex.org/W2966888491, https://openalex.org/W2789944120, https://openalex.org/W6727029182, https://openalex.org/W2099166894, https://openalex.org/W4224942241, https://openalex.org/W3042999524, https://openalex.org/W2764034829, https://openalex.org/W2412588858, https://openalex.org/W2940726923, https://openalex.org/W2767778161, https://openalex.org/W2516616494, https://openalex.org/W2751993439, https://openalex.org/W2896092083, https://openalex.org/W2896365540, https://openalex.org/W2884276099, https://openalex.org/W3010257550, https://openalex.org/W6783326637, https://openalex.org/W2908624219, https://openalex.org/W4285173877, https://openalex.org/W2986028557, https://openalex.org/W3133438312, https://openalex.org/W6803420298, https://openalex.org/W3200974967, https://openalex.org/W2951991161, https://openalex.org/W3099503507, https://openalex.org/W6789510070, https://openalex.org/W4214601273, https://openalex.org/W4285214830, https://openalex.org/W6790731625, https://openalex.org/W4285215416, https://openalex.org/W6802234969, https://openalex.org/W3005632666, https://openalex.org/W3163207600, https://openalex.org/W6803719748, https://openalex.org/W4293059536, https://openalex.org/W4285243313, https://openalex.org/W6809456617, https://openalex.org/W4226066651, https://openalex.org/W3027225766, https://openalex.org/W6797500923, https://openalex.org/W4224212608, https://openalex.org/W6804157828, https://openalex.org/W6799693294, https://openalex.org/W4224067506, https://openalex.org/W4285298122, https://openalex.org/W4226246368, https://openalex.org/W4312549298, https://openalex.org/W4312350143, https://openalex.org/W3040323135, https://openalex.org/W4284886326, https://openalex.org/W2087380704, https://openalex.org/W2121058967, https://openalex.org/W2150081556, https://openalex.org/W1885185971, https://openalex.org/W2242218935, https://openalex.org/W2963372104, https://openalex.org/W2963470893, https://openalex.org/W6791113896, https://openalex.org/W2996444841, https://openalex.org/W6803910153, https://openalex.org/W3216892772, https://openalex.org/W6806859363, https://openalex.org/W2962793481, https://openalex.org/W2601564443, https://openalex.org/W6792067159, https://openalex.org/W3036453075, https://openalex.org/W2102166818, https://openalex.org/W6806648790, https://openalex.org/W3210281071, https://openalex.org/W3135879617, https://openalex.org/W3205361185, https://openalex.org/W2808945524, https://openalex.org/W2519960185, https://openalex.org/W3124502372, https://openalex.org/W3186032668, https://openalex.org/W3130754787, https://openalex.org/W4206244656, https://openalex.org/W3099831940, https://openalex.org/W3084438775, https://openalex.org/W3212741915, https://openalex.org/W4214828290, https://openalex.org/W3207889202, https://openalex.org/W3133726918, https://openalex.org/W2782522152, https://openalex.org/W3176330035, https://openalex.org/W4206511326, https://openalex.org/W3209695792 |
| referenced_works_count | 101 |
| abstract_inverted_index.a | 57, 82, 126, 179, 223 |
| abstract_inverted_index.CD | 217 |
| abstract_inverted_index.In | 38, 73, 116 |
| abstract_inverted_index.To | 54, 172 |
| abstract_inverted_index.We | 146 |
| abstract_inverted_index.an | 149, 159 |
| abstract_inverted_index.be | 140 |
| abstract_inverted_index.by | 47, 88, 188, 222 |
| abstract_inverted_index.in | 35, 125 |
| abstract_inverted_index.is | 27, 44, 65, 86, 186, 202 |
| abstract_inverted_index.it | 26 |
| abstract_inverted_index.of | 8, 19, 94, 176, 204, 208, 216 |
| abstract_inverted_index.on | 5 |
| abstract_inverted_index.to | 16, 29, 77, 99, 111, 153, 162 |
| abstract_inverted_index.CD. | 72 |
| abstract_inverted_index.Due | 15 |
| abstract_inverted_index.The | 192 |
| abstract_inverted_index.and | 23, 51, 59, 105, 158, 170, 194, 213 |
| abstract_inverted_index.are | 108, 123 |
| abstract_inverted_index.can | 139 |
| abstract_inverted_index.for | 67, 130, 142 |
| abstract_inverted_index.gap | 166 |
| abstract_inverted_index.our | 177, 200 |
| abstract_inverted_index.the | 6, 17, 31, 74, 91, 113, 117, 155, 164, 168, 174, 206, 209, 214 |
| abstract_inverted_index.use | 148 |
| abstract_inverted_index.(CD) | 2 |
| abstract_inverted_index.deep | 132 |
| abstract_inverted_index.end, | 56 |
| abstract_inverted_index.loss | 104, 107 |
| abstract_inverted_index.that | 199 |
| abstract_inverted_index.this | 55 |
| abstract_inverted_index.vast | 48 |
| abstract_inverted_index.with | 12 |
| abstract_inverted_index.work | 4 |
| abstract_inverted_index.basis | 7 |
| abstract_inverted_index.first | 75 |
| abstract_inverted_index.fully | 89 |
| abstract_inverted_index.large | 224 |
| abstract_inverted_index.novel | 127, 180 |
| abstract_inverted_index.other | 219 |
| abstract_inverted_index.thus, | 135 |
| abstract_inverted_index.units | 122 |
| abstract_inverted_index.using | 189 |
| abstract_inverted_index.which | 97 |
| abstract_inverted_index.Change | 0 |
| abstract_inverted_index.Earth. | 191 |
