The　spectral　information　contained　in　the　hyperspectral　images　(HSI)　distinguishes　the　intrinsic　properties　of　a　target　from　the　background,　which　is　widely　used　in　remote　sensing.　However,　the　low　imaging　speed　and　high　data　redundancy　caused　by　the　high　spectral　resolution　of　imaging　spectrometers　limit　their　application　in　scenarios　with　the　real-time　requirement.　In　this　work,　we　achieve　the　precise　detection　of　camouflaged　targets　based　on　snapshot　multispectral　imaging　technology　and　band　selection　methods　in　urban-related　scenes.　Specifically,　the　camouflaged　target　detection　algorithm　combines　the　constrained　energy　minimization　(CEM)　algorithm　and　the　improved　maximum　between-class　variance　(OTSU)　algorithm　(t-OTSU),　which　is　proposed　to　obtain　the　initial　target　detection　results　and　adaptively　segment　the　target　region.　Moreover,　an　object　region　extraction　(ORE)　algorithm　is　proposed　to　obtain　a　complete　target　contour　that　improves　the　target　detection　capability　of　multispectral　images　(MSI).　The　experimental　results　show　that　the　proposed　algorithm　has　the　ability　to　detect　different　camouflaged　targets　by　using　only　four　bands.　The　detection　accuracy　is　above　99%,　and　the　false　alarm　rate　is　below　0.2%.　The　research　achieves　the　effective　detection　of　camouflaged　targets　and　has　the　potential　to　provide　a　new　means　for　real-time　multispectral　sensing　in　complex　scenes.