Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟯𝟳𝟬 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 A 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿-𝗕𝗮𝘀𝗲𝗱 𝗦𝗶𝗮𝗺𝗲𝘀𝗲 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗳𝗼𝗿 𝗖𝗵𝗮𝗻𝗴𝗲 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 𝗯𝘆 𝗝𝗼𝗵𝗻𝘀 𝗛𝗼𝗽𝗸𝗶𝗻𝘀 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗶𝘁𝘆 Follow me for a similar post: Ashish Patel  ------------------------------------------------------------------- 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 : 🔸 Paper: 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿-𝗕𝗮𝘀𝗲𝗱 𝗦𝗶𝗮𝗺𝗲𝘀𝗲 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗳𝗼𝗿 𝗖𝗵𝗮𝗻𝗴𝗲 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 🔸 This paper is published IGARSS2022. 🔸 Change Detection (CD) aims to detect relevant changes from a pair of co-registered images acquired at distinct times. The definition of change may usually vary depending on the ap- plication. The changes in man-made facilities (e.g., build- ings, vehicles, etc.), vegetation changes, and environmental changes (e.g., polar ice cap melting, deforestation, damages caused by disasters) are usually regarded as relevant changes. ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 This paper presents a transformer-based Siamese network architecture (abbreviated by ChangeFormer) for Change Detection (CD) from a pair of co-registered remote sensing images. 🔸Different from recent CD frameworks, which are based on fully convolutional networks (ConvNets), the proposed method unifies hierarchically structured transformer encoder with Multi-Layer Perception (MLP) decoder in a Siamese network architecture to efficiently render multi-scale long-range details required for accurate CD. 🔸Experiments on two CD datasets show that the proposed end-to-end trainable ChangeFormer architecture achieves better CD performance than previous counterparts. #computervision #artificialintelligence #innovation