| abstract_inverted_index.Google | 190 |
| abstract_inverted_index.atrous | 150 |
| abstract_inverted_index.bridge | 163 |
| abstract_inverted_index.change | 61, 143, 182 |
| abstract_inverted_index.field, | 157 |
| abstract_inverted_index.images | 11, 42, 211 |
| abstract_inverted_index.module | 152, 161 |
| abstract_inverted_index.object | 49 |
| abstract_inverted_index.robust | 136 |
| abstract_inverted_index.second | 118 |
| abstract_inverted_index.sensor | 20 |
| abstract_inverted_index.stage, | 76, 119 |
| abstract_inverted_index.verify | 173 |
| abstract_inverted_index.visual | 114 |
| abstract_inverted_index.(MRCDD) | 185 |
| abstract_inverted_index.RACDNet | 201 |
| abstract_inverted_index.acquire | 30 |
| abstract_inverted_index.adopted | 110 |
| abstract_inverted_index.between | 167 |
| abstract_inverted_index.capable | 203 |
| abstract_inverted_index.changes | 50 |
| abstract_inverted_index.created | 187 |
| abstract_inverted_index.dataset | 184 |
| abstract_inverted_index.desired | 32 |
| abstract_inverted_index.details | 207 |
| abstract_inverted_index.encoder | 169 |
| abstract_inverted_index.enlarge | 154 |
| abstract_inverted_index.further | 109, 147 |
| abstract_inverted_index.imagery | 71 |
| abstract_inverted_index.images, | 33, 81 |
| abstract_inverted_index.imaging | 21 |
| abstract_inverted_index.improve | 112 |
| abstract_inverted_index.largely | 45 |
| abstract_inverted_index.limited | 46 |
| abstract_inverted_index.margin. | 225 |
| abstract_inverted_index.methods | 3, 221 |
| abstract_inverted_index.network | 63, 85 |
| abstract_inverted_index.optical | 69 |
| abstract_inverted_index.period, | 25 |
| abstract_inverted_index.results | 197 |
| abstract_inverted_index.revisit | 24 |
| abstract_inverted_index.RACDNet, | 178 |
| abstract_inverted_index.accurate | 40 |
| abstract_inverted_index.decoder. | 171 |
| abstract_inverted_index.detailed | 100 |
| abstract_inverted_index.embedded | 124 |
| abstract_inverted_index.features | 133, 138 |
| abstract_inverted_index.generate | 78 |
| abstract_inverted_index.matching | 52 |
| abstract_inverted_index.proposed | 66, 87 |
| abstract_inverted_index.quality. | 115 |
| abstract_inverted_index.regions, | 96 |
| abstract_inverted_index.semantic | 165 |
| abstract_inverted_index.(RACDNet) | 64 |
| abstract_inverted_index.addition, | 39 |
| abstract_inverted_index.attention | 160 |
| abstract_inverted_index.detection | 1, 62, 183 |
| abstract_inverted_index.different | 95 |
| abstract_inverted_index.difficult | 28 |
| abstract_inverted_index.emergency | 36 |
| abstract_inverted_index.enhancing | 205 |
| abstract_inverted_index.generated | 141 |
| abstract_inverted_index.receptive | 156 |
| abstract_inverted_index.recovery. | 102 |
| abstract_inverted_index.surpasses | 218 |
| abstract_inverted_index.alignment; | 134 |
| abstract_inverted_index.conditions | 22 |
| abstract_inverted_index.deformable | 120 |
| abstract_inverted_index.difference | 137 |
| abstract_inverted_index.difficulty | 93 |
| abstract_inverted_index.equivalent | 13 |
| abstract_inverted_index.especially | 34 |
| abstract_inverted_index.limitation | 18 |
| abstract_inverted_index.perceptual | 106 |
| abstract_inverted_index.Adversarial | 103 |
| abstract_inverted_index.algorithms. | 53 |
| abstract_inverted_index.bi-temporal | 80, 131 |
| abstract_inverted_index.considering | 90 |
| abstract_inverted_index.convolution | 121, 151 |
| abstract_inverted_index.demonstrate | 198 |
| abstract_inverted_index.extraction. | 145 |
| abstract_inverted_index.facilitates | 98 |
| abstract_inverted_index.information | 101, 144 |
| abstract_inverted_index.performance | 215 |
| abstract_inverted_index.qualitative | 195 |
| abstract_inverted_index.resolution- | 58 |
| abstract_inverted_index.situations. | 37 |
| abstract_inverted_index.architecture | 129 |
| abstract_inverted_index.construction | 92 |
| abstract_inverted_index.experimental | 196 |
| abstract_inverted_index.high-quality | 79 |
| abstract_inverted_index.quantitative | 193 |
| abstract_inverted_index.resolutions. | 14 |
| abstract_inverted_index.co-registered | 9 |
| abstract_inverted_index.effectiveness | 175 |
| abstract_inverted_index.reconstructed | 210 |
| abstract_inverted_index.Siamese–UNet | 128 |
| abstract_inverted_index.light-weighted | 83 |
| abstract_inverted_index.multi-temporal | 10, 41 |
| abstract_inverted_index.remote-sensing | 70 |
| abstract_inverted_index.significantly, | 212 |
| abstract_inverted_index.alignment-aware | 60 |
| abstract_inverted_index.co-registration | 43 |
| abstract_inverted_index.multi-resolution | 68, 181 |
| abstract_inverted_index.state-of-the-art | 220 |
| abstract_inverted_index.super-resolution | 84 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 96 |
| corresponding_author_ids | https://openalex.org/A5071358222 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I200845125 |
| citation_normalized_percentile.value | 0.87785082 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |