TM和SAR遥感图像的不同层次融合分类比较

doi:10.11873/j.issn.1004-0323.1999.3.38

閬ユ劅鎶�鏈笌搴旂敤鈥衡�� 1999, Vol. 14 鈥衡�� Issue (3): 38-43.DOI: 10.11873/j.issn.1004-0323.1999.3.38

鈥� 鎶�鏈柟娉� 鈥� 涓婁竴绡� 涓嬩竴绡�

TM鍜孲AR閬ユ劅鍥惧儚鐨勪笉鍚屽眰娆¤瀺鍚堝垎绫绘瘮杈�

浜庣鍏帮紝閽卞浗钑�

鍝堝皵婊ㄥ伐涓氬ぇ瀛﹁埅澶╃數瀛愪笌鍏夌數宸ョ▼绯� 鍝堝皵婊�150001

鍑虹増鏃ユ湡:1999-06-20 鍙戝竷鏃ユ湡:2012-09-12
鍩洪噾璧勫姪:
鍥介槻鍩洪噾

Comparison of TM and SAR Remote Sensing Image Different Level Fusion Classification Effect

YU Xiu-lan QIAN Guo-hui

(Dept. of Astronautic Electronics & Opto Electronic Engineering, Harbin Institute of Technology,Haerbin, 150001)

Online:1999-06-20 Published:2012-09-12

鎽樿/Abstract

鎽樿锛� 澶氫紶鎰熷櫒閬ユ劅鍥惧儚涓哄垎绫绘妧鏈彁渚涗簡鏇村鐨勫湴鐗╃壒寰佷俊鎭紝鏈夊姪浜庢彁楂樺垎绫荤簿搴︼紝澧炲己璁＄畻鏈鸿嚜鍔ㄨВ璇戠殑鑳藉姏锛屽噺灏戦仴鎰熷浘鍍忕殑鍚庡鐞嗘椂闂淬�傜粰鍑轰簡锛达辑鍜岋汲锛★疾閬ユ劅鍥惧儚鏍规嵁淇℃伅澶勭悊鎵�鍦ㄧ殑涓嶅悓灞傛铻嶅悎鍒嗙被鐗瑰緛鐨勯�夊彇鍙婂垎绫绘柟娉曪紝骞朵粠铻嶅悎鍒嗙被鐨勭簿搴﹀拰璁＄畻鏃堕棿娑堣�椾笂瀵瑰搱灏旀花甯傞檮杩戝湴鍖猴紩锛戯紥脳锛曪紤锛� 澶у皬鐨勶即锛拰锛讥锛诧汲锛� 鐨勶汲锛★疾鍥惧儚鐨勪笉鍚屽眰娆¤瀺鍚堢殑鍒嗙被杩涜姣旇緝锛屾寚鍑虹壒寰佸眰铻嶅悎鏄即锛拰锛筹肌锛查仴鎰熷浘鍍忚瀺鍚堝垎绫荤殑鏈�鐞嗘兂灞傛

鍏抽敭璇�: 閬ユ劅鍥惧儚, 铻嶅悎, 鍒嗙被

Abstract: Multi sensor remote sensing image fusion technique provides more information of ground objects to classification than single sensor image. And it is helpful to improve classified accuracy, enhance computer automatic interpreting capability, and reduce the remote sensing image postprocessing time. The characters selection and classified method of TM and SAR remote sensing image different level fusion are given in this paper, and the different level fusion classified results of 512脳512 TM and JERS

Key words: Remote sensing image, Fusion, Classification

涓浘鍒嗙被鍙�:

TP751

浜庣鍏帮紝閽卞浗钑�. TM鍜孲AR閬ユ劅鍥惧儚鐨勪笉鍚屽眰娆¤瀺鍚堝垎绫绘瘮杈僛J]. 閬ユ劅鎶�鏈笌搴旂敤, 1999, 14(3): 38-43.

