面向对象的地物分类法分析与评

doi:10.11873/j.issn.1004-0323.2004.1.20

閬ユ劅鎶�鏈笌搴旂敤鈥衡�� 2004, Vol. 19 鈥衡�� Issue (1): 20-23.DOI: 10.11873/j.issn.1004-0323.2004.1.20

鈥� 鎶�鏈柟娉� 鈥� 涓婁竴绡� 涓嬩竴绡�

闈㈠悜瀵硅薄鐨勫湴鐗╁垎绫绘硶鍒嗘瀽涓庤瘎

鏉滃嚖鍏�,鐢板簡涔�,澶忓榻�,鎯犲嚖楦�

(鍗椾含澶у鍥介檯鍦扮悆绯荤粺绉戝鐮旂┒鎵�,姹熻嫃鍗椾含銆�210093)

鏀剁鏃ユ湡:2003-08-09 淇洖鏃ユ湡:2003-11-06 鍑虹増鏃ユ湡:2004-02-15 鍙戝竷鏃ユ湡:2011-12-26
浣滆�呯畝浠�:鏉滃嚖鍏�(1980-),濂�,纭曞＋鐢�,涓昏浠庝簨楂樺厜璋卞拰楂樼┖闂村垎杈ㄧ巼閬ユ劅鐮旂┒銆�

Object-oriented Image Classification Analysis and Evaluation

DU Feng-lan, TIAN Qing-jiu, XIA Xue-qi, HUI Feng-ming

(International Institute For Earth System Science,Nanjing University,Nanjing210093,China)

Received:2003-08-09 Revised:2003-11-06 Online:2004-02-15 Published:2011-12-26

鎽樿/Abstract

鎽樿锛�

鎽樿:缁撳悎鍗椾含甯傚尯IKONOS楂樺垎杈ㄧ巼閬ユ劅鏁版嵁,鐮旂┒鍒╃敤闈㈠悜瀵硅薄鍒嗙被娉曞疄鐜板湴鐗╃簿纭垎绫荤殑鏂规硶鍜屾妧宸�,璇勪及鍒嗙被缁撴灉鐨勭簿搴�,鎺㈣闈㈠悜瀵硅薄鍒嗙被娉曞湪鍦扮墿鍒嗙被搴旂敤涓殑娼滃姏鍜岄檺鍒�,涓洪珮鍒嗚鲸鐜囬仴鎰熷湪鍦扮墿鍒嗙被涓殑鏈夋晥搴旂敤鎻愪緵鏂扮殑鎶�鏈�斿緞銆�

鍏抽敭璇�: 楂樺垎杈ㄧ巼閬ユ劅, 闈㈠悜瀵硅薄, 鍒嗙被, IKONOS, eCognition

Abstract:

High-resolution remote sensing data have a lot of characteristics compared to the other remotesensing data. Object-oriented image classification can make the most of their advantages. Using thismethod, we classify IKONOS image data of Nanjing to realize accurate classification, evaluate theprecision of the classification results, and discuss the potential and restriction of this method in imageclassification so that providing new methods in the high-resolution remote sensing data classification.

Key words: High-resolution remote sensing, Object-oriented, Classification, IKONOS, e-Cognition

涓浘鍒嗙被鍙�:

TP 75

鏉滃嚖鍏�,鐢板簡涔�,澶忓榻�,鎯犲嚖楦�. 闈㈠悜瀵硅薄鐨勫湴鐗╁垎绫绘硶鍒嗘瀽涓庤瘎[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2004, 19(1): 20-23.

DU Feng-lan, TIAN Qing-jiu, XIA Xue-qi, HUI Feng-ming. Object-oriented Image Classification Analysis and Evaluation[J]. Remote Sensing Technology and Application, 2004, 19(1): 20-23.

鍙傝�冩枃鐚�

銆�1銆曘��鏂囨矁鏍�.楂樺垎杈ㄧ巼IKONOS鍗槦褰卞儚鍙婂叾浜у搧鐨勭壒鎬с�擩銆�.閬ユ劅淇℃伅,2001,1:37锝�38.銆�2銆曘��绔犲瓭鐏�,榛勬櫤鎵�,璧靛厓娲�.閬ユ劅鏁板瓧鍥惧儚澶勭悊銆擬銆�.娴欐睙:娴欐睙澶у鍑虹増绀�,1997.銆�3銆曘��Baatz M, Schape A. Object-Oriented and Multi-Scale ImageAnalysis in Semantic Networks銆擜銆�. In: Proc. of the 2ndInternational Symposium on Operationalization of RemoteSensing銆擟銆�. August 16锝�20 th 1999. Enschede. ITC.銆�4銆曘��鐒﹀瓙閿�,鏉庡皬鏂�,鐜嬮敠鍦�,绛�.涓�绉嶅熀浜庡垎绫荤殑铻嶅悎绠楁硶銆擩銆�.涓浗鍥捐薄鍥惧舰瀛︽姤,2002,7(8):771锝�776.銆�5銆曘��eCognition 3 Made in Germany銆擹銆�.銆�6銆曘��Gorte B. Probabilistic Segmentation of Remotely Sensed Images銆擜銆�. In: ITC Publication Series銆擟銆�.1998.銆�7銆曘��Laine A, Fan J. Frame Representations for Texture Segmentation銆擩銆�. IEEE Transactions on Image Processing,1996,5(5):771锝�779.銆�8銆曘��Pierce E, Ulaby F T, Sarabandi K,et al. Knowledge-BasedClassification of Polarimetric SAR Images銆擩銆�. IEEETransactions on Geosciences and Remote Sensing, 1994,32(5): 1081锝�1086.銆�9銆曘��Hans Bandemer, WithSiegfried Gottwald. Fuzzy Sets, FuzzyLogic, Fuzzy Methods with Applications銆擬銆�. New York:Wiley Press New York, 1995.銆�10銆曘��璧垫尟瀹�,寰愮敤鎳�.妯＄硦鐞嗚鍜岀缁忕綉缁滅殑鍩虹涓庡簲鐢ㄣ�擬銆�.鍖椾含:娓呭崕澶у鍑虹増绀�,1996.銆�11銆曘��HelneCaillol. Fuzzy Random Field Sand Unsupervised ImageSegmentation銆擩銆�. IEEE Transactions on Geosciences andRemote Sensing, 1993,31(4): 801锝�810.銆�12銆曘��Congalton R G. A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data銆擩銆�. Rem. Sens. Environ. 1991,37:35锝�46.銆�13銆曘��Rosen field G H, Fitzpatrick-Lines K. A Coefficient of Agreement as a Measure of Thematic Classification Accuracy銆擩銆�.Phonogram. Eng. Remote Sensing, 1986,52:223锝�227.

[1]	鍗㈠織鍒�,闄堣姵娣�,琚佽秴,鐢颁害闄�,闄堝己,鏂囩編骞�,灏归敶,鏉ㄥ厜. 閲囩敤I-PSPNet璇箟鍒嗗壊妯″瀷鐨勯珮鍒嗚鲸鐜囬仴鎰熷奖鍍忔煇鐗圭妞嶇墿绉嶆鍦板潡鎻愬彇鐮旂┒[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2024, 39(1): 222-233.
[2]	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.
[3]	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.
[4]	寮犳烦,钁涙瘏,浠昏秺,楂橀,闊╁媷,钁ｆ�濇簮,绉︽澃鑻�,璁歌讲,鍚曞┃,楂樿壋鑺�. 鍩轰簬浼樺寲鐨凞eeplabV3+缃戠粶鍜岄珮鍒嗗奖鍍忓垎鍓叉诞钀嶅瀷鍐滄潙榛戣嚟姘翠綋[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(6): 1433-1444.
[5]	鑳¤吘浜�,瑙ｉ箯椋�,娓╀簹妤�,鎱曞彿浼�. 鍩轰簬涓嶅悓娣卞害瀛︿範妯″瀷鎻愬彇寤虹瓚杞粨鐨勬柟娉曠爺绌�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(4): 892-902.
[6]	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.
[7]	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.
[8]	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.
[9]	鑼冩矝钖�,閮濊挋钂�,姹熶笢,涓佹柟瀹�. 鍩轰簬楂樺垎閬ユ劅褰卞儚鐨勯湶澶╃熆鍖哄湡鍦板埄鐢ㄥ姩鎬佺洃娴嬧�斺�斾互骞虫湐鐭垮尯涓轰緥[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2023, 38(2): 274-284.
[10]	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.
[11]	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.
[12]	鐜嬫櫠,楂樺竻,閮寒,姹簯. 鍩轰簬澶氬昂搴︾壒寰佽瀺鍚堢殑U-Net缃戠粶楂樺垎褰卞儚涓嶉�忔按闈㈡彁鍙栫爺绌�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2022, 37(4): 811-819.
[13]	璧靛竻,鏇圭編鑺�,钂嬪厛铦�,闄堣��浜�,闄嗙伅鐩�. 瀹夊窘鐪佸埄杈涘幙骞冲師鍖轰汉宸ユ灄鏍戠鍒嗙被鐮旂┒[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2022, 37(3): 589-598.
[14]	鏈卞帤鏂�,缃楀啿,瀹樻捣缈�,寮犳柊涔�,鏉ㄥ槈閼�,瀹嬫ⅵ瀹�,鍒樼剷鍐�. 鍩轰簬闈㈠悜瀵硅薄鐨勫婧愬崼鏄熼仴鎰熷奖鍍忕帀绫冲�掍紡闈㈢Н鎻愬彇[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2022, 37(3): 599-607.
[15]	鍙蹭匠鐫�,鐢宠寽,褰孩鏄�,鏉庡埄浼�,濮氭湀,姹槑绉�,鐜嬭尮. 鍩轰簬涓夌楂樺垎杈ㄧ巼褰卞儚鏍锋湰杩佺Щ瀛︿範鐨勫煄甯傛按浣撴彁鍙栫爺绌�[J]. 閬ユ劅鎶�鏈笌搴旂敤, 2022, 37(3): 731-738.

闈㈠悜瀵硅薄鐨勫湴鐗╁垎绫绘硶鍒嗘瀽涓庤瘎

Object-oriented Image Classification Analysis and Evaluation

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

Metrics

鏈枃璇勪环