[1]	鐜嬩功浼�,鑸掓竻鎬�,椹棴,鑲栧姴妤�,鍛ㄦ枃姝�. 婵�鍏夐浄杈句笌楂樺厜璋辨垚鍍忔妧鏈暟鎹瀺鍚堢爺绌惰繘灞�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2024, 39(1): 11-23.
[2]	鏈变繆宄�, 鍒樻竻鏃�, 宕斿笇姘�, 寮犳枃鍗�. 鍦板熀涓庢棤浜烘満LiDAR铻嶅悎鐐逛簯鐨勪汉宸ユ灄钃勭Н閲忎及绠�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2024, 39(1): 45-54.
[3]	瀹嬪噷瀵�,鍒樺皬鏉�,寮犱粨鐨�,閽熼湝闆�,鍒樺仴,浣欏潳鍕�,鐜嬪竼. 铻嶅悎绌衡�斿ぉ閬ユ劅鏁版嵁鐨勬瘺绔规灄鍙剁豢绱犲惈閲忓弽婕旀ā鍨嬪姣旂爺绌�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2024, 39(1): 67-76.
[4]	閯㈡晱,澶忔案鍗�,鐜嬪啿,瀛斿涓�,澶旦瀹�,鏉庢櫒. 鏃犱汉鏈虹偣浜戜笌鍥惧儚璺ㄦā鎬佹贩鍚堣瀺鍚堢殑涔旀湪鏋楀崟鏈ㄥ昂搴︽爲绉嶅垎绫荤爺绌�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2024, 39(1): 87-97.
[5]	钁涙ⅵ婊�,楂樼ǔ,绁濇晱,閮紵鍏�,瀹嬪穽. 鍩轰簬SE-ConvLSTM鐨勬椂绌虹壒寰佽瀺鍚圫AR鍥惧儚娴峰啺鍒嗙被[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(6): 1306-1316.
[6]	鍒樺叴鏂�,鏉庡阀鐜�,瀹嬫窇绾�. 鍩轰簬2D Triple-Collocation鍜屾満鍣ㄥ涔犳柟娉曠殑澶氭簮閬ユ劅鍦熷￥婀垮害铻嶅悎涓庨檷灏哄害鐮旂┒[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(6): 1317-1327.
[7]	閮壋,榛勮繙绋�,绔為湠. 鍩轰簬澶氬昂搴﹁瀺鍚堢殑閬ユ劅瑙嗚闂瓟[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(6): 1477-1484.
[8]	鏌闃�,鑳＄ゥ绁�,鏄庣拹鐠�,鍒樺姝�,灏圭户閼�,鍒樺畤鑸�. 闈㈠悜鍦拌〃褰㈠彉楂樼簿搴︾洃娴嬬殑GNSS-InSAR铻嶅悎鏂规硶[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(5): 1028-1041.
[9]	鏇规枌,閮戞仼璁�,娌堥挧鎴�. 鍩轰簬绁炵粡缃戠粶娉ㄦ剰鍔涙灦鏋勬悳绱㈢殑鍏夊閬ユ劅鍥惧儚鍦烘櫙鍒嗙被[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(4): 913-923.
[10]	寮犲濞�,璋竴鏁�,钁ｈ悕,瀛熷湥鍗�,鍙告捣骞�,鐜嬫檽骞�,椹柊鏄�. 鍩轰簬娣卞害瀛︿範鐨勯仴鎰熷奖鍍忔椂绌鸿瀺鍚堝強鍐皬楹︾妞嶉潰绉彁鍙�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(3): 578-587.
[11]	閲戞ⅵ濠�,寰愭潈,閮箯,闊╁疂鍗�,閲戝啗. 鍩轰簬闈㈠悜瀵硅薄澶氱壒寰佸涔犵殑鏃犱汉鏈哄奖鍍忓啘浣滅墿绮剧粏鍒嗙被鏂规硶[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(3): 588-598.
[12]	鍛ㄦ槉寮�,鍖呭垰,寰愯嚜涓�,Bayarsaikhan Sainbuyan,鍖呯帀娴�. 鍩轰簬CO₂閫氶噺鍜岄仴鎰熻瀺鍚堟暟鎹殑浜氶珮灞辫崏鐢稿叧閿墿鍊欏弬鏁版彁鍙栫爺绌�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(3): 624-639.
[13]	钁ｄ笘鑻�,鍚寸敯鍐�,鐒︽�濅匠. 鍦板潡绾ц埅绌洪珮鍏夎氨閬ユ劅鍦熷湴瑕嗙洊鍒跺浘鍙婂叾绮惧害璇勪及[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(2): 353-361.
[14]	闂槑,搴炲媷,浣曚簯鐜�,钂欒瘲鏍�,榄忓穽. 鍩轰簬GEE浜戝钩鍙板拰Sentinel鈦�2鏁版嵁鐨勬櫘娲卞競妫灄瑕嗙洊鍒跺浘[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(2): 432-442.
[15]	寮犲Ξ濞�,寮犵弬,鏉庤繍骞�,鏉庢洣,鍒樻稕. 涓浗鍗楁柟鍏稿瀷婀挎鼎灞卞尯妞嶈绫诲瀷鐨勬棤浜烘満澶氬厜璋遍仴鎰熸満鍣ㄥ涔犲垎绫荤爺绌�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(1): 163-172.

TM鍜孲AR閬ユ劅鍥惧儚鐨勪笉鍚屽眰娆¤瀺鍚堝垎绫绘瘮杈�

Comparison of TM and SAR Remote Sensing Image Different Level Fusion Classification Effect

鍙鍖�

鎽樿/Abstract

寮曠敤鏈枃

浣跨敤鏈枃

鍙傝�冩枃鐚�

鐩稿叧鏂囩珷 15

缂栬緫鎺ㄨ崘